Analysis of Natural movement of Romanian Population during 2007-2014 - III

Acta Universitatis Danubius. Œconomica, Vol 13, No 6 (2017)

Analysis of Natural Movement of Romanian Population During 2007-2014 - III

Cătălin Angelo Ioan

Abstract: Article shall carry out the analysis of natural movement of Romanian population During 2007-2014. They are thus treated indicators: Live births, Deceased, Natural increase, Marriages, Divorces and Deaths under 1 year. In addition to the regression analysis, are determined the median, quartiles, the arithmetic mean and standard deviation for each indicator. Also the analysis examines dependence aforementioned indicators of regional GDP variation.

Keywords: live births; deceased; natural increase; marriages; divorces

JEL Classification: Q56

1. Introduction

In what follows we shall carry out the analysis of natural movement of Romanian population During 2007-2014. They are thus treated indicators: Live births, Deceased, Natural increase, Marriages, Divorces and Deaths under 1 year. In addition to the regression analysis, are determined the median, quartiles, the arithmetic mean and standard deviation for each indicator. Also the analysis examines dependence aforementioned indicators of regional GDP variation.

In this third part, we shall analize the following counties: Hunedoara, Ialomita, Iasi, Ilfov, Maramures, Mehedinti, Mures, Neamt, Olt, Prahova and Salaj.

2. Analysis of Natural Movement of Romanian Population during 2007-2014

2.23. Analysis of Natural Movement of Hunedoara County Population

Statistics of natural movement corresponding to Hunedoara County are the following:

From figure 243 we can see a sinusoidal evolution of the indicator. #VALUE!

Regression analysis relative to indicator “Live births” gives us an equation: y=-0.867329083x+341.4508772 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=0.034820944x+469.2070175 where x is the number of month (Jan, 2007=1), therefore an upward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=-0.902150027x+-127.7561404 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

For the set of values above, the median indicator for “Live births” is 299, for “Deceased” is 470 and for “Natural increase”: -176. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (228,268.5,298.5,334,400), for “Deceased”: (362,439,469.5,498,574) and for “Natural increase”: (-312,-217.25,-176,-131.25,-42).

The arithmetic mean and the standard deviation for “Live births” are: (299,39.88), for “Deceased”: (471,41.53) and for “Natural increase”: (-172,62.33). This means that with a probability greather than 0.68 “Live births” are in the range [259,339], for “Deceased” in [429,513] and for “Natural increase” in [-234,-110].

Percentiles length indicators analysis (Figure 244) show that, indeed the concentration is around the middle of the data.

Taking into account the population dynamics during the analyzed period we have the following evolution of the indicators: Live births/10000 inh., Deceased/10000 inh. and Natural increase/10000 inh. as in the figure 245.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.01403398x+6.773460526 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=0.006449335x+9.283561404 where x is the number of month (Jan, 2007=1), therefore an upward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.020450488x+-2.511484649 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 6, for “Deceased/10000 inh.” is 10 and for “Natural increase/10000 inh.”: -4. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (4.73,5.4975,6.06,6.67,7.96), for “Deceased/10000 inh.”: (7.39,8.8925,9.65,10.16,11.82) and for “Natural increase/10000 inh.”: (-6.42,-4.4475,-3.555,-2.6225,-0.84).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (6,0.75), for “Deceased/10000 inh.”: (10,0.87) and for “Natural increase/10000 inh.”: (-4,1.3). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [5,7], for “Deceased/10000 inh.” in [9,11] and for “Natural increase/10000 inh.” in [-5,-3].

Percentiles length indicators analysis (Figure 246) show that, indeed the concentration is around the middle of the data.

A comparison of the indicator “Live births” with the national level shows that it is worse than the national, being better only in 1.04% cases. For “Deceased” the indicator is worse than the national, being better only in 28.13% cases. Finally, for “Natural increase”, the indicator is worse than the national, being better only in 2.08% cases.

Regression analysis relative to indicator “Marriages” gives us an equation: y=-0.998955507x+261.8660088 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Divorces” gives us an equation: y=-0.591766142x+116.1381579 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

For the set of values above, the median indicator for “Marriages” is 197 and for “Divorces” is 93. Also, the distribution of quartiles is for “Marriages”: (57,102,197,308,517) and for “Divorces”: (27,63,93,108.25,151). The arithmetic mean and the standard deviation for “Marriages” are: (213,119.69) and for “Divorces”: (87,28.97). This means that with a probability greather than 0.68 “Marriages” are in the range [93,333] and for “Divorces” in [58,116].

Percentiles length indicators analysis (Figure 248) show that, indeed the concentration is around the middle of the data.

Taking into account the population dynamics during the analyzed period we have the following evolution of the indicators: Marriages/10000 inh. and Divorces/10000 inh. as in the figure 249.

Regression analysis relative to indicator “Marriages/10000 inh.” gives us an equation: y=-0.017586747x+5.191811404 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Divorces/10000 inh.” gives us an equation: y=-0.011066264x+2.312859649 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Marriages/10000 inh.” is 4 and for “Divorces/10000 inh.” is 2. Also, the distribution of quartiles is for “Marriages/10000 inh.”: (1.18,2.13,4.035,6.2225,10.35) and for “Divorces/10000 inh.”: (0.57,1.3075,1.895,2.1925,3.08). The arithmetic mean and the standard deviation for “Marriages/10000 inh.” are: (4,2.42) and for “Divorces/10000 inh.”: (2,0.58). This means that with a probability greather than 0.68 “Marriages/10000 inh.” are in the range [2,6] and for “Divorces/10000 inh.” in [1,3].

Percentiles length indicators analysis (Figure 250) show that, indeed the concentration is around the middle of the data.

Figure 250

A comparison of the indicator “Marriages” with the national level shows that it is worse than the national, being better only in 32.29% cases. For “Divorces” the indicator is worse than the national, being better only in 7.29% cases.

Regression analysis relative to indicator “Deaths under 1 year” gives us an equation: y=-0.022816061x+3.981578947 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year” is 3 and the distribution of quartiles is for “Deaths under 1 year”: (0,2,3,4,7). The arithmetic mean and the standard deviation for “Deaths under 1 year” are: (3,1.7) which means that with a probability greather than 0.68 “Deaths under 1 year” are in the range [1,5].

Percentiles length indicators analysis (Figure 252) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Deaths under 1 year/100000 inh.” gives us an equation: y=-0.004257868x+0.790673246 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year/100000 inh.” is 1 and the distribution of quartiles is for “Deaths under 1 year/100000 inh.”: (0,0.4,0.6,0.8125,1.41). The arithmetic mean and the standard deviation for “Deaths under 1 year/100000 inh.” are: (1,0.34) which means that with a probability greather than 0.68 “Deaths under 1 year/100000 inh.” are in the range [1,1].

A comparison of the indicator “Deaths under 1 year” with the national level shows that it is better than the national, being better in 67.71% cases.

A final analysis examines dependence aforementioned indicators of regional GDP variation.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is a dependence of Live births from GDP in the current year and the regression equation is: 0.5878dGDP+-1.0181we find that there is a dependence of Live births from GDP offset by 2 years and the regression equation is:0.7415dGDP+-1.4156. Searching dependence annual variations of “Deceased” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Natural increase” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Marriages” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Divorces” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is not a dependence of the variation of GDP.

2.24. Analysis of Natural Movement of Ialomita County Population

Statistics of natural movement corresponding to Ialomita County are the following:

From figure 254 we can see a sinusoidal evolution of the indicator. Except months aug 2007, sept 2007, iun 2008, iul 2008, sept 2008, iun 2009, iul 2009, aug 2009, sept 2009, oct 2009, iun 2010, iul 2010, sept 2010, sept 2011, aug 2012, sept 2012, oct 2012, aug 2013, sept 2013, aug 2014, sept 2014 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=-0.713917526x+292.5625 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=0.044546934x+314.9019737 where x is the number of month (Jan, 2007=1), therefore an upward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=-0.75846446x+-22.33947368 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

For the set of values above, the median indicator for “Live births” is 260, for “Deceased” is 313 and for “Natural increase”: -69. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (176,229,259.5,288.25,378), for “Deceased”: (232,291,312.5,349.5,396) and for “Natural increase”: (-202,-112.5,-69,-7.5,106).

The arithmetic mean and the standard deviation for “Live births” are: (258,41.86), for “Deceased”: (317,38.54) and for “Natural increase”: (-59,67.9). This means that with a probability greather than 0.68 “Live births” are in the range [216,300], for “Deceased” in [278,356] and for “Natural increase” in [-127,9].

Percentiles length indicators analysis (Figure 255) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.020772179x+9.532554825 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=0.004984807x+10.24657018 where x is the number of month (Jan, 2007=1), therefore an upward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.025743489x+-0.714357456 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 9, for “Deceased/10000 inh.” is 10 and for “Natural increase/10000 inh.”: -2. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (5.85,7.57,8.54,9.4525,12.42), for “Deceased/10000 inh.”: (7.8,9.635,10.36,11.5675,13.16) and for “Natural increase/10000 inh.”: (-6.75,-3.705,-2.265,-0.245,3.47).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (9,1.35), for “Deceased/10000 inh.”: (10,1.29) and for “Natural increase/10000 inh.”: (-2,2.26). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [8,10], for “Deceased/10000 inh.” in [9,11] and for “Natural increase/10000 inh.” in [-4,0].

Percentiles length indicators analysis (Figure 257) show that, indeed the concentration is around the middle of the data.

A comparison of the indicator “Live births” with the national level shows that it is better than the national, being better in 91.67% cases. For “Deceased” the indicator is worse than the national, being better only in 3.13% cases. Finally, for “Natural increase”, the indicator is about the same with the national, being better in 41.67% cases.

Regression analysis relative to indicator “Marriages” gives us an equation: y=-1.362757732x+194.75 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Divorces” gives us an equation: y=-0.17289745x+40.11469298 where x is the number of month (Jan, 2007=1), therefore a downward trend. For the set of values above, the median indicator for “Marriages” is 114 and for “Divorces” is 30. Also, the distribution of quartiles is for “Marriages”: (21,56,114,176,434) and for “Divorces”: (6,20,29.5,43.25,78). The arithmetic mean and the standard deviation for “Marriages” are: (129,81.88) and for “Divorces”: (32,14.67). This means that with a probability greather than 0.68 “Marriages” are in the range [47,211] and for “Divorces” in [17,47].

Percentiles length indicators analysis (Figure 259) show that, indeed the concentration is around the middle of the data.

Taking into account the population dynamics during the analyzed period we have the following evolution of the indicators: Marriages/10000 inh. and Divorces/10000 inh. as in the figure 260.

Regression analysis relative to indicator “Marriages/10000 inh.” gives us an equation: y=-0.043437398x+6.350984649 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Divorces/10000 inh.” gives us an equation: y=-0.005389921x+1.309640351 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Marriages/10000 inh.” is 4 and for “Divorces/10000 inh.” is 1. Also, the distribution of quartiles is for “Marriages/10000 inh.”: (0.7,1.8625,3.79,5.89,14.18) and for “Divorces/10000 inh.”: (0.2,0.67,0.975,1.4275,2.58). The arithmetic mean and the standard deviation for “Marriages/10000 inh.” are: (4,2.68) and for “Divorces/10000 inh.”: (1,0.48). This means that with a probability greather than 0.68 “Marriages/10000 inh.” are in the range [1,7] and for “Divorces/10000 inh.” in [1,1].

Percentiles length indicators analysis (Figure 261) show that, indeed the concentration is around the middle of the data.

A comparison of the indicator “Marriages” with the national level shows that it is worse than the national, being better only in 28.13% cases. For “Divorces” the indicator is better than the national, being better in 61.46% cases.

Regression analysis relative to indicator “Deaths under 1 year” gives us an equation: y=-0.01359197x+3.49254386 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year” is 3 and the distribution of quartiles is for “Deaths under 1 year”: (0,2,3,4,8). The arithmetic mean and the standard deviation for “Deaths under 1 year” are: (3,1.77) which means that with a probability greather than 0.68 “Deaths under 1 year” are in the range [1,5].

Percentiles length indicators analysis (Figure 263) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Deaths under 1 year/100000 inh.” gives us an equation: y=-0.004211883x+1.140317982 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year/100000 inh.” is 1 and the distribution of quartiles is for “Deaths under 1 year/100000 inh.”: (0,0.66,0.98,1.31,2.63). The arithmetic mean and the standard deviation for “Deaths under 1 year/100000 inh.” are: (1,0.58) which means that with a probability greather than 0.68 “Deaths under 1 year/100000 inh.” are in the range [0,2].

A comparison of the indicator “Deaths under 1 year” with the national level shows that it is about the same with the national, being better in 40.63% cases.

A final analysis examines dependence aforementioned indicators of regional GDP variation.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deceased” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Natural increase” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Marriages” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Divorces” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is a dependence of Deaths under 1 year from GDP offset by 2 years and the regression equation is:-1.1925dGDP+-3.1494.

2.25. Analysis of Natural Movement of Iasi County Population

Statistics of natural movement corresponding to Iasi County are the following:

From figure 265 we can see a sinusoidal evolution of the indicator. Except months an 2007, feb 2007, mar 2007, apr 2007, mai 2007, iun 2007, iul 2007, aug 2007, sept 2007, oct 2007, nov 2007, dec 2007, ian 2008, feb 2008, mar 2008, apr 2008, mai 2008, iun 2008, iul 2008, aug 2008, sept 2008, oct 2008, nov 2008, ian 2009, feb 2009, mar 2009, mai 2009, iun 2009, iul 2009, aug 2009, sept 2009, oct 2009, feb 2010, mai 2010, iun 2010, iul 2010, aug 2010, sept 2010, oct 2010, nov 2010, mai 2011, iun 2011, iul 2011, aug 2011, sept 2011, oct 2011, nov 2011, mai 2012, iun 2012, iul 2012, aug 2012, sept 2012, oct 2012, nov 2012, ian 2013, iun 2013, iul 2013, aug 2013, sept 2013, oct 2013, nov 2013, ian 2014, feb 2014, mai 2014, iun 2014, iul 2014, aug 2014, sept 2014, oct 2014, nov 2014 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=-1.357297884x+864.0372807 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend. Regression analysis relative to indicator “Deceased” gives us an equation: y=0.3375x+679.1625 where x is the number of month (Jan, 2007=1), therefore a pronounced upward trend. Regression analysis relative to indicator “Natural increase” gives us an equation: y=-1.694797884x+184.8747807 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend. For the set of values above, the median indicator for “Live births” is 799, for “Deceased” is 704 and for “Natural increase”: 96. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (553,734.5,799,872.5,1110), for “Deceased”: (533,651,703.5,740.75,898) and for “Natural increase”: (-261,-17.25,96,231.75,359). The arithmetic mean and the standard deviation for “Live births” are: (798,110.83), for “Deceased”: (696,74.36) and for “Natural increase”: (103,149.99). This means that with a probability greather than 0.68 “Live births” are in the range [687,909], for “Deceased” in [622,770] and for “Natural increase” in [-47,253].

Percentiles length indicators analysis (Figure 266) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.019725312x+10.16376096 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=0.000446215x+7.996587719 where x is the number of month (Jan, 2007=1), therefore a very small upward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.020154029x+2.166324561 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 9, for “Deceased/10000 inh.” is 8 and for “Natural increase/10000 inh.”: 1. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (6.3,8.3125,9.295,10.125,12.94), for “Deceased/10000 inh.”: (6.14,7.4775,8.095,8.5325,10.34) and for “Natural increase/10000 inh.”: (-3.01,-0.195,1.12,2.685,4.19).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (9,1.31), for “Deceased/10000 inh.”: (8,0.85) and for “Natural increase/10000 inh.”: (1,1.73). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [8,10], for “Deceased/10000 inh.” in [7,9] and for “Natural increase/10000 inh.” in [-1,3].

Percentiles length indicators analysis (Figure 268) show that, indeed the concentration is around the middle of the data.

A comparison of the indicator “Live births” with the national level shows that it is better than the national, being better in 98.96% cases. For “Deceased” the indicator is better than the national, being better in 98.96% cases. Finally, for “Natural increase”, the indicator is better than the national, being better in 100% cases.

Regression analysis relative to indicator “Marriages” gives us an equation: y=-1.666793272x+516.610307 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Divorces” gives us an equation: y=0.349742268x+55.99583333 where x is the number of month (Jan, 2007=1), therefore a pronounced upward trend.

For the set of values above, the median indicator for “Marriages” is 375 and for “Divorces” is 77. Also, the distribution of quartiles is for “Marriages”: (78,206.75,374.5,586.5,1313) and for “Divorces”: (-8,55.75,77,92,124). The arithmetic mean and the standard deviation for “Marriages” are: (436,289.9) and for “Divorces”: (73,25.51). This means that with a probability greather than 0.68 “Marriages” are in the range [146,726] and for “Divorces” in [47,99].

Percentiles length indicators analysis (Figure 270) show that, indeed the concentration is around the middle of the data.

Taking into account the population dynamics during the analyzed period we have the following evolution of the indicators: Marriages/10000 inh. and Divorces/10000 inh. as in the figure 271.

Regression analysis relative to indicator “Marriages/10000 inh.” gives us an equation: y=-0.021579083x+6.075960526 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.Regression analysis relative to indicator “Divorces/10000 inh.” gives us an equation: y=0.003691129x+0.660563596 where x is the number of month (Jan, 2007=1), therefore an upward trend.

For the set of values above, the median indicator for “Marriages/10000 inh.” is 4 and for “Divorces/10000 inh.” is 1. Also, the distribution of quartiles is for “Marriages/10000 inh.”: (0.9,2.35,4.36,6.8075,15.31) and for “Divorces/10000 inh.”: (-0.09,0.6475,0.89,1.07,1.41). The arithmetic mean and the standard deviation for “Marriages/10000 inh.” are: (5,3.35) and for “Divorces/10000 inh.”: (1,0.29). This means that with a probability greather than 0.68 “Marriages/10000 inh.” are in the range [2,8] and for “Divorces/10000 inh.” in [1,1]. Percentiles length indicators analysis (Figure 272) show that, indeed the concentration is around the middle of the data.

Figure 272

A comparison of the indicator “Marriages” with the national level shows that it is about the same with the national, being better in 55.21% cases. For “Divorces” the indicator is better than the national, being better in 83.33% cases.

Regression analysis relative to indicator “Deaths under 1 year” gives us an equation: y=-0.064785676x+11.0379386 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year” is 7 and the distribution of quartiles is for “Deaths under 1 year”: (2,5,7,10,16). The arithmetic mean and the standard deviation for “Deaths under 1 year” are: (8,3.29) which means that with a probability greather than 0.68 “Deaths under 1 year” are in the range [5,11].

Percentiles length indicators analysis (Figure 274) show that, indeed the concentration is around the middle of the data.

Figure 274

Regression analysis relative to indicator “Deaths under 1 year/100000 inh.” gives us an equation: y=-0.007889582x+1.295561404 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year/100000 inh.” is 1 and the distribution of quartiles is for “Deaths under 1 year/100000 inh.”: (0.23,0.58,0.81,1.16,1.87). The arithmetic mean and the standard deviation for “Deaths under 1 year/100000 inh.” are: (1,0.39) which means that with a probability greather than 0.68 “Deaths under 1 year/100000 inh.” are in the range [1,1].

A comparison of the indicator “Deaths under 1 year” with the national level shows that it is worse than the national, being better only in 35.42% cases.

A final analysis examines dependence aforementioned indicators of regional GDP variation.

Table 150. The evolution of Iasi County GDP during 2007-2014

Source: INSSE and own calculations

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

2.26. Analysis of Natural Movement of Ilfov County Population

Statistics of natural movement corresponding to Ilfov County are the following:

From figure 276 we can see a sinusoidal evolution of the indicator. Except months mai 2007, iul 2007, aug 2007, sept 2007, oct 2007, ian 2008, apr 2008, mai 2008, iun 2008, iul 2008, aug 2008, sept 2008, oct 2008, nov 2008, apr 2009, mai 2009, iun 2009, iul 2009, aug 2009, sept 2009, oct 2009, nov 2009, ian 2010, feb 2010, mar 2010, iun 2010, iul 2010, aug 2010, sept 2010, oct 2010, nov 2010, iun 2011, iul 2011, aug 2011, sept 2011, oct 2011, nov 2011, dec 2011, ian 2012, feb 2012, iun 2012, aug 2012, sept 2012, oct 2012, nov 2012, ian 2013, mai 2013, iun 2013, iul 2013, aug 2013, sept 2013, oct 2013, dec 2013, feb 2014, mai 2014, iun 2014, iul 2014, aug 2014, sept 2014, oct 2014 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=0.443597396x+294.7355263 where x is the number of month (Jan, 2007=1), therefore a pronounced upward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=0.160560228x+293.3690789 where x is the number of month (Jan, 2007=1), therefore an upward trend. Regression analysis relative to indicator “Natural increase” gives us an equation: y=0.283037168x+1.366447368 where x is the number of month (Jan, 2007=1), therefore an upward trend.

For the set of values above, the median indicator for “Live births” is 317, for “Deceased” is 302 and for “Natural increase”: 17. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this. Also, the distribution of quartiles is for “Live births”: (239,288.75,317,339.75,432), for “Deceased”: (238,279,302,321,388) and for “Natural increase”: (-93,-31,16.5,51.25,138).

The arithmetic mean and the standard deviation for “Live births” are: (316,41.08), for “Deceased”: (301,29.73) and for “Natural increase”: (15,54.53). This means that with a probability greather than 0.68 “Live births” are in the range [275,357], for “Deceased” in [271,331] and for “Natural increase” in [-40,70].

Percentiles length indicators analysis (Figure 277) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.012187873x+10.34486184 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=-0.019653622x+10.25163816 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=0.007484672x+0.092201754 where x is the number of month (Jan, 2007=1), therefore an upward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 10, for “Deceased/10000 inh.” is 9 and for “Natural increase/10000 inh.”: 1. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (7.33,8.7725,9.505,10.685,13.09), for “Deceased/10000 inh.”: (7,8.7075,9.32,9.8075,12.6) and for “Natural increase/10000 inh.”: (-3.02,-0.91,0.51,1.695,4.48).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (10,1.3), for “Deceased/10000 inh.”: (9,1.04) and for “Natural increase/10000 inh.”: (0,1.68). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [9,11], for “Deceased/10000 inh.” in [8,10] and for “Natural increase/10000 inh.” in [-2,2]. Percentiles length indicators analysis (Figure 279) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Marriages” gives us an equation: y=-0.224511666x+196.0138158 where x is the number of month (Jan, 2007=1), therefore a downward trend.

Regression analysis relative to indicator “Divorces” gives us an equation: y=0.117702116x+22.64561404 where x is the number of month (Jan, 2007=1), therefore an upward trend.

For the set of values above, the median indicator for “Marriages” is 149 and for “Divorces” is 26. Also, the distribution of quartiles is for “Marriages”: (32,79.5,149,281,436) and for “Divorces”: (5,20,26,34.25,96). The arithmetic mean and the standard deviation for “Marriages” are: (185,114.52) and for “Divorces”: (28,14.4). This means that with a probability greather than 0.68 “Marriages” are in the range [70,300] and for “Divorces” in [14,42].

Percentiles length indicators analysis (Figure 281) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Marriages/10000 inh.” gives us an equation: y=-0.023597328x+6.892699561 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Divorces/10000 inh.” gives us an equation: y=0.001239352x+0.810412281 where x is the number of month (Jan, 2007=1), therefore a very small upward trend.

For the set of values above, the median indicator for “Marriages/10000 inh.” is 5 and for “Divorces/10000 inh.” is 1. Also, the distribution of quartiles is for “Marriages/10000 inh.”: (0.97,2.345,4.785,8.37,14.63) and for “Divorces/10000 inh.”: (0.14,0.6,0.82,1.0625,2.63). The arithmetic mean and the standard deviation for “Marriages/10000 inh.” are: (6,3.63) and for “Divorces/10000 inh.”: (1,0.43). This means that with a probability greather than 0.68 “Marriages/10000 inh.” are in the range [2,10] and for “Divorces/10000 inh.” in [1,1].

Percentiles length indicators analysis (Figure 283) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Deaths under 1 year” gives us an equation: y=-0.006992675x+2.932894737 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year” is 2 and the distribution of quartiles is for “Deaths under 1 year”: (0,1.75,2,4,7). The arithmetic mean and the standard deviation for “Deaths under 1 year” are: (3,1.58) which means that with a probability greather than 0.68 “Deaths under 1 year” are in the range [1,5].

Percentiles length indicators analysis (Figure 285) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Deaths under 1 year/100000 inh.” gives us an equation: y=-0.004082406x+1.004350877 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year/100000 inh.” is 1 and the distribution of quartiles is for “Deaths under 1 year/100000 inh.”: (0,0.4975,0.64,1.21,2.27). The arithmetic mean and the standard deviation for “Deaths under 1 year/100000 inh.” are: (1,0.5) which means that with a probability greather than 0.68 “Deaths under 1 year/100000 inh.” are in the range [1,2].

A comparison of the indicator “Deaths under 1 year” with the national level shows that it is about the same with the national, being better in 57.29% cases.

A final analysis examines dependence aforementioned indicators of regional GDP variation.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

2.27. Analysis of Natural Movement of Maramures County Population

Statistics of natural movement corresponding to Maramures County are the following:

From figure 287 we can see a sinusoidal evolution of the indicator. Except months mai 2007, iun 2007, iul 2007, aug 2007, sept 2007, oct 2007, mar 2008, apr 2008, iul 2008, aug 2008, sept 2008, oct 2008, nov 2008, feb 2009, iul 2009, aug 2009, sept 2009, aug 2010, iul 2011, aug 2011, sept 2011, mai 2012, iul 2012, aug 2012, sept 2012, iul 2013, aug 2013, iul 2014, aug 2014 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=-0.886808193x+458.3122807 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend. Regression analysis relative to indicator “Deceased” gives us an equation: y=0.014093869x+452.170614 where x is the number of month (Jan, 2007=1), therefore an upward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=-0.900902062x+6.141666667 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend. For the set of values above, the median indicator for “Live births” is 415, for “Deceased” is 445 and for “Natural increase”: -37. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this. Also, the distribution of quartiles is for “Live births”: (280,372,414.5,446.25,646), for “Deceased”: (390,425,444.5,478.25,571) and for “Natural increase”: (-208,-100.5,-37,7,214).

The arithmetic mean and the standard deviation for “Live births” are: (415,66.38), for “Deceased”: (453,40.47) and for “Natural increase”: (-38,85.84). This means that with a probability greather than 0.68 “Live births” are in the range [349,481], for “Deceased” in [413,493] and for “Natural increase” in [-124,48].

Percentiles length indicators analysis (Figure 288) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.015262547x+8.526379386 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=0.001749593x+8.40785307 where x is the number of month (Jan, 2007=1), therefore a very small upward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.017016888x+0.118756579 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 8, for “Deceased/10000 inh.” is 8 and for “Natural increase/10000 inh.”: -1. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (5.28,7.005,7.745,8.37,12.14), for “Deceased/10000 inh.”: (7.26,7.9375,8.33,9.015,10.64) and for “Natural increase/10000 inh.”: (-3.89,-1.8875,-0.69,0.13,4.02).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (8,1.23), for “Deceased/10000 inh.”: (8,0.76) and for “Natural increase/10000 inh.”: (-1,1.61). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [7,9], for “Deceased/10000 inh.” in [7,9] and for “Natural increase/10000 inh.” in [-3,1].

Percentiles length indicators analysis (Figure 290) show that, indeed the concentration is around the middle of the data.

A comparison of the indicator “Live births” with the national level shows that it is about the same with the national, being better in 53.13% cases. For “Deceased” the indicator is better than the national, being better in 98.96% cases. Finally, for “Natural increase”, the indicator is better than the national, being better in 89.58% cases.

Regression analysis relative to indicator “Marriages” gives us an equation: y=-0.960967173x+315.9506579 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Divorces” gives us an equation: y=-0.335533098x+84.86710526 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

For the set of values above, the median indicator for “Marriages” is 205 and for “Divorces” is 68. Also, the distribution of quartiles is for “Marriages”: (58,131.75,205,357.25,815) and for “Divorces”: (17,55,68,82.25,127). The arithmetic mean and the standard deviation for “Marriages” are: (269,182.3) and for “Divorces”: (69,21.46). This means that with a probability greather than 0.68 “Marriages” are in the range [87,451] and for “Divorces” in [48,90].

Percentiles length indicators analysis (Figure 292) show that, indeed the concentration is around the middle of the data.

Taking into account the population dynamics during the analyzed period we have the following evolution of the indicators: Marriages/10000 inh. and Divorces/10000 inh. as in the figure 293.

A comparison of the indicator “Marriages” with the national level shows that it is about the same with the national, being better in 52.08% cases. For “Divorces” the indicator is worse than the national, being better only in 28.13% cases.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is a dependence of Live births from GDP in the current year and the regression equation is: 0.7916dGDP+-2.7224. Searching dependence annual variations of “Deceased” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Natural increase” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Marriages” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Divorces” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is a dependence of Deaths under 1 year from GDP offset by 1 year and the regression equation is:-5.4739dGDP+0.9088.

2.28. Analysis of Natural Movement of Mehedinti County Population

Statistics of natural movement corresponding to Mehedinti County are the following:

From figure 298 we can see a sinusoidal evolution of the indicator. Except months sept 2009, aug 2011 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=-0.295523603x+224.4995614 where x is the number of month (Jan, 2007=1), therefore a downward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=-0.330921053x+353.2475877 where x is the number of month (Jan, 2007=1), therefore a downward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=0.03539745x+-128.7480263 where x is the number of month (Jan, 2007=1), therefore an upward trend.

For the set of values above, the median indicator for “Live births” is 211, for “Deceased” is 338 and for “Natural increase”: -132. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (140,187.75,210.5,230.75,303), for “Deceased”: (255,309.75,337.5,364,439) and for “Natural increase”: (-232,-167.5,-132,-91.5,21).

The arithmetic mean and the standard deviation for “Live births” are: (210,34.34), for “Deceased”: (337,37.31) and for “Natural increase”: (-127,55.99). This means that with a probability greather than 0.68 “Live births” are in the range [176,244], for “Deceased” in [300,374] and for “Natural increase” in [-183,-71].

Percentiles length indicators analysis (Figure 299) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.005678717x+7.28927193 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=-0.004101058x+11.45640132 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.001602008x+-4.165219298 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 7, for “Deceased/10000 inh.” is 11 and for “Natural increase/10000 inh.”: -4. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (4.76,6.2225,6.975,7.64,10.16), for “Deceased/10000 inh.”: (8.62,10.39,11.22,12.11,14.27) and for “Natural increase/10000 inh.”: (-7.84,-5.7225,-4.41,-3.035,0.69).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (7,1.13), for “Deceased/10000 inh.”: (11,1.22) and for “Natural increase/10000 inh.”: (-4,1.88). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [6,8], for “Deceased/10000 inh.” in [10,12] and for “Natural increase/10000 inh.” in [-6,-2].

Percentiles length indicators analysis (Figure 301) show that, indeed the concentration is around the middle of the data.

A comparison of the indicator “Live births” with the national level shows that it is worse than the national, being better only in 23.96% cases. For “Deceased” the indicator is worse than the national, being better only in 0% cases. Finally, for “Natural increase”, the indicator is worse than the national, being better only in 0% cases.

Taking into account the population dynamics during the analyzed period we have the following evolution of the indicators: Marriages/10000 inh. and Divorces/10000 inh. as in the figure 304.

A comparison of the indicator “Marriages” with the national level shows that it is better than the national, being better in 60.42% cases. For “Divorces” the indicator is about the same with the national, being better in 52.08% cases.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is a dependence of Live births from GDP offset by 1 year and the regression equation is:1.9742dGDP+3.3069. Searching dependence annual variations of “Deceased” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Natural increase” from GDP, we find that there is a dependence of Natural increase from GDP offset by 2 years and the regression equation is:4.4552dGDP+19.6447. Searching dependence annual variations of “Marriages” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Divorces” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is a dependence of Deaths under 1 year from GDP offset by 2 years and the regression equation is:-6.1815dGDP+-31.2189.

2.29. Analysis of Natural Movement Of Mures County Population

Statistics of natural movement corresponding to Mures County are the following:

From figure 309 we can see a sinusoidal evolution of the indicator. Except months aug 2007, sept 2007, iun 2008, iul 2008, aug 2008, sept 2008, oct 2008, iul 2009, aug 2009, sept 2009, aug 2010, iul 2011, aug 2011, sept 2011, aug 2012, sept 2012, oct 2012, mai 2013, iul 2013, iul 2014, sept 2014 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=-0.931741725x+550.3144737 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=-0.307813348x+588.5122807 where x is the number of month (Jan, 2007=1), therefore a downward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=-0.633288117x+-37.95219298 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

For the set of values above, the median indicator for “Live births” is 503, for “Deceased” is 576 and for “Natural increase”: -71. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (379,468.25,502.5,543,633), for “Deceased”: (437,530.25,575.5,619,752) and for “Natural increase”: (-261,-130.25,-70.5,-10.75,135).

The arithmetic mean and the standard deviation for “Live births” are: (505,56.87), for “Deceased”: (574,56.29) and for “Natural increase”: (-69,86.83). This means that with a probability greather than 0.68 “Live births” are in the range [448,562], for “Deceased” in [518,630] and for “Natural increase” in [-156,18].

Percentiles length indicators analysis (Figure 310) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.014293814x+9.072208333 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=-0.003817146x+9.700756579 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.010655792x+-0.623923246 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 8, for “Deceased/10000 inh.” is 10 and for “Natural increase/10000 inh.”: -1. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (6.33,7.7725,8.34,8.9875,10.46), for “Deceased/10000 inh.”: (7.27,8.7675,9.53,10.2525,12.44) and for “Natural increase/10000 inh.”: (-4.35,-2.155,-1.17,-0.1775,2.23).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (8,0.93), for “Deceased/10000 inh.”: (10,0.93) and for “Natural increase/10000 inh.”: (-1,1.44). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [7,9], for “Deceased/10000 inh.” in [9,11] and for “Natural increase/10000 inh.” in [-2,0].

Percentiles length indicators analysis (Figure 312) show that, indeed the concentration is around the middle of the data.

A comparison of the indicator “Live births” with the national level shows that it is better than the national, being better in 89.58% cases. For “Deceased” the indicator is worse than the national, being better only in 36.46% cases. Finally, for “Natural increase”, the indicator is better than the national, being better in 82.29% cases.

Taking into account the population dynamics during the analyzed period we have the following evolution of the indicators: Marriages/10000 inh. and Divorces/10000 inh. as in the figure 315.

A comparison of the indicator “Marriages” with the national level shows that it is worse than the national, being better only in 26.04% cases. For “Divorces” the indicator is better than the national, being better in 72.92% cases.

Regression analysis relative to indicator “Deaths under 1 year/100000 inh.” gives us an equation: y=-0.004961272x+1.097809211 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year/100000 inh.” is 1 and the distribution of quartiles is for “Deaths under 1 year/100000 inh.”: (0,0.66,0.83,1.04,1.66). The arithmetic mean and the standard deviation for “Deaths under 1 year/100000 inh.” are: (1,0.38) which means that with a probability greather than 0.68 “Deaths under 1 year/100000 inh.” are in the range [1,1].

A comparison of the indicator “Deaths under 1 year” with the national level shows that it is about the same with the national, being better in 43.75% cases.

A final analysis examines dependence aforementioned indicators of regional GDP variation.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deceased” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Natural increase” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Marriages” from GDP, we find that there is a dependence of Marriages from GDP offset by 2 years and the regression equation is:0.7835dGDP+-2.362. Searching dependence annual variations of “Divorces” from GDP, we find that there is a dependence of Divorces from GDP offset by 1 year and the regression equation is:-1.8414dGDP+-4.906. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is not a dependence of the variation of GDP.

2.30. Analysis of Natural Movement of Neamt County Population

Statistics of natural movement corresponding to Neamt County are the following:

From figure 320 we can see a sinusoidal evolution of the indicator. Except months iun 2007, iul 2007, aug 2007, sept 2007, iul 2008, aug 2008, sept 2008, iul 2009, aug 2009, sept 2009, aug 2010, aug 2011, sept 2011, aug 2012, sept 2012, aug 2013, iul 2014, aug 2014 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=-0.665056972x+464.2135965 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=0.252651926x+529.4859649 where x is the number of month (Jan, 2007=1), therefore an upward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=-0.917708899x+-65.27236842 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

For the set of values above, the median indicator for “Live births” is 429, for “Deceased” is 551 and for “Natural increase”: -123. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (325,374.75,429,475,577), for “Deceased”: (387,497.25,550.5,582.25,652) and for “Natural increase”: (-303,-209,-122.5,-35.25,120).

The arithmetic mean and the standard deviation for “Live births” are: (432,65.27), for “Deceased”: (542,58.07) and for “Natural increase”: (-110,108.12). This means that with a probability greather than 0.68 “Live births” are in the range [367,497], for “Deceased” in [484,600] and for “Natural increase” in [-218,-2].

Percentiles length indicators analysis (Figure 321) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.00961191x+7.799719298 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=0.006316739x+8.893846491 where x is the number of month (Jan, 2007=1), therefore an upward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.015944995x+-1.09364693 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 7, for “Deceased/10000 inh.” is 9 and for “Natural increase/10000 inh.”: -2. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (5.52,6.355,7.28,8.0425,9.83), for “Deceased/10000 inh.”: (6.59,8.44,9.335,9.99,11.11) and for “Natural increase/10000 inh.”: (-5.16,-3.5475,-2.075,-0.5975,2.02).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (7,1.1), for “Deceased/10000 inh.”: (9,0.99) and for “Natural increase/10000 inh.”: (-2,1.84). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [6,8], for “Deceased/10000 inh.” in [8,10] and for “Natural increase/10000 inh.” in [-4,0].

Percentiles length indicators analysis (Figure 323) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Marriages” gives us an equation: y=-1.486950624x+335.6796053 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Divorces” gives us an equation: y=-0.427177157x+107.6451754 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

For the set of values above, the median indicator for “Marriages” is 183 and for “Divorces” is 83. Also, the distribution of quartiles is for “Marriages”: (37,93,182.5,331.75,1151) and for “Divorces”: (38,61,83,104.25,243). The arithmetic mean and the standard deviation for “Marriages” are: (264,241.91) and for “Divorces”: (87,33.79). This means that with a probability greather than 0.68 “Marriages” are in the range [22,506] and for “Divorces” in [53,121].

Percentiles length indicators analysis (Figure 325) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Marriages/10000 inh.” gives us an equation: y=-0.024184889x+5.643800439 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Divorces/10000 inh.” gives us an equation: y=-0.006949878x+1.811756579 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Marriages/10000 inh.” is 3 and for “Divorces/10000 inh.” is 1. Also, the distribution of quartiles is for “Marriages/10000 inh.”: (0.63,1.5925,3.11,5.645,19.42) and for “Divorces/10000 inh.”: (0.65,1.0375,1.415,1.7725,4.13). The arithmetic mean and the standard deviation for “Marriages/10000 inh.” are: (4,4.1) and for “Divorces/10000 inh.”: (1,0.57). This means that with a probability greather than 0.68 “Marriages/10000 inh.” are in the range [0,8] and for “Divorces/10000 inh.” in [0,2].

Percentiles length indicators analysis (Figure 327) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Deaths under 1 year” gives us an equation: y=-0.018970429x+5.263815789 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year” is 4 and the distribution of quartiles is for “Deaths under 1 year”: (0,3,4,6,10). The arithmetic mean and the standard deviation for “Deaths under 1 year” are: (4,2.1) which means that with a probability greather than 0.68 “Deaths under 1 year” are in the range [2,6].

Percentiles length indicators analysis (Figure 329) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Deaths under 1 year/100000 inh.” gives us an equation: y=-0.003048426x+0.884932018 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year/100000 inh.” is 1 and the distribution of quartiles is for “Deaths under 1 year/100000 inh.”: (0,0.51,0.68,1.01,1.69). The arithmetic mean and the standard deviation for “Deaths under 1 year/100000 inh.” are: (1,0.36) which means that with a probability greather than 0.68 “Deaths under 1 year/100000 inh.” are in the range [1,1].

A comparison of the indicator “Deaths under 1 year” with the national level shows that it is about the same with the national, being better in 48.96% cases.

A final analysis examines dependence aforementioned indicators of regional GDP variation.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is a dependence of Live births from GDP offset by 1 year and the regression equation is:0.8859dGDP+-0.8957. Searching dependence annual variations of “Deceased” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Natural increase” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Marriages” from GDP, we find that there is a dependence of Marriages from GDP offset by 1 year and the regression equation is:1.0502dGDP+-3.3141. Searching dependence annual variations of “Divorces” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is not a dependence of the variation of GDP.

2.31. Analysis of Natural Movement of Olt County Population

Statistics of natural movement corresponding to Olt County are the following:

From figure 331 we can see a sinusoidal evolution of the indicator. #VALUE!

Regression analysis relative to indicator “Live births” gives us an equation: y=-0.634122355x+328.9320175 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=-0.371819045x+554.085307 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=-0.26230331x+-225.1532895 where x is the number of month (Jan, 2007=1), therefore a downward trend.

For the set of values above, the median indicator for “Live births” is 301, for “Deceased” is 539 and for “Natural increase”: -247. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (203,266.75,301,323.25,393), for “Deceased”: (397,482.75,538.5,588.25,740) and for “Natural increase”: (-402,-311.25,-246.5,-165.75,-26).

The arithmetic mean and the standard deviation for “Live births” are: (298,41.63), for “Deceased”: (536,72.33) and for “Natural increase”: (-238,91.36). This means that with a probability greather than 0.68 “Live births” are in the range [256,340], for “Deceased” in [464,608] and for “Natural increase” in [-329,-147].

Percentiles length indicators analysis (Figure 332) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.008842037x+6.713109649 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=0.000318638x+11.29392105 where x is the number of month (Jan, 2007=1), therefore a very small upward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.00915681x+-4.580269737 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 6, for “Deceased/10000 inh.” is 11 and for “Natural increase/10000 inh.”: -5. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (4.34,5.6375,6.28,6.7525,8.18), for “Deceased/10000 inh.”: (8.41,10.135,11.29,12.465,15.27) and for “Natural increase/10000 inh.”: (-8.59,-6.6975,-5.13,-3.46,-0.54).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (6,0.83), for “Deceased/10000 inh.”: (11,1.52) and for “Natural increase/10000 inh.”: (-5,1.95). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [5,7], for “Deceased/10000 inh.” in [9,13] and for “Natural increase/10000 inh.” in [-7,-3].

Percentiles length indicators analysis (Figure 334) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Marriages” gives us an equation: y=-1.58817146x+275.2763158 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Divorces” gives us an equation: y=-0.270048833x+55.68070175 where x is the number of month (Jan, 2007=1), therefore a downward trend.

For the set of values above, the median indicator for “Marriages” is 167 and for “Divorces” is 43. Also, the distribution of quartiles is for “Marriages”: (43,72.5,166.5,300.25,576) and for “Divorces”: (0,24.75,43,58,115). The arithmetic mean and the standard deviation for “Marriages” are: (198,130.83) and for “Divorces”: (43,24.4). This means that with a probability greather than 0.68 “Marriages” are in the range [67,329] and for “Divorces” in [19,67].

Percentiles length indicators analysis (Figure 336) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Marriages/10000 inh.” gives us an equation: y=-0.030085458x+5.623311404 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Divorces/10000 inh.” gives us an equation: y=-0.005066196x+1.140710526 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Marriages/10000 inh.” is 4 and for “Divorces/10000 inh.” is 1. Also, the distribution of quartiles is for “Marriages/10000 inh.”: (0.91,1.5225,3.59,6.2075,11.8) and for “Divorces/10000 inh.”: (0,0.53,0.905,1.22,2.41). The arithmetic mean and the standard deviation for “Marriages/10000 inh.” are: (4,2.72) and for “Divorces/10000 inh.”: (1,0.51). This means that with a probability greather than 0.68 “Marriages/10000 inh.” are in the range [1,7] and for “Divorces/10000 inh.” in [0,2].

Percentiles length indicators analysis (Figure 338) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Deaths under 1 year” gives us an equation: y=-0.023379002x+4.185964912 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year” is 3 and the distribution of quartiles is for “Deaths under 1 year”: (0,2,3,4,8). The arithmetic mean and the standard deviation for “Deaths under 1 year” are: (3,1.74) which means that with a probability greather than 0.68 “Deaths under 1 year” are in the range [1,5].

Percentiles length indicators analysis (Figure 340) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Deaths under 1 year/100000 inh.” gives us an equation: y=-0.004434685x+0.856436404 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Deaths under 1 year/100000 inh.” is 1 and the distribution of quartiles is for “Deaths under 1 year/100000 inh.”: (0,0.42,0.635,0.85,1.65). The arithmetic mean and the standard deviation for “Deaths under 1 year/100000 inh.” are: (1,0.36) which means that with a probability greather than 0.68 “Deaths under 1 year/100000 inh.” are in the range [1,1].

A comparison of the indicator “Deaths under 1 year” with the national level shows that it is better than the national, being better in 64.58% cases.

A final analysis examines dependence aforementioned indicators of regional GDP variation.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deceased” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Natural increase” from GDP, we find that there is a dependence of Natural increase from GDP in the current year and the regression equation is: 0.7436dGDP+1.4146. Searching dependence annual variations of “Marriages” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Divorces” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is not a dependence of the variation of GDP.

2.32. Analysis of Natural Movement of Prahova County Population

Statistics of natural movement corresponding to Prahova County are the following:

From figure 342 we can see a sinusoidal evolution of the indicator. Except months aug 2007, iul 2009, sept 2009 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=-1.490721649x+655.55 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=-0.017722463x+823.2449561 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=-1.472999186x+-167.6949561 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

For the set of values above, the median indicator for “Live births” is 584, for “Deceased” is 818 and for “Natural increase”: -255. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (432,532.75,583.5,631,768), for “Deceased”: (670,765,817.5,874.25,1029) and for “Natural increase”: (-504,-332.5,-255,-146.25,60).

The arithmetic mean and the standard deviation for “Live births” are: (583,77.18), for “Deceased”: (822,80.71) and for “Natural increase”: (-239,128.15). This means that with a probability greather than 0.68 “Live births” are in the range [506,660], for “Deceased” in [741,903] and for “Natural increase” in [-367,-111].

Percentiles length indicators analysis (Figure 343) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=-0.015716766x+7.779971491 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=0.002910404x+9.760824561 where x is the number of month (Jan, 2007=1), therefore a very small upward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=-0.018633003x+-1.980361842 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 7, for “Deceased/10000 inh.” is 10 and for “Natural increase/10000 inh.”: -3. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (5.2,6.4525,6.995,7.57,9.18), for “Deceased/10000 inh.”: (8.01,9.115,9.835,10.4875,12.45) and for “Natural increase/10000 inh.”: (-6.16,-4.0225,-3.08,-1.7425,0.72).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (7,0.9), for “Deceased/10000 inh.”: (10,0.98) and for “Natural increase/10000 inh.”: (-3,1.55). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [6,8], for “Deceased/10000 inh.” in [9,11] and for “Natural increase/10000 inh.” in [-5,-1].

Percentiles length indicators analysis (Figure 345) show that, indeed the concentration is around the middle of the data.

A comparison of the indicator “Live births” with the national level shows that it is worse than the national, being better only in 5.21% cases. For “Deceased” the indicator is worse than the national, being better only in 10.42% cases. Finally, for “Natural increase”, the indicator is worse than the national, being better only in 4.17% cases.

Taking into account the population dynamics during the analyzed period we have the following evolution of the indicators: Marriages/10000 inh. and Divorces/10000 inh. as in the figure 348.

A comparison of the indicator “Marriages” with the national level shows that it is worse than the national, being better only in 31.25% cases. For “Divorces” the indicator is about the same with the national, being better in 46.88% cases.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is a dependence of Live births from GDP offset by 1 year and the regression equation is:0.4717dGDP+-3.813. Searching dependence annual variations of “Deceased” from GDP, we find that there is a dependence of Deceased from GDP offset by 2 years and the regression equation is:-0.2596dGDP+-0.6757. Searching dependence annual variations of “Natural increase” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Marriages” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Divorces” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is not a dependence of the variation of GDP.

2.33. Analysis of Natural Movement of Salaj County Population

Statistics of natural movement corresponding to Salaj County are the following:

From figure 353 we can see a sinusoidal evolution of the indicator. Except months aug 2007, sept 2007, iul 2008, iul 2009, aug 2009, sept 2009, aug 2010, sept 2010, aug 2011, sept 2011, mai 2012, aug 2012, iul 2013, aug 2013, sept 2013, iul 2014, aug 2014, sept 2014 the natural increase was negative.

Regression analysis relative to indicator “Live births” gives us an equation: y=-0.05846446x+215.1063596 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Deceased” gives us an equation: y=-0.238435974x+267.9287281 where x is the number of month (Jan, 2007=1), therefore a downward trend.

Regression analysis relative to indicator “Natural increase” gives us an equation: y=0.179971514x+-52.82236842 where x is the number of month (Jan, 2007=1), therefore an upward trend.

For the set of values above, the median indicator for “Live births” is 208, for “Deceased” is 255 and for “Natural increase”: -54. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births”: (157,193.75,208,229,265), for “Deceased”: (195,235.75,254.5,279,318) and for “Natural increase”: (-131,-72,-54,-12,60).

The arithmetic mean and the standard deviation for “Live births” are: (212,24.04), for “Deceased”: (256,28.83) and for “Natural increase”: (-44,41.97). This means that with a probability greather than 0.68 “Live births” are in the range [188,236], for “Deceased” in [227,285] and for “Natural increase” in [-86,-2].

Percentiles length indicators analysis (Figure 354) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Live births/10000 inh.” gives us an equation: y=0.000207406x+8.391607456 where x is the number of month (Jan, 2007=1), therefore a very small upward trend.

Regression analysis relative to indicator “Deceased/10000 inh.” gives us an equation: y=-0.006422273x+10.45689693 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Natural increase/10000 inh.” gives us an equation: y=0.006600312x+-2.064802632 where x is the number of month (Jan, 2007=1), therefore an upward trend.

For the set of values above, the median indicator for “Live births/10000 inh.” is 8, for “Deceased/10000 inh.” is 10 and for “Natural increase/10000 inh.”: -2. This means that the probability that the indicator has a value less than the median is equal to the probability that it has a higher value than this.

Also, the distribution of quartiles is for “Live births/10000 inh.”: (6.27,7.665,8.205,9.0725,10.63), for “Deceased/10000 inh.”: (7.79,9.35,10.125,11.03,12.43) and for “Natural increase/10000 inh.”: (-5.12,-2.8175,-2.115,-0.47,2.41).

The arithmetic mean and the standard deviation for “Live births/10000 inh.” are: (8,0.95), for “Deceased/10000 inh.”: (10,1.12) and for “Natural increase/10000 inh.”: (-2,1.66). This means that with a probability greather than 0.68 “Live births/10000 inh.” are in the range [7,9], for “Deceased/10000 inh.” in [9,11] and for “Natural increase/10000 inh.” in [-4,0].

Percentiles length indicators analysis (Figure 356) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Marriages” gives us an equation: y=-0.640816603x+148.9546053 where x is the number of month (Jan, 2007=1), therefore a pronounced downward trend.

Regression analysis relative to indicator “Divorces” gives us an equation: y=0.004252577x+25.65833333 where x is the number of month (Jan, 2007=1), therefore an upward trend.

For the set of values above, the median indicator for “Marriages” is 108 and for “Divorces” is 26. Also, the distribution of quartiles is for “Marriages”: (24,55.5,107.5,169.75,336) and for “Divorces”: (3,20,25.5,32,50). The arithmetic mean and the standard deviation for “Marriages” are: (118,70.96) and for “Divorces”: (26,8.67). This means that with a probability greather than 0.68 “Marriages” are in the range [47,189] and for “Divorces” in [17,35].

Percentiles length indicators analysis (Figure 358) show that, indeed the concentration is around the middle of the data.

Regression analysis relative to indicator “Marriages/10000 inh.” gives us an equation: y=-0.023821148x+5.815221491 where x is the number of month (Jan, 2007=1), therefore a very small downward trend.

Regression analysis relative to indicator “Divorces/10000 inh.” gives us an equation: y=0.000438416x+1.002070175 where x is the number of month (Jan, 2007=1), therefore a very small upward trend.

For the set of values above, the median indicator for “Marriages/10000 inh.” is 4 and for “Divorces/10000 inh.” is 1. Also, the distribution of quartiles is for “Marriages/10000 inh.”: (0.95,2.225,4.265,6.7525,13.14) and for “Divorces/10000 inh.”: (0.12,0.7875,1.01,1.255,1.97). The arithmetic mean and the standard deviation for “Marriages/10000 inh.” are: (5,2.8) and for “Divorces/10000 inh.”: (1,0.34). This means that with a probability greather than 0.68 “Marriages/10000 inh.” are in the range [2,8] and for “Divorces/10000 inh.” in [1,1].

Percentiles length indicators analysis (Figure 360) show that, indeed the concentration is around the middle of the data.

In what follows, we shall investigate if there is a dependency between GDP variation (noted with dGDP) and the aforementioned indicators.

Searching dependence annual variations of “Live births” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deceased” from GDP, we find that there is a dependence of Deceased from GDP offset by 2 years and the regression equation is:-0.3439dGDP+-2.1961. Searching dependence annual variations of “Natural increase” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Marriages” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Divorces” from GDP, we find that there is not a dependence of the variation of GDP. Searching dependence annual variations of “Deaths under 1 year” from GDP, we find that there is not a dependence of the variation of GDP.

Bibliography

Ioan, Gina & Ioan, Cătălin Angelo (2017). Macroeconomics. Galati: Zigotto Publishers.

Ioan, Cătălin Angelo (2011). Mathematics. Galati: Zigotto Publishers.

Voineagu, Vergil; Mitrut, Constantin & Isaic-Maniu, Alexandru (2003). Statistics. Bucharest: Universitara Publishers.

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Acta Universitatis Danubius. Œconomica, Vol 13, No 6 (2017)

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