Question

-+ Ft to page Page view 2. A doctor wanted to determine whether there is a Age versus HDL Cholesterol relation between a male's age and his HDL (so-called good) cholesterol. He randomly selected 17 of his patients and determined their HDL cholesterol. He obtained the following data: (Use Minitab only for graphs. Show all the steps) HDL HDL Age, x Cholesterol,y Age, x Cholesterol, y 38 42 46 32 57 54 34 56 35 40 42 38 47 38 62 53 36 45 38 4955 28 30 51 27 52 52 61 61 26 39 (a) Draw a scatter diagram of the data, treating age as the explanatory variable. What type of relation, if any, appears to exist between age and HDL cholesterol? (5 points) (b) Determine the least-squares regression equation from the sample data. (10 points) (c) Plot the residuals against the explanatory variable, age. Does a linear model seem appropriate, on the basis of the residual plot? (5 points) (d) Compute the standard error of the estimate. (10 points) (e) Determine whether the residuals are normally distributed. (5 points) (f) If the residuals are normally distributed, determine sbı. (5 points) (g) If the residuals are normally distributed, test the claim that a linear relation exists between age and HDL cholesterol levels at the a 0.01 level of significance. (10 points) (h) If the residuals are normally distributed, construct a 95% confidence interval about the slope of the true least-square regression line. (5 points) (0) Suppose a 42-year-old male patient visits the doctor's office. Would you recommend using the least-squares regression line obtained in part (b) to predict the HDL cholesterol of this patient? Why? What would be a good guess as to the HDL cholesterol of this patient? (5 points)

Answer 1

Home Insert Page Layout Formulas Data Review View show Detail | Hide Detall Data Analy Properties From From From From Other Existing Refresh Access Web Text Sources Connections AllEdit Links Reapply Advanced sort l Filter シ Text to Remove Columns Duplicates Validation Data Consolidate What-I1 Group Ungroup Subtotal Analysis Get External Data Sort & Filter Data Tools SUMMARY OUTPUT x Residual Plot Statistics 4 Multiple R 5 RSquare 6 Adjusted R Square 0.163664317 0.026786009 0.038094924 9.953359624 17 40 20 Standard Error 0 8 Observations 20 -20 10 ANOVA SSMSF gnficance 1 40.90066 40.90066 0.412849 0.530226 15 1486.041 99.06937 16 1526.941 Normal Probability Plot 12 Regression 13 Residual 14 Total 80 60 40 p-value Lower 95%Upper 959 ower 95.00 pper 95.0% 7.4E-05 30.7302 70.83806 30.7302 70.83806 coefficients andard Err t stat 16 17 Intercept 18 x 19 50.78412783 9.408589 5.397635 0.129843364 0.20208 -0,64253 0.530226 -0.56057 0.300881 -0.56057 0.300881 020406080 100 120 Sample Percentile 21 PROBABILITY OUTPUT 22 RESIDUAL OUTPUT Percentile Predicted y Residuals 1 45.85008002 11.14992 Observation 2.941176 28 het Sheet2 Sheets sheet Sheeto -Sheet10 Sheetli_ Sheet13-Sheetí 4-SI etí 5. She ti 200 O Type here to search

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