question_for_statistics..docx
Running Head: Data analysis drawn from World Bank. 3
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Year\Indicator Name
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1995
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1996
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1997
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1998
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1999
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2000
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2001
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2002
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2003
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2004
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2005
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2006
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2007
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2008
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Life expectancy at birth, total (years)
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77.82926829
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78.07804878
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78.4804878
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78.63170732
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78.93170732
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79.23414634
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79.63414634
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79.93658534
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80.23902439
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80.4902439
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80.84146341
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81.04146341
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81.29268293
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81.39512195
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CO2 emissions (metric tons per capita)
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17.0116172
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17.98153733
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18.01715505
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18.54058404
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17.19979166
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17.20903399
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16.73412301
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17.372691
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17.4149047
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17.35063038
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18.01405569
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18.2328628
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18.07162335
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18.56961909
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Health expenditure per capita (current US$)
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1574.279016
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1767.721588
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1766.049449
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1597.620745
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1754.984609
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1728.461993
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1644.80348
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1858.490441
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2339.219055
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2888.976962
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3157.754309
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3330.212891
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3956.468724
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4229.033066
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GDP growth (annual %)
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3.939983314
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4.181423944
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3.972448021
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4.585530281
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5.15985438
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3.951352155
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2.071616393
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3.903791221
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3.272264035
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4.155794748
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2.959140943
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3.081394099
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3.564177973
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3.832065489
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Mobile cellular subscriptions (per 100 people)
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12.37415015
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21.77990754
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24.71234651
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26.25050454
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33.32824218
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44.67670207
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57.43417616
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64.62949928
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72.31307069
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81.97446237
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90.27854017
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95.25510508
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100.662936
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102.8176451
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1 .Create appropriate plots (histogram, pie-charts, bar charts box and whiskers, scatter plots) and comment on the general trends present.
2. Calculate the mean, median, standard deviation for each variable and comment.
3. Perform correlation analysis of each variable with life expectancy. Comment on the result and the implied relationship .i. e .just because the two variables are highly correlated does this imply a cause and effect relationship? Discuss.
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