Question

In: Statistics and Probability

(Ch. 18 – Interaction Effects)  Below is Minitab output for a regression model using the Teen Gambling...

  1. (Ch. 18 – Interaction Effects)  Below is Minitab output for a regression model using the Teen Gambling data from Chapter 18.   Use it to answer the questions below.

Regression Analysis: Gambling Amount ($) versus Sex, Status, Sex:Status, Income ($100)

Model Summary

      S    R-sq  R-sq(adj)  

21.7798  56.39%     52.24%      

Coefficients

Term          Coef  SE Coef  T-Value  P-Value  

Constant      30.1     16.7     1.80    0.079

Sex          -68.2     20.1    -3.40    0.002  

Status      -0.481    0.269    -1.79    0.081

Sex:Status   1.088    0.460     2.37    0.023

Income ($)   4.970    0.984     5.05    0.000

Regression Equation

Gambling Amount($) = 30.1 - 68.2 Sex - 0.481 Status + 1.088 Sex:Status + 4.970 Income

  1. Interpret the slope of the Sex:Status (interaction) predictor for a female teenager. (Remember to account for the slope of Status.)
  2. Interpret the slope of the Status predictor for a male teenager.

Solutions

Expert Solution


Related Solutions

EXERCISES FOR CHAPTERS 18 – 19 (Ch. 18 – Interaction Effects)  Below is Minitab output for a...
EXERCISES FOR CHAPTERS 18 – 19 (Ch. 18 – Interaction Effects)  Below is Minitab output for a regression model using the Teen Gambling data from Chapter 18.   Use it to answer the questions below. Regression Analysis: Gambling Amount ($) versus Sex, Status, Sex:Status, Income ($100) Model Summary       S    R-sq  R-sq(adj)   21.7798  56.39%     52.24%       Coefficients Term          Coef  SE Coef  T-Value  P-Value   Constant      30.1     16.7     1.80    0.079 Sex          -68.2     20.1    -3.40    0.002   Status      -0.481    0.269    -1.79    0.081 Sex:Status   1.088    0.460     2.37    0.023 Income ($)   4.970    0.984     5.05    0.000 Regression Equation Gambling Amount($) = 30.1 - 68.2 Sex - 0.481 Status + 1.088 Sex:Status + 4.970 Income What is the predicted Gambling Amount...
These problems may be solved using Minitab. Copy and paste the appropriate Minitab output into a...
These problems may be solved using Minitab. Copy and paste the appropriate Minitab output into a word-processed file. Add your explanations of the output near the Minitab output. DO NOT SIMPLY ATTACH PAGES OF OUTPUT AS AN APPENDIX. Each problem should be able to fit on one or two pages, and each problem should include the following: Minitab output for the ANOVA. Written statement interpreting the ANOVA. Four-in-one plot of the residuals. Written interpretation as to whether the three assumptions...
Using Minitab, use the regression model with all nine independent variables to test the hy- potheses...
Using Minitab, use the regression model with all nine independent variables to test the hy- potheses H0 : βAGE = −2500 vs. Ha : βAGE < −2500. Use α = 0.05 and include all steps of a hypothesis test. Row   PRICE   BATHS   BEDA   BEDB   BEDC   CARA   CARB   AGE   LOT   DOM   1   25750   1.0   1   0   0   1   0   23   9680   164   2   37950   1.0   0   1   0   0   1   7   1889   67   3   46450   2.5   0   1   0   0   0  ...
The multiple regression model is estimated in Excel and part of the output is provided below....
The multiple regression model is estimated in Excel and part of the output is provided below. ANOVA df SS MS F Significance F Regression 3 3.39E+08 1.13E+08 1.327997 0.27152899 Residual 76 6.46E+09 85052151 Total 79 6.8E+09 Question 8 (1 point) Use the information from the ANOVA table to complete the following statement. To test the overall significance of this estimated regression model, the hypotheses would state there is    between attendance and the group of all explanatory variables, jointly. there is...
Present the regression output below noting the coefficients, assessing the adequacy of the model and the...
Present the regression output below noting the coefficients, assessing the adequacy of the model and the p-value of the model and the coefficients individually. SUMMARY OUTPUT Regression Statistics Multiple R 0.2967345 R Square 0.088051364 Adjusted R Square 0.08408637 Standard Error 11.78856107 Observations 694 ANOVA df SS MS F Significance F Regression 3 9258.409674 3086.136558 22.2071867 9.78014E-14 Residual 690 95889.41876 138.9701721 Total 693 105147.8284 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 34.16092365 1.25201462...
The summary output of Paradise Retreats' model in Excel is in the tables below: Regression Statistics...
The summary output of Paradise Retreats' model in Excel is in the tables below: Regression Statistics Multiple R 0.764437898 R Square 0.5843653 Adjusted R Square 0.555700838 Standard Error 315.8931794 Observations 60 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 413 317.1379944 0.110883586 0.912472524 -613.4546286 683.785425 x1 15 7.187801286 1.304664049 0.202266328 -5.323038326 24.07837018 x2 5 9.990263453 3.509123492 0.001488311 14.62468522 55.48945112 According to the tables, which of the following statements about this regression model are true? Select all correct...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be .10 Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 8.3323 .1570 (8.0107, 8.6539) (6.7456, 9.9189) 2 8.3601 .142 (8.0689, 8.6512) (6.7793, 9.9408) (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all sales periods when the price...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be .10 Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 8.4972 .1734 (8.1421, 8.8523) (6.9576, 6.9576) 2 8.4425 .134 (8.1690, 8.7160) (6.9197, 6.9197) (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all sales periods when the price...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be .10 Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 8.6116 .1505 (8.3032, 8.9200) (7.1346, 10.0887) 2 8.4946 .129 (8.2308, 8.7583) (7.0262, 9.9629) (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all sales periods when the price...
The R output below gives the summary of the multiple regression model for birth weight based...
The R output below gives the summary of the multiple regression model for birth weight based on both gestation length and smoking status: lm(formula = Weight ~ Weeks + SmokingStatus, data = births) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1724.42 558.84 -3.086 0.00265 ** Weeks 130.05 14.52 8.957 2.39e-14 *** SmokingStatusSmoker -294.40 135.78 -2.168 0.03260 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 484.6 on 97 degrees...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT