Which of the following types of regressions will always have a binary outcome variable? (A) Probit (B) Difference-in-differences (C) Regression discontinuity (D) (A) and (B) will both have binary outcome variables
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2. Which of the following types of regressions will always have a binary outcome variable?
(A) Probit
(B) Difference-in-differences
(C) Regression discontinuity
(D) (A) and (B) will both have binary outcome variables
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- 18. A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: D. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?Q5. Show that µY = Yµ − µY · 1. Data Mining Regression Evaluation chapterA multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: a. Are the estimates of a, b, c, and d statistically significant at the 1 percent significance level? b. How much of the total variation is explained by this regression equation? c. Is the overall regression equation statistically significant at the 1 percent level of significance? d. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?
- q23- What is the aspect of sound regression analysis? Select one: a. Keep trying different models until statistical significance is achieved b. Reporting only the results that are statistically significant c. Statistical significance solely based on "p-value < 0.05" d. Economic plausibility and significance of the model Clear my choice4) identify of intercept (B0), slope of regression line (B1) , and the p value and analysis of their meaningsQuestion 15 When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.
- 4. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). 5. Re-write the specified model in (a) with values from the regression results and interpret the coefficients.What does the value for the coefficient mean in the regression analysis of both the company ? P-Values - how its related to customer satisfaction in the regression analysis of both the company ?a)What is multicollinearity?Discuss causes and consequences of multicollinearity for OLS estimation. Suggest possible remedial measures.b)Suppose you are estimating parameters of the following regression model: Ŷt= 9941 + 0.25 X2t+ 15125 X3t (6114) (0.121) (7349) R2= 0.87, RSS = 10310 (The figures in parentheses are the estimated standard errors. RSS are residual sum of squares.) (i) Comment on the explanatory power of the regression. (ii)Using t-tests show whether individual coefficients are significantly different from zero at 5% level of significance. (iii)Test whether the coefficient of X2issignificantly different from 1 at 5% level of significance .(iv)Carry out an appropriate test to check ifcoefficients are jointly significant.
- what are the key features , Strength and limitation of following model? and when which model should be used? Ordinary Least Squares Logit regression model Probit regression modelPerform necessary tests and analysis to determine the validity of the regression attached in the below table and function provided The tests and analysis must be broken down into two sections namely: tests that are possible with the given regression’s information and tests that should be conducted but are not possible with the given information. For the tests that are possible please conduct them at a 5% significance level and for those that are not possible only mention their names without any further detailsq22- What is NOT the aspect of sound regression analysis? Select one: a. The effect size that matters economically b. Economic plausibility and significance of the model c. Satisfactory predictive ability of the model d. Reporting only the results that are statistically significant