Estimate a multiple regression model that answers your research question. Post your response to the following: What is your research question? Interpret the coefficients for the model, specifically commenting on the dummy variable. Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?
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For example, "What is the impact of education level, work experience, and gender on annual income?" Show more…
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1. Explain the multiple linear regression model, the independent variables and the dependent variable, assumptions of the model, objectives, and the approach taken to construct the model, and using the model for prediction. 2. Explain the analysis of variance (ANOVA) test on the significance of the regression and how the result of this test is interpreted; discuss the hypotheses on coefficients of the regression and how the results of testing these hypotheses are interpreted about the significance of these coefficients; include both unidirectional and bidirectional situations the coefficient of determination, adjusted coefficient of determination, and their significance. 3. Describe how multicollinearity can have adverse effects in constructing the regression model, how it is identified, and how the normality of residuals is verified.
Sri K.
In a regression analysis involving 30 observations, the following estimated regression equation was obtained: y-hat = 17.6 + 3.8x1 - 2.3x2 + 7.6x3 + 2.7x4. For this estimated regression equation SST = 1805 and SSR = 1760. a. At α = 0.05, test the significance of the relationship among the variables. Suppose variables x1 and x4 are dropped from the model and the following estimated regression equation is obtained: y-hat = 11.1 - 3.6x2 + 8.1x3. For this model SST = 1805 and SSR = 1705. b. Compute SSE(x1, x2, x3, x4). c. Compute SSE(x2, x3). d. Use an F test and a 0.05 level of significance to determine whether x1 and x4 contribute significantly to the model. Hints: Recall the ANOVA table for multiple regression. Test the overall significance of the relationship by using the F-test.
Madhur L.
Using statistical software, run the same multiple linear regression model as in Part 2 above but this time using Operating Margin New as the response variable. Then, answer the following questions: a) Briefly compare the resulting regression equation and fit with those obtained in Part 2. And then Plot the residuals against the fitted values and comment on whether the model complies with the usual conditions for multiple linear regression. b) Provide an interpretation for the model intercept and for the regression coefficients associated with variables Income and Distance. Is an interpretation of the model intercept appropriate in this case? Compare the value of the regression coefficient for Distance with the one obtained in Part 1 above and clearly explain any difference. Do you see any justification for dropping any variable(s) from the model? Explain (hint: multicollinearity; the significance of predictors). c) Run a final model using Operating Margin New as the response variable and including only the significant predictors (hint: those with a p-value ≤ 5%). d) Test the overall significance of the final model in part e). Use a 1% significance level and follow all the steps for hypothesis testing indicated in the Instructions section.
Dominador T.
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