1 / 6

Analysis - 1 st with Minitab

Analysis - 1 st with Minitab. Regression Analysis: Capacity versus Age, Height, ... The regression equation is Capacity = - 6.17 - 0.0140 Age + 0.149 Height + 0.00636 Weight - 0.0087 Chest - 0.0220 Waist + 0.343 Activity - 0.109 Smoke - 0.409 Gender

rdawn
Download Presentation

Analysis - 1 st with Minitab

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Analysis - 1st with Minitab Regression Analysis: Capacity versus Age, Height, ... The regression equation is Capacity = - 6.17 - 0.0140 Age + 0.149 Height + 0.00636 Weight - 0.0087 Chest - 0.0220 Waist + 0.343 Activity - 0.109 Smoke - 0.409 Gender 40 cases used 1 cases contain missing values Predictor Coef SE Coef T P Constant -6.172 2.653 -2.33 0.027 Age -0.014032 0.007000 -2.00 0.054 Height 0.14856 0.03503 4.24 0.000 Weight 0.006359 0.006094 1.04 0.305 Chest -0.00867 0.05791 -0.15 0.882 Waist -0.02197 0.04557 -0.48 0.633 Activity 0.3427 0.1282 2.67 0.012 Smoke -0.1092 0.1491 -0.73 0.469 Gender -0.4086 0.2757 -1.48 0.148 S = 0.4607 R-Sq = 84.3% R-Sq(adj) = 80.2%

  2. Analysis - 2nd with Minitab Notice that the P-values on the right suggest that only the predictors height (P-value = 0.000) and activity level (P-value = 0.012) are significant at the 0.05 level of significance. The only other variable that seem possibly significant are age (P-value = 0.054 and gender (P-value =0.148). When stepwise regression techniques are applied using Minitab, the variables that remain significant are height, activity level, age and gender. The output is on the next two slides.

  3. Analysis - 2nd with Minitab Stepwise Regression: Capacity versus Age, Height, ... Alpha-to-Enter: 0.1 Alpha-to-Remove: 0.1 Response is Capacity on 8 predictors, with N = 40 N(cases with missing observations) = 1 N(all cases) = 41 Step 1 2 3 4 Constant -10.251 -9.759 -9.787 -6.929 Height 0.209 0.191 0.198 0.161 T-Value 10.42 9.87 10.43 6.55 P-Value 0.000 0.000 0.000 0.000 Activity 0.35 0.31 0.30 T-Value 2.87 2.60 2.67 P-Value 0.007 0.013 0.011

  4. Analysis - 2nd with Minitab Activity 0.35 0.31 0.30 T-Value 2.87 2.60 2.67 P-Value 0.007 0.013 0.011 Age -0.0109 -0.0137 T-Value -1.96 -2.54 P-Value 0.057 0.016 Gender -0.47 T-Value -2.24 P-Value 0.032 S 0.534 0.490 0.472 0.448 R-Sq 74.06 78.78 80.84 83.23 R-Sq(adj) 73.38 77.63 79.24 81.32 C-p 15.1 7.8 5.8 3.0

  5. Analysis - 2nd with Minitab The resulting Minitab output from the regression analysis using those 4 predictors follows. Regression Analysis: Capacity versus Height, Activity, Gender, Age The regression equation is Capacity = - 6.93 + 0.161 Height + 0.302 Activity - 0.466 Gender - 0.0137 Age 40 cases used 1 cases contain missing values Predictor Coef SE Coef T P Constant -6.929 1.708 -4.06 0.000 Height 0.16079 0.02454 6.55 0.000 Activity 0.3025 0.1133 2.67 0.011 Gender -0.4658 0.2082 -2.24 0.032 Age -0.013744 0.005404 -2.54 0.016 S = 0.4477 R-Sq = 83.2% R-Sq(adj) = 81.3%

  6. Analysis - 2nd with Minitab Consider the following graphs: residuals vs fits and the normal plot of the residual.

More Related