the bottom picture is first part of question, the top is the graph that goes along with the bottom (first part) of the question

37. The researcher above wants to check that he has satisfied some of the assumptions of regression first, before he publishes his findings. He conducted a histogram of his dependent variable and his independent variable. Second, he conducted a scatterplot of his two variables. He also saved his residuals to look at the error terms to check their assumptions with a histogram and a correlation matrix with the independent variable. Finally, he conducted a correlation matrix to see whether there are any issues with multicollinearity. Using the output below, answer the following questions about how confident he can be in his findings (6 points). a. Does he have any issues with the normality assumption of the two variables? b. Does he have any issues with heteroscedasticity? With linearity? c. Looking at the correlation matrix and histogram, does he have any issues with assuming normality and independence (non-correlation) of the error term (residuals)? 100 Std. Dev - 3,491 =1.831 500- 400- Frequency Frequency 200- 2.50 2 50 5.00 -10 10 20 Log Incarceration Number of prior convictions of WMean = -1.02E-4 Sid. Dev. = 1 23853 1=547 O 5 00 60 250 8 0 Frequency Log Incarceration 0 00 0 1Rom DOT COMTO CID CO O 2 50- 20 10 25 -2.00000 00000 2.00000 4.00000 Number of prior convictions Unstandardized Residual Correlations Number of prior Unstandardiz convictions ed Residual Number of prior Pearson Correlation .000 convictions Sig. (2-tailed 1.000 N 1336 547 Unstandardized Residual Pearson Correlation .000 Sig. (2-tailed) 1.000 547 547 Correlations Number of If a public prior. defender was Conviction convictions used Violent Severity DGender Number of prior Pearson Correlation 140 .036 076 139 convictions Sig. (2-tailed) :000 .183 .005 .000 N 1336 1336 1336 1336 1336 If a public defender was Pearson Correlation 140 -.043 106 -.013 used Sig. (2-tailed) .000 120 .000 .626 N 1336 1336 1336 1336 1336 Violent Pearson Correlation -.036 .043 254 027 Sig. (2-tailed) 183 .120 .000 326 N 1336 1336 1336 1336 1336 Conviction Severity Pearson Correlation 076 106 254 043 Sig. (2-tailed) .005 1000 .000 117 N 1336 1336 1336 1336 1336 DGender Pearson Correlation 139 -.013 .027 043 Sig. (2-tailed) .000 .626 326 117 1336 1336 1336 1336 1336 * Correlation is significant at the 0.01 level (2-tailed). 4 W O