MLR Solutions

MLR Solutions - Distribution of individual variables –...

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Unformatted text preview: Distribution of individual variables – Step 1 Students 10 20 30 40 50 60 70 80 AK CT DC NJ NY .01 .05 .10 .25 .50 .75 .90 .95 .99-3-2-1 1 2 3 Normal Quantile Plot Fitted Normal Parameter Estimates Type Parameter Estimate Lower 95% Upper 95% Location μ 34.25 27.536844 40.963156 Dispersion σ 24.113193 20.208016 29.903419 Goodness-of-Fit Test Shapiro-Wilk W Test W Prob<W 0.867726 <.0001 Note: Ho = The data is from the Normal distribution. Small p-values reject Ho. Pay 20 25 30 35 40 45 AK CT DC NJ NY .01 .05 .10 .25 .50 .75 .90 .95 .99-3-2-1 1 2 3 Normal Quantile Plot Fitted Normal Parameter Estimates Type Parameter Estimate Lower 95% Upper 95% Location μ 31.311538 29.837326 32.785751 Dispersion σ 5.2952681 4.4376894 6.5668044 Goodness-of-Fit Test Shapiro-Wilk W Test W Prob<W 0.962394 0.0994 Note: Ho = The data is from the Normal distribution. Small p-values reject Ho. Spending 2000 3000 4000 5000 6000 7000 8000 9000 10000 AK CT DC NY NJ AK CT DC NJ NY .01 .05 .10 .25 .50 .75 .90 .95 .99-3-2-1 1 2 3 Normal Quantile Plot Fitted Normal Parameter Estimates Type Parameter Estimate Lower 95% Upper 95% Location μ 5181.3462 4801.8665 5560.8258 Dispersion σ 1363.0647 1142.3138 1690.3732 Goodness-of-Fit Test Shapiro-Wilk W Test W Prob<W 0.922798 0.0024 Note: Ho = The data is from the Normal distribution. Small p-values reject Ho. SAT 800 850 900 950 1000 1050 1100 AK CT DC NJ NY .01 .05 .10 .25 .50 .75 .90 .95 .99-3-2-1 1 2 3 Normal Quantile Plot Fitted Normal Parameter Estimates Type Parameter Estimate Lower 95% Upper 95% Location μ 945.54902 927.33264 963.7654 Dispersion σ 64.768299 54.192224 80.512058 Goodness-of-Fit Test Shapiro-Wilk W Test W Prob<W 0.962633 0.1080 Note: Ho = The data is from the Normal distribution. Small p-values reject Ho. Multivariate Correlation Plot - Step 2 Correlations SAT Students Pay Spending SAT 1.0000 -0.8686 -0.5182 -0.5092 Students -0.8686 1.0000 0.6593 0.7110 Pay -0.5182 0.6593 1.0000 0.8442 Spending -0.5092 0.7110 0.8442 1.0000 Scatterplot Matrix 800 850 900 950 1000 1050 20 40 60 20 25 30 35 40 2000 4000 6000 8000 SAT AK CT DC NJ NY AK CT DC NJ NY AK CT DC NJ NY 800 900 1000 AK CT DC NJ NY Students AK CT DC NJ NY AK CT DC NJ NY 10 30 50 70 AK CT DC NJ NY AK CT DC NJ NY Pay AK CT DC NJ NY 20 25 30 35 40 AK CT DC NJ NY AK CT DC NJ NY AK CT DC NJ NY Spending 2000 5000 8000 SLR models - SAT by Students, SAT by Pay, and SAT by Spending - Step 3 Bivariate Fit of SAT By Students 800 850 900 950 1000 1050 1100 SAT 10 20 30 40 50 60 70 80 Students Fit Mean Linear Fit Linear Fit SAT = 1024.4067 - 2.3368623*Students Summary of Fit RSquare 0.754458 RSquare Adj 0.749447 Root Mean Square Error 32.41996 Mean of Response 945.549 Observations (or Sum Wgts) 51 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 158244.98 158245 150.5584 Error 49 51501.64 1051 Prob > F C. Total 50 209746.63 <.0001 Parameter Estimates Term Estimate Std Error t Ratio Prob>|t| Intercept 1024.4067 7.868418 130.19 130....
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This note was uploaded on 04/13/2008 for the course BIT 2406 taught by Professor Llclark during the Spring '07 term at Virginia Tech.

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MLR Solutions - Distribution of individual variables –...

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