Question 23 1 pts The table below shows the output of a regression analysis to predict test scores based on p = 5 independent variables. Is there evidence that any of the independent variables are useful for predicting test scores? Use a 5% significance level. (If you are having difficulty reading the table below, please zoom in using your browser or device.) SUMMARY OUTPUT Regression Statistics Multiple R 0.55609914 R Square 0.309246254 Adjusted R Square 0.272108956 Standard Error 0.399717208 Observations 99 ANOVA df MS Significance F Regression 5 6.652269528 1.330453906 8.327106955 1.54541E-06 Residual 93 14.85896769 0.159773846 Total 98 21.51123722 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3.810710692 0.123201774 30.93064793 9.7963E-51 3.566056386 4.055364999 3.566056386 4.055364999 X1 0.279089748 0.050775587 5.496534105 3.37115E-07 0.178259494 0.379920001 0.178259494 0.379920001 X2 0.245720925 0.082971349 -2.961515372 0.003885212 -0,41048558 -0.08095627 -0.41048558 -0.08095627 X3 -0.209130674 0.091013386 -2.29780127 0.023815124 -0.389865221 -0.028396127 -0.389865221 -0.028396127 X4 -0.282288583 0.160089251 -1.763320033 0.081131053 3 -0.600194108 0.035616942 -0.600194108 0.035616942 0.062357237 0.115643193 0.539220994 0.591022967 -0.167287225 0.2920017 -0.167287225 0.2920017 O Yes O No O Cannot be determined from the information givenQuestion 26 1 pts Which of the following could be plausible values for the correlation coefficients of each scatterplot, going LEFT to RIGHT? Assume the units are the same for all four scatterplots. (1) (4) 009 0.0 02 04 0.6 0.8 1.0 0.0 02 04 0.6 0.8 1.0 00 02 04 06 0.8 1.0 0.0 02 04 0.6 0.8 1.0 O 0.498, 0.419, 0.029, -0.473 O -0.945, 0.997, 0.059, 0.838 O 0.997, 0.838, 0.059, -0.945 O 0.997, -0.945, 0.059, 0.838Question 27 1 pts The COMPAS [Correctional Offender Management Profiling for Alternative Sanctions} algorithm is currently used in US court systems to predict the likelihood that a defendant would become a recidivist (Le. to commit another crime after being released}. After a careful analysis. ProPublica found that there was noticeable discrepancy in the predictions based on race and occupation. Northpointe, the company that created COMPAS, countered that any discrepancies could not be taken as evidence of bias since race and occupation were never used as variables in their model. What is the BEST explanation of this reasoning? O Northpointe is correct that without explictly including these variables in CDMPAS, it is not possible for COM PAS to discriminate based on these attributes. 0 Even though Northpointe did not explicitly include these variables in ICOMPASr it is still possible for COMPAS to discriminate based on these attributes, e.g. by reconstructing proxies based on other variables in the model. C) Northpointe has nothing to worry about since it is impossible for COM PAS to reconstruct proxies for race and occupation based on other variables in the model. C) If Northpointe had only included these variables, it could have prevented these discrepancies, and they should go back and re-build their model using these variables. Question 23 1 pts Which of the following is the BEST use case for regression? O Forecasting future stock returns on the London stock exchange based on data from the US stock exchange. 0 Predicting health outcomes for a particular rare form of cancer {e.g. bile duct cancer] by using aggregated data across all forms of cancer [e.g. lung, kidney, brain, etc.] known to a hospital. 0 Determining how customer spending behaviour will change in response to a promotion. 0 Predicting customer attributes from anonymized social media