# SAS HATCO Multiple Regression Analysis, programming homework help

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### Question Description

SAS HATCO Multiple Regression Analysis – HW

Perform a Multiple Regression Analysis paralleling the HBAT analysis performed in the textbook and in class, but now use the HATCO dataset (see HATCO_X1-X14_Tabs data file).

Include the following analyses:

• Consider the overall general Model X9 = X1 X2 X3 X4 X5 X6 X7
• Perform a Correlation Analysis
• Fit and Analyze the best single independent (simple) regression analysis
• Perform a Stepwise Regression Analysis to select the best subset model using Alpha =0.05
• Perform a Full Model (Confirmatory) Regression Analysis using Alpha = 0.05
• Using the best subset from the Stepwise Regression Analysis now include variable X8 (Firm Size) and analyze its effect and the new resultant model
• Analyze and Discuss the Multiple Regression Analysis Assumptions, e.g., Linearity, Normality, Homoscedasticity, and Independence using Residual and other Graphical/Statistical measures and tests for all Model Analyses.
• Similarly Analyze and Discuss any Influential observations with recommendations, as well as the Model’s Multicollinearity.

Provide a brief 2-3 page management summary including key selected results along with the SAS program and output as an appendix.

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SAS HATCO Multiple Regression Analysis – HW Perform a Multiple Regression Analysis paralleling the HBAT analysis performed in the textbook and in class, but now use the HATCO dataset (see HATCO_X1-X14_Tabs data file). Include the following analyses: • • • • • • • • Consider the overall general Model X9 = X1 X2 X3 X4 X5 X6 X7 Perform a Correlation Analysis Fit and Analyze the best single independent (simple) regression analysis Perform a Stepwise Regression Analysis to select the best subset model using Alpha =0.05 Perform a Full Model (Confirmatory) Regression Analysis using Alpha = 0.05 Using the best subset from the Stepwise Regression Analysis now include variable X8 (Firm Size) and analyze its effect and the new resultant model Analyze and Discuss the Multiple Regression Analysis Assumptions, e.g., Linearity, Normality, Homoscedasticity, and Independence using Residual and other Graphical/Statistical measures and tests for all Model Analyses. Similarly Analyze and Discuss any Influential observations with recommendations, as well as the Model’s Multicollinearity. Provide a brief 2-3 page management summary including key selected results along with the SAS program and output as an appendix. SAS Output The SAS System HBAT_Multiple Regression_Normal_Unpack-Plots_SAS_Output_Sp16.htm[3/30/2016 5:19:31 AM] Obs ID X3 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 1 1 1 8.5 3.9 2.5 5.9 4.8 4.9 6.0 6.8 4.7 4.3 5.0 5.1 3.7 8.2 2 2 0 8.2 2.7 5.1 7.2 3.4 7.9 3.1 5.3 5.5 4.0 3.9 4.3 4.9 5.7 3 3 1 9.2 3.4 5.6 5.6 5.4 7.4 5.8 4.5 6.2 4.6 5.4 4.0 4.5 8.9 4.8 4 4 1 6.4 3.3 7.0 3.7 4.7 4.7 4.5 8.8 7.0 3.6 4.3 4.1 3.0 5 5 1 9.0 3.4 5.2 4.6 2.2 6.0 4.5 6.8 6.1 4.5 4.5 3.5 3.5 7.1 6 6 0 6.5 2.8 3.1 4.1 4.0 4.3 3.7 8.5 5.1 9.5 3.6 4.7 3.3 4.7 5.7 7 7 1 6.9 3.7 5.0 2.6 2.1 2.3 5.4 8.9 4.8 2.5 2.1 4.2 2.0 8 8 1 6.2 3.3 3.9 4.8 4.6 3.6 5.1 6.9 5.4 4.8 4.3 6.3 3.7 6.3 9 9 1 5.8 3.6 5.1 6.7 3.7 5.9 5.8 9.3 5.9 4.4 4.4 6.1 4.6 7.0 10 10 1 6.4 4.5 5.1 6.1 4.7 5.7 5.7 8.4 5.4 5.3 4.1 5.8 4.4 5.5 11 11 1 8.7 3.2 4.6 4.8 2.7 6.8 4.6 6.8 5.8 7.5 3.8 3.7 4.0 7.4 12 12 1 6.1 4.9 6.3 3.9 4.4 3.9 6.4 8.2 5.8 5.9 3.0 4.9 3.2 6.0 13 13 0 9.5 5.6 4.6 6.9 5.0 6.9 6.6 7.6 6.5 5.3 5.1 4.5 4.4 8.4 14 14 0 9.2 3.9 5.7 5.5 2.4 8.4 4.8 7.1 6.7 3.0 4.5 2.6 4.2 7.6 15 15 1 6.3 4.5 4.7 6.9 4.5 6.8 5.9 8.8 6.0 5.4 4.8 6.2 5.2 8.0 16 16 0 8.7 3.2 4.0 6.8 3.2 7.8 3.8 4.9 6.1 5.0 4.3 3.9 4.5 6.6 17 17 0 5.7 4.0 6.7 6.0 3.3 5.5 5.1 6.2 6.7 5.4 4.2 6.2 4.5 6.4 18 18 1 5.9 4.1 5.5 7.2 3.5 6.4 5.5 8.4 6.2 6.3 5.7 5.8 4.8 7.4 19 19 1 5.6 3.4 5.1 6.4 3.7 5.7 5.6 9.1 5.4 6.1 5.0 6.0 4.5 6.8 20 20 1 9.1 4.5 3.6 6.4 5.3 5.3 7.1 8.4 5.8 6.7 4.5 6.1 4.4 7.6 21 21 0 5.2 3.8 7.1 5.2 3.9 4.3 5.0 8.4 7.1 4.6 3.3 4.9 3.3 5.4 22 22 1 9.6 5.7 6.8 5.9 5.4 8.3 7.8 4.5 6.4 6.5 4.3 3.0 4.3 9.9 23 23 0 8.6 3.6 7.4 5.1 3.5 7.3 4.7 3.7 6.7 6.0 4.8 3.4 4.0 7.0 24 24 1 9.3 2.4 2.6 7.2 2.2 7.2 4.5 6.2 6.4 4.2 6.7 4.4 4.5 8.6 25 25 0 6.0 4.1 5.3 4.7 3.5 5.3 5.3 8.0 6.5 3.9 4.7 5.3 4.0 4.8 6.6 26 26 1 6.4 3.6 6.6 6.1 4.0 3.9 5.3 7.1 6.1 3.7 5.6 6.6 3.9 27 27 0 8.5 3.0 7.2 5.8 4.1 7.6 3.7 4.8 6.9 6.7 5.3 3.8 4.4 6.3 28 28 0 7.0 3.3 5.4 5.5 2.6 4.8 4.2 9.0 6.5 5.9 4.3 5.2 3.7 5.4 29 29 0 8.5 3.0 5.7 6.0 2.3 7.6 3.7 4.8 5.8 6.0 5.7 3.8 4.4 6.3 30 30 1 7.6 3.6 3.0 4.0 5.1 4.2 4.6 7.7 4.9 7.2 4.7 5.5 3.5 5.4 31 31 0 6.9 3.4 8.5 4.3 4.5 6.4 4.7 5.2 7.7 3.3 3.7 2.7 3.3 6.1 6.4 32 32 1 8.1 2.5 7.2 4.5 2.3 5.1 3.8 6.6 6.8 6.1 3.0 3.5 3.0 33 33 1 6.7 3.7 6.5 5.3 5.3 5.1 4.9 9.2 5.7 4.2 3.5 4.5 3.4 5.4 34 34 1 8.0 3.3 6.1 5.7 5.5 4.6 4.7 8.7 5.9 3.8 4.7 6.6 4.2 7.3 6.3 35 35 1 6.7 4.0 5.2 3.9 3.0 5.4 6.8 8.4 6.2 6.0 2.5 4.3 3.5 36 36 0 8.7 3.2 6.1 4.3 3.5 6.1 2.9 5.6 6.1 6.5 3.1 2.9 2.5 5.4 37 37 0 9.0 3.4 5.9 4.6 3.9 6.0 4.5 6.8 6.4 4.3 3.9 3.5 3.5 7.1 8.7 38 38 1 9.6 4.1 6.2 7.3 2.9 7.7 5.5 7.7 6.1 4.4 5.2 4.6 4.9 39 39 1 8.2 3.6 3.9 6.2 5.8 4.9 5.0 9.0 5.2 7.1 4.7 6.9 4.5 7.6 40 40 0 6.1 4.9 3.0 4.8 5.1 3.9 6.4 8.2 5.1 6.8 4.5 4.9 3.2 6.0 7.0 41 41 1 8.3 3.4 3.3 5.5 3.1 4.6 5.2 9.1 4.1 1.7 4.6 5.8 3.9 42 42 0 9.4 3.8 4.7 5.4 3.8 6.5 4.9 8.5 4.9 6.2 4.1 4.5 4.1 7.6 43 43 1 9.3 5.1 4.6 6.8 5.8 6.6 6.3 7.4 5.1 4.1 4.6 4.6 4.3 8.9 44 44 1 5.1 5.1 6.6 6.9 4.4 5.4 7.8 5.9 7.2 5.2 4.9 6.3 4.5 7.6 45 45 0 8.0 2.5 4.7 7.1 3.6 7.7 3.0 5.2 5.1 3.9 4.3 4.2 4.7 5.5 SAS Output HBAT_Multiple Regression_Normal_Unpack-Plots_SAS_Output_Sp16.htm[3/30/2016 5:19:31 AM] 46 46 1 5.9 4.1 5.7 5.9 5.8 6.4 5.5 8.4 6.4 5.1 5.2 5.8 4.8 47 47 0 10.0 4.3 7.1 6.3 2.9 5.4 4.5 3.8 6.7 3.7 5.0 4.0 3.5 7.4 7.1 48 48 1 5.7 3.8 6.8 7.5 5.7 5.7 6.0 8.2 6.6 4.8 6.5 7.3 5.2 7.6 8.7 49 49 0 9.9 3.7 3.7 6.1 4.2 7.0 6.7 6.8 5.9 7.2 4.5 3.4 3.9 50 50 1 7.9 3.9 4.3 5.8 4.4 6.9 5.8 4.7 5.2 3.6 4.1 4.2 4.3 8.6 51 51 1 6.7 3.6 5.9 4.2 3.4 4.7 4.8 7.2 5.7 5.3 4.0 3.6 2.8 5.4 52 52 0 8.2 2.7 3.7 7.4 2.7 7.9 3.1 5.3 5.3 5.0 4.5 4.3 4.9 5.7 53 53 1 9.4 2.5 4.8 6.1 3.2 7.3 4.6 6.3 6.3 9.2 4.7 4.6 4.6 8.7 54 54 0 6.9 3.4 5.7 4.4 3.3 6.4 4.7 5.2 6.4 4.4 3.2 2.7 3.3 6.1 55 55 1 8.0 3.3 3.8 5.8 3.2 4.6 4.7 8.7 5.3 4.2 4.9 6.6 4.2 7.3 56 56 0 9.3 3.8 7.3 5.7 3.7 6.4 5.5 7.4 6.6 5.9 4.1 3.2 3.4 7.7 57 57 1 7.4 5.1 4.8 7.7 4.5 7.2 6.9 9.6 6.4 7.4 5.7 6.5 5.5 9.0 58 58 0 7.6 3.6 5.2 5.8 5.6 6.6 5.4 4.4 6.7 6.4 4.6 3.9 4.0 8.2 59 59 0 10.0 4.3 5.3 3.7 4.2 5.4 4.5 3.8 6.7 4.5 3.7 4.0 3.5 7.1 60 60 1 9.9 2.8 7.2 6.9 2.6 5.8 3.5 5.4 6.2 7.0 5.6 4.9 4.0 7.9 61 61 0 8.7 3.2 8.4 6.1 2.8 7.8 3.8 4.9 7.2 4.5 5.4 3.9 4.5 6.6 62 62 1 8.4 3.8 6.7 5.0 4.5 4.7 5.9 6.7 5.1 4.2 2.7 5.0 3.6 8.0 63 63 0 8.8 3.9 3.8 5.1 4.3 4.7 4.8 5.8 5.0 7.2 4.4 3.7 2.9 6.3 64 64 1 7.7 2.2 6.3 4.5 2.4 4.7 3.4 6.2 6.0 4.7 3.3 3.1 2.6 6.0 65 65 1 6.6 3.6 5.8 4.1 4.9 4.7 4.8 7.2 6.5 3.9 3.5 3.6 2.8 5.4 66 66 1 5.7 3.8 3.5 6.7 5.4 5.7 6.0 8.2 5.4 5.0 4.7 7.3 5.2 7.6 67 67 0 5.7 4.0 7.9 6.4 2.7 5.5 5.1 6.2 7.5 6.4 5.0 6.2 4.5 6.4 68 68 0 5.5 3.7 4.7 5.4 4.3 5.3 4.9 6.0 5.6 2.5 4.5 5.9 4.3 6.1 69 69 1 7.5 3.5 3.8 3.5 2.9 4.1 4.5 7.6 5.1 5.2 4.0 5.4 3.4 5.2 70 70 1 6.4 3.6 2.7 5.3 3.9 3.9 5.3 7.1 5.2 5.5 4.7 6.6 3.9 6.6 71 71 0 9.1 4.5 6.1 5.9 6.3 5.3 7.1 8.4 7.1 5.7 5.4 6.1 4.4 7.6 72 72 0 6.7 3.2 3.0 3.7 4.8 6.3 4.5 5.0 5.2 2.5 2.9 2.6 3.1 5.8 73 73 1 6.5 4.3 2.7 6.6 6.5 6.3 6.0 8.7 4.7 6.3 4.6 5.6 4.6 7.9 74 74 1 9.9 3.7 7.5 4.7 5.6 7.0 6.7 6.8 7.2 4.6 4.1 3.4 3.9 8.6 75 75 1 8.5 3.9 5.3 5.5 5.0 4.9 6.0 6.8 5.7 3.6 4.4 5.1 3.7 8.2 76 76 0 9.9 3.0 6.8 5.0 5.4 5.9 4.8 4.9 7.3 7.6 3.1 4.3 3.8 7.1 77 77 0 7.6 3.6 7.6 4.6 4.7 4.6 5.0 7.4 8.1 6.6 4.5 5.8 3.9 6.4 78 78 0 9.4 3.8 7.0 6.2 4.7 6.5 4.9 8.5 7.3 2.4 4.3 4.5 4.1 7.6 79 79 0 9.3 3.5 6.3 7.6 5.5 7.5 5.9 4.6 6.6 3.1 5.2 4.1 4.6 8.9 80 80 1 7.1 3.4 4.9 4.1 4.0 5.0 5.9 7.8 6.1 3.5 2.6 3.1 2.7 5.7 81 81 1 9.9 3.0 7.4 4.8 4.0 5.9 4.8 4.9 5.9 6.9 3.2 4.3 3.8 7.1 82 82 0 8.7 3.2 6.4 4.9 2.4 6.8 4.6 6.8 6.3 5.1 4.3 3.7 4.0 7.4 83 83 0 8.6 2.9 5.8 3.9 2.9 5.6 4.0 6.3 6.1 4.0 2.7 3.0 3.0 6.6 84 84 0 6.4 3.2 6.7 3.6 2.2 2.9 5.0 8.4 7.3 6.5 2.0 3.7 1.6 5.0 85 85 0 7.7 2.6 6.7 6.6 1.9 7.2 4.3 5.9 6.5 4.1 4.7 3.9 4.3 8.2 86 86 1 7.5 3.5 4.1 4.5 3.5 4.1 4.5 7.6 4.9 2.8 3.4 5.4 3.4 5.2 5.2 87 87 0 5.0 3.6 1.3 3.0 3.5 4.2 4.9 8.2 4.3 7.6 2.4 4.8 3.1 88 88 0 7.7 2.6 8.0 6.7 3.5 7.2 4.3 5.9 6.9 7.7 5.1 3.9 4.3 8.2 89 89 0 9.1 3.6 5.5 5.4 4.2 6.2 4.6 8.3 6.5 4.1 4.6 4.3 3.9 7.3 8.2 90 90 0 5.5 5.5 7.7 7.0 5.6 5.7 8.2 6.3 7.4 4.9 5.5 6.7 4.9 91 91 0 9.1 3.7 7.0 4.1 4.4 6.3 5.4 7.3 7.5 4.6 4.4 3.0 3.3 7.4 92 92 0 7.1 4.2 4.1 2.6 2.1 3.3 4.5 9.9 5.5 3.5 2.0 4.0 2.4 4.8 93 93 1 9.2 3.9 4.6 5.3 4.2 8.4 4.8 7.1 6.2 6.6 4.4 2.6 4.2 7.6 SAS Output HBAT_Multiple Regression_Normal_Unpack-Plots_SAS_Output_Sp16.htm[3/30/2016 5:19:31 AM] 94 94 1 9.3 3.5 5.4 7.8 4.6 7.5 5.9 4.6 6.4 4.9 4.8 4.1 4.6 8.9 95 95 1 9.3 3.8 4.0 4.6 4.7 6.4 5.5 7.4 5.3 4.8 3.6 3.2 3.4 7.7 7.3 96 96 0 8.6 4.8 5.6 5.3 2.3 6.0 5.7 6.7 5.8 3.6 4.9 3.6 3.6 97 97 0 7.4 3.4 2.6 5.0 4.1 4.4 4.8 7.2 4.5 6.4 4.2 5.6 3.7 6.3 98 98 0 8.7 3.2 3.3 3.2 3.1 6.1 2.9 5.6 5.0 4.3 3.1 2.9 2.5 5.4 99 99 0 7.8 4.9 5.8 5.3 5.2 5.3 7.1 7.9 6.0 5.7 4.3 4.9 3.9 6.4 100 100 1 7.9 3.0 4.4 5.1 5.9 4.2 4.8 9.7 5.7 5.8 3.4 5.4 3.5 6.4 SAS Output The SAS System The CORR Procedure 14 Variables: X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 Simple Statistics Variable N Mean Std Dev Sum Minimum Maximum X6 100 7.81000 1.39628 781.00000 5.00000 10.00000 Label X7 100 3.67200 0.70052 367.20000 2.20000 5.70000 X7 - E-Commerce X8 100 5.36500 1.53046 536.50000 1.30000 8.50000 X8 - Technical Support X9 100 5.44200 1.20840 544.20000 2.60000 7.80000 X9 - Complaint Resolution X10 100 4.01000 1.12694 401.00000 1.90000 6.50000 X10 - Advertizing X11 100 5.80500 1.31529 580.50000 2.30000 8.40000 X11 - Product Line X12 100 5.12300 1.07232 512.30000 2.90000 8.20000 X12 - Salesforce Image X13 100 6.97400 1.54506 697.40000 3.70000 9.90000 X13 - Competitive Pricing X14 100 6.04300 0.81974 604.30000 4.10000 8.10000 X14 - Warranty & Claims X15 100 5.15000 1.49305 515.00000 1.70000 9.50000 X15 - New Products X16 100 4.27800 0.92884 427.80000 2.00000 6.70000 X16 - Order & Billing X17 100 4.61000 1.20600 461.00000 2.60000 7.30000 X17 - Price Flexibility X18 100 3.88600 0.73444 388.60000 1.60000 5.50000 X18 - Delivery Speed X19 100 6.91800 1.19184 691.80000 4.70000 9.90000 X19 - Satisfaction X6 - Product Quality Pearson Correlation Coefficients, N = 100 Prob > |r| under H0: Rho=0 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 1.00000 -0.13716 0.1736 0.09560 0.3441 0.10637 0.2922 -0.05347 0.5972 0.47749 <.0001 -0.15181 0.1316 -0.40128 <.0001 0.08831 0.3823 0.02699 0.7898 0.10430 0.3017 -0.49314 <.0001 0.02772 0.7843 0.48632 <.0001 -0.13716 0.1736 1.00000 0.00087 0.9932 0.14018 0.1642 0.42989 <.0001 -0.05269 0.6026 0.79154 <.0001 0.22946 0.0216 0.05190 0.6081 -0.02743 0.7865 0.15615 0.1208 0.27067 0.0065 0.19164 0.0561 0.28275 0.0044 X8 X8 - Technical Support 0.09560 0.3441 0.00087 0.9932 1.00000 0.09666 0.3387 -0.06287 0.5343 0.19263 0.0549 0.01699 0.8668 -0.27079 0.0064 0.79717 <.0001 -0.07358 0.4669 0.08010 0.4282 -0.18610 0.0638 0.02544 0.8016 0.11260 0.2647 X9 X9 - Complaint Resolution 0.10637 0.2922 0.14018 0.1642 0.09666 0.3387 1.00000 0.19692 0.0496 0.56142 <.0001 0.22975 0.0215 -0.12795 0.2046 0.14041 0.1635 0.05940 0.5572 0.75687 <.0001 0.39451 <.0001 0.86509 <.0001 0.60326 <.0001 -0.05347 0.5972 0.42989 <.0001 -0.06287 0.5343 0.19692 0.0496 1.00000 -0.01155 0.9092 0.54220 <.0001 0.13422 0.1831 0.01079 0.9151 0.08417 0.4051 0.18424 0.0665 0.33355 0.0007 0.27586 0.0055 0.30467 0.0021 0.47749 <.0001 -0.05269 0.6026 0.19263 0.0549 0.56142 <.0001 -0.01155 0.9092 1.00000 -0.06132 0.5445 -0.49495 <.0001 0.27308 0.0060 0.04616 0.6483 0.42441 <.0001 -0.37797 0.0001 0.60185 <.0001 0.55055 <.0001 X12 X12 - Salesforce Image -0.15181 0.1316 0.79154 <.0001 0.01699 0.8668 0.22975 0.0215 0.54220 <.0001 -0.06132 0.5445 1.00000 0.26460 0.0078 0.10746 0.2873 0.03164 0.7547 0.19513 0.0517 0.35224 0.0003 0.27155 0.0063 0.50021 <.0001 X13 X13 - Competitive Pricing -0.40128 <.0001 0.22946 0.0216 -0.27079 0.0064 -0.12795 0.2046 0.13422 0.1831 -0.49495 <.0001 0.26460 0.0078 1.00000 -0.24499 0.0140 0.02316 0.8191 -0.11457 0.2564 0.47111 <.0001 -0.07287 0.4712 -0.20830 0.0376 X14 X14 - Warranty & Claims 0.08831 0.3823 0.05190 0.6081 0.79717 <.0001 0.14041 0.1635 0.01079 0.9151 0.27308 0.0060 0.10746 0.2873 -0.24499 0.0140 1.00000 0.03520 0.7281 0.19707 0.0494 -0.17025 0.0904 0.10939 0.2786 0.17754 0.0772 X15 X15 - New Products 0.02699 0.7898 -0.02743 0.7865 -0.07358 0.4669 0.05940 0.5572 0.08417 0.4051 0.04616 0.6483 0.03164 0.7547 0.02316 0.8191 0.03520 0.7281 1.00000 0.06854 0.4980 0.09413 0.3516 0.10575 0.2950 0.07090 0.4833 X16 X16 - Order & Billing 0.10430 0.3017 0.15615 0.1208 0.08010 0.4282 0.75687 <.0001 0.18424 0.0665 0.42441 <.0001 0.19513 0.0517 -0.11457 0.2564 0.19707 0.0494 0.06854 0.4980 1.00000 0.40697 <.0001 0.75100 <.0001 0.52173 <.0001 X17 X17 - Price Flexibility -0.49314 <.0001 0.27067 0.0065 -0.18610 0.0638 0.39451 <.0001 0.33355 0.0007 -0.37797 0.0001 0.35224 0.0003 0.47111 <.0001 -0.17025 0.0904 0.09413 0.3516 0.40697 <.0001 1.00000 0.49669 <.0001 0.05595 0.5803 X18 X18 - Delivery Speed 0.02772 0.7843 0.19164 0.0561 0.02544 0.8016 0.86509 <.0001 0.27586 0.0055 0.60185 <.0001 0.27155 0.0063 -0.07287 0.4712 0.10939 0.2786 0.10575 0.2950 0.75100 <.0001 0.49669 <.0001 1.00000 0.57704 <.0001 X19 X19 - Satisfaction 0.48632 <.0001 0.28275 0.0044 0.11260 0.2647 0.60326 <.0001 0.30467 0.0021 0.55055 <.0001 0.50021 <.0001 -0.20830 0.0376 0.17754 0.0772 0.07090 0.4833 0.52173 <.0001 0.05595 0.5803 0.57704 <.0001 1.00000 X6 X6 - Product Quality X7 X7 - E-Commerce X10 X10 - Advertizing X11 X11 - Product Line HBAT_Multiple Regression_Normal_Unpack-Plots_SAS_Output_Sp16.htm[3/30/2016 5:19:31 AM] SAS Output HBAT_Multiple Regression_Normal_Unpack-Plots_SAS_Output_Sp16.htm[3/30/2016 5:19:31 AM] SAS Output The SAS System The REG Procedure Model: MODEL1 Dependent Variable: X19 X19 - Satisfaction Number of Observations Read 100 Number of Observations Used 100 Analysis of Variance DF Sum of Squares Mean Square F Value Pr > F Model 1 51.17801 51.17801 56.07 <.0001 Error 98 89.44959 0.91275 Corrected Total 99 140.62760 Source Root MSE 0.95538 R-Square 0.3639 Dependent Mean 6.91800 Adj R-Sq 0.3574 Coeff Var 13.81006 Parameter Estimates Variable Label DF Parameter Estimate Standard Error t Value Pr > |t| Standardized Estimate Tolerance Variance Inflation Intercept Intercept 1 3.68005 0.44285 8.31 <.0001 0 . 0 X9 X9 - Complaint Resolution 1 0.59499 0.07946 7.49 <.0001 0.60326 1.00000 1.00000 HBAT_Multiple Regression_Normal_Unpack-Plots_SAS_Output_Sp16.htm[3/30/2016 5:19:31 AM] SAS Output The SAS System The REG Procedure Model: MODEL1 Dependent Variable: X19 X19 - Satisfaction Output Statistics DFBETAS Obs Dependent Variable Predicted Value Std Error Mean Predict Residual Std Error Residual Student Residual Cook's D RStudent Hat Diag H Cov Ratio DFFITS Intercept 1 8.2000 7.1905 0.1022 1.0095 0.950 1.063 X9 |      |**    | 0.007 1.0635 0.0115 1.0089 0.1145 -0.0167 0.0407 2 5.7000 7.9640 0.1692 -2.2640 0.940 3 8.9000 7.0120 0.0964 1.8880 0.951 -2.408 |  ****|      | 0.094 -2.4697 0.0314 0.9328 -0.4445 0.3041 -0.3669 1.986 |      |***   | 0.020 2.0172 0.0102 0.9499 0.2045 0.0177 4 4.8000 5.8815 0.1682 -1.0815 0.0266 0.940 -1.150 |    **|      | 0.021 -1.1519 0.0310 1.0251 -0.2060 -0.1908 5 7.1000 6.4170 0.1166 0.1695 0.6830 0.948 0.720 |      |*     | 0.004 0.7185 0.0149 1.0252 0.0884 0.0651 -0.0507 6 4.7000 6.1195 7 5.7000 5.2270 0.1432 -1.4195 0.945 -1.503 |   ***|      | 0.026 -1.5126 0.0225 0.9966 -0.2293 -0.1997 0.1708 0.2452 0.4730 0.923 0.512 |      |*     | 0.009 0.5103 0.0659 1.0869 0.1355 0.1332 -0.1248 8 6.3000 9 7.0000 6.5360 0.1083 -0.2360 0.949 -0.249 |      |      | 0.000 -0.2474 0.0129 1.0327 -0.0282 -0.0184 0.0133 7.6665 0.1383 -0.6665 0.945 -0.705 |     *|      | 0.005 -0.7032 0.0209 1.0320 -0.1029 0.0573 -0.0744 10 11 5.5000 7.3095 0.1089 -1.8095 0.949 -1.906 |   ***|      | 0.024 -1.9329 0.0130 0.9589 -0.2218 0.0620 -0.1065 7.4000 6.5360 0.1083 0.8640 0.949 0.910 |      |*     | 0.005 0.9094 0.0129 1.0166 0.1038 0.0675 -0.0489 12 6.0000 6.0005 0.1554 -0.000520 0.943 -0.0006 |      |      | 0.000 -0.000549 0.0264 1.0485 -0.0001 -0.0001 0.0001 13 8.4000 7.7855 0.1502 0.6145 0.944 0.651 |      |*     | 0.005 0.6494 0.0247 1.0375 0.1034 -0.0637 0.0797 14 7.6000 6.9525 0.0956 0.6475 0.951 0.681 |      |*     | 0.002 0.6793 0.0100 1.0213 0.0684 0.0115 0.0033 15 8.0000 7.7855 0.1502 0.2145 0.944 0.227 |      |      | 0.001 0.2262 0.0247 1.0455 0.0360 -0.0222 0.0278 16 6.6000 7.7260 0.1441 -1.1260 0.944 -1.192 |    **|      | 0.017 -1.1948 0.0228 1.0144 -0.1823 0.1072 -0.1365 17 6.4000 7.2500 0.1053 -0.8500 0.950 -0.895 |     *|      | 0.005 -0.8942 0.0122 1.0165 -0.0992 0.0214 -0.0418 18 7.4000 7.9640 0.1692 -0.5640 0.940 -0.600 |     *|      | 0.006 -0.5979 0.0314 1.0461 -0.1076 0.0736 -0.0888 19 6.8000 7.4880 0.1222 -0.6880 0.948 -0.726 |     *|      | 0.004 -0.7243 0.0163 1.0266 -0.0934 0.0411 -0.0582 20 7.6000 7.4880 0.1222 0.1120 0.948 0.118 |      |      | 0.000 0.1176 0.0163 1.0374 0.0152 -0.0067 0.0094 21 5.4000 6.7740 0.0975 -1.3740 0.950 -1.446 |    **|      | 0.011 -1.4539 0.0104 0.9879 -0.1491 -0.0603 0.0294 22 9.9000 7.1905 0.1022 2.7095 0.950 2.852 |      |***** | 0.047 2.9635 0.0115 0.8682 0.3190 -0.0466 0.1135 23 7.0000 6.7145 0.0993 0.2855 0.950 0.300 |      |      | 0.000 0.2991 0.0108 1.0300 0.0313 0.0148 -0.0086 24 8.6000 7.9640 0.1692 0.6360 0.940 0.676 |      |*     | 0.007 0.6745 0.0314 1.0440 0.1214 -0.0831 0.1002 25 4.8000 6.4765 0.1123 -1.6765 0.949 -1.767 |   ***|      | 0.022 -1.7867 0.0138 0.9701 -0.2114 -0.1472 0.1110 26 6.6000 7.3095 0.1089 -0.7095 0.949 -0.748 |     *|      | 0.004 -0.7458 0.0130 1.0224 -0.0856 0.0239 -0.0411 27 6.3000 7.1310 0.0997 -0.8310 0.950 -0.875 |     *|      | 0.004 -0.8735 0.0109 1.0159 -0.0916 0.0066 -0.0262   -2-1 0 1 2 28 5.4000 6.9525 0.0956 -1.5525 0.951 -1.633 |   ***|      | 0.014 -1.6474 0.0100 0.9757 -0.1658 -0.0279 -0.0080 29 6.3000 7.2500 0.1053 -0.9500 0.950 -1.000 |    **|      | 0.006 -1.0005 0.0122 1.0123 -0.1110 0.0239 -0.0467 30 5.4000 6.0600 0.1492 -0.6600 0.944 -0.699 |     *|      | 0.006 -0.6976 0.0244 1.0358 ...
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