Compute the normalized euclidean

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Reference no: EM133001559

Problem #1 (Cluster Analysis): For the Excel file Colleges and Universities Cluster Analysis Worksheet (D2L Content > Datasets by Chapter > Chapter 10 > CollegesandUniversitiesClusterAnalysisWorksheet.xlsx), compute the normalized Euclidean distances between Berkley, Cal Tech, UCLA, and illustrate the results in a distance matrix.

Problem #2 (Cluster Analysis): For the three clusters identified in Table 10.3, find the average and standard deviations of each numerical variable for the schools in each cluster and compare them with the average and standard deviation for the entire data set. Does the clustering show distinct differences among these clusters?
This is Table 10.3 (taken from the author's PPT - but matches the textbook)

Problem #3 (Classification): Using the approach described in Example 10.6, classify first record in the worksheet Records to Classify in the Excel File Credit Risk Data (D2L Content > Datasets by Chapter > Chapter 10 > CreditRiskData.xlsx), using the l-NN algorithm for k = 1 to 5. Use only Checking, Savings, Months Customer, and Months Employed.

Problem #4 (Classification): Use discriminant analysis to classify the new records in the Excel file Credit Approval Decisions Discriminant Analysis (D2L Content > Datasets by Chapter > Chapter 10 > CreditApprovalDecisionsDiscriminantAnalysis.xlsx) using only Credit Score and Years of Credit History as input variables.

Problem #5 (Residual Analysis and Regression Assumptions): Use the results from Problem #11 (D2L Content > Datasets by Chapter > Chapter 8 > National Football League.xlsx) to analyze the residuals to determine if the assumptions underlying the regression analysis are valid. In addition, use the standard residuals to determine if any possible outliers exist.

Problem #6 (Building Good Regression Models): The State of Ohio Department of Education has a mandated ninth-grade proficiency test that covers writing, reading, mathematics, citizenship (social studies), and science. The Excel file Ohio Education Performance (D2L Content > Datasets by Chapter > Chapter 8 > OhioEducationPerformance.xlsx) provides data on success rates (defined as the percent of students passing) in school districts in the greater Cincinnati metropolitan area with state averages.

Part A: Suggest the best regression model to predict math success as a function of success in the other subjects by examining the correlation matrix; then run the Regression tool for this set of variables. (Note: that "All" is not an academic subject!)

Part B: Develop a multiple regression model to predict math success as a function of success in all other subjects using the systematic approach described in the chapter. Is multicollinearity a problem?

Part C: Compare models in parts A and B. Are they the same? Why or why not?

Problem #7 (Regression with Categorical Independent Variables): A national homebuilder builds single-family homes and condominium-style townhouse. The Excel file House Sales (D2L Content > Datasets by Chapter > Chapter 8 > HouseSales.xlsx) provides information on the selling price, lot cost, type of home, and region of the country (M=Midwest, S=South) for closings during one month.

Part A: Develop a multiple regression model for sales prices as a function of lost cost and type of home without any interaction term.

Part B: determine if an interaction exists between lot cost and type of home and find the best model.

Attachment:- Credit Risk Data.rar

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This task provides a clear working procedure on discriminant analysis and multiple regression models. Discriminant analysis was used for the loan approval process. Multiple regression regression was used to determine the factors that influenced the math success. The best model was classified using adjusted r square value. the two models used for comparison are independent variables with interaction and without interaction.

Reference no: EM133001559

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