CXU10A:Create a research question using the General Social Survey dataset that can be answered by multiple regression. Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables.Estimate a multiple regression model that answers your research question.What is your research question?Interpret the coefficients for the model, specifically commenting on the dummy variable.Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?CXU10B:In CXU9B/C, you completed Part 1 for this Assignment. You will complete Part 2 where you will create a research question that can be answered through multiple regression using dummy variables.Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) Consider the following:Create a research question with metric variables and one variable that requires dummy coding. Estimate the model and report results. Note: You are expected to perform regression diagnostics and report that as well.Once you perform your analysis, copy and paste your output into your Word document.Write a 2- to 3-page analysis of your multiple regression using dummy variables results for each research question. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be.Use proper APA format, citations, and referencing for your analysis, research question, and display of output.

Question 2: Machine Learning a) Which of unsupervised or supervised machine learning is best suited to assessing causation? Explain your choice. b) Your analytics team presents you with two sets of results that have improved the organization’s ability to predict customer defections. The first method uses deep learning and has a precision of 85%. The second method uses decision trees and has a precision of 70%. The previous approach had a precision of 40%. i) Make a case for using the results of the deep learning method. ii) Make a case for using the decision tree method. In your answers, consider aspects of customer lifetime value and managerial decision making. c) An analytics team used two different models to predict the likelihood of an outcome. The results from two different analysts are below: Don’s Analysis Actual Positive Negative Predicted Positive 220 100 Negative 30 650 Katie’s Analysis Actual Positive Negative Predicted Positive 170 10 Negative 80 740 i) Use the Confusion Matrix and Index Calculation tables below to calculate the model performance measures. Confusion Matrix Actual Positive Negative Predicted Positive TP FP Negative FN TN Formula Don Calculation Katie Calculation Accuracy (completed as an example) (TP + TN) / (TP + TN + FP + FN) (220 + 650) / (220 + 650 + 100 + 30) 0.87 (170 + 740) / (170 + 740 + 10 + 80) 0.91 Precision TP / (TP + FP) Error rate (FP + FN) / (TP + TN + FP + FN) Recall TP / (TP + FN) Specificity TN / (TN + FP) False positive rate FP / (TN + FP) F-score 2* ((Precision*Recall) / (Precision + Recall)) ii) Describe a medical or business context where you would prefer to use Don’s model. Why do you prefer Don’s model? iii) Describe a medical or business context where you would prefer to use Katie’s model. Why do you prefer Katie’s model? Ian is an intern with the team who claims he made a breakthrough with a model that outperforms both Don’s and Katie’s. The confusion matrix for his model is below: Ian’s Analysis Actual Positive Negative Predicted Positive 249 2 Negative 1 748 iv) What could possibly have gone wrong that would result in his results being invalid? How could this be solved? (15 marks) Question 3: Experiments Jennifer was given the results of an experiment that was designed to determine if a 10% reduction in price on an online shopping portal would lead to an increase in purchases. Control and treatment group were created. These groups are described below: Control Group Treatment Group Number of males 25 25 Number of females 25 25 Average age 47 years 37 years Average spend per visit in the month BEFORE the experiment $25.00 $25.00 Average spend per visit in the month AFTER the experiment $25.00 $29.00 a) Were the control and treatment groups effectively randomized? Why or why not? b) What are the two most likely explanations for the treatment groups showing a higher average spend than the control group? c) What type of analysis could be used to remove one of the possible explanations for the difference in average spend? d) Experiments are useful in helping determine if people have responded due to a stimulus or if they would have responded even without the stimulus. Design an experiment that could demonstrate what proportion of people have responded to a stimulus. These people could be customers or employees within a company. Examples could be an advertising campaign to customers, or a policy of flexible work hours for employees. Requirements: i) How would you pick the treatment and control groups? Fill in the table below to indicate the number of people and 3 important characteristics that describe each group Control Group Treatment Group People Characteristic 1: Characteristic 2: Characteristic 3: ii) Predict the results and state the managerial conclusion you could make from this result. Use the table below to indicate the change in behavior you expect to observe. Control Group Treatment Group Observed behavior before treatment: Observed behavior after treatment: iii) State the managerial action you could take from the results of your experiment. Briefly describe a useful follow-up experiment that would further deepen understanding of why people behaved in the manner observed.