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# Compare the slopes of the two analyses (the b coefficient. How much does life expectancy in a country change if literacy rate is increased by 1% for

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Compare the slopes of the two analyses (the b coefficient. How much does life expectancy in a country change if literacy rate is increased by 1% for males and for females? Next, talk about R 2 . What is the proportion of variance of female life expectancy accounted for by the country’s female literacy rate? What is the proportion of variance in make life expectancy accounted for by the make literacy rate? Do you have any hypotheses about why one is higher than the other? What other factors do you think may be important predictors of male and female life expectancy? Here are the information you need to answer the question Correlations Females who read (%) Males who read (%) Number of people / sq. kilometer People who read (%) Fertility: average number of kids Gross domestic product / capita Females who read (%) Pearson Correlation 1 .964 ** .029 .973 ** -.839 ** Sig. (2-tailed) .000 .795 .000 .000 N 85 85 85 85 85 Males who read (%) Pearson Correlation .964 ** 1 .085 .948 ** -.796 ** Sig. (2-tailed) .000 .437 .000 .000 N 85 85 85 85 85 Number of people / sq. kilometer Pearson Correlation .029 .085 1 .031 -.162 Sig. (2-tailed) .795 .437 .753 .095 N 85 85 109 107 107 People who read (%) Pearson Correlation .973 ** .948 ** .031 1 -.866 ** Sig. (2-tailed) .000 .000 .753 .000 N 85 85 107 107 105 Fertility: average number of kids Pearson Correlation -.839 ** -.796 ** -.162 -.866 ** 1 -.583 Sig. (2-tailed) .000 .000 .095 .000 N 85 85 107 105 107 Gross domestic product / capita Pearson Correlation .429 ** .417 ** .201 * .552 ** -.583 ** Sig. (2-tailed) .000 .000 .036 .000 .000 N 85 85 109 107 107 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
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