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
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q23-
What is the aspect of sound regression analysis?
Keep trying different models until statistical significance is achieved
Reporting only the results that are statistically significant
Statistical significance solely based on "p-value < 0.05"
Economic plausibility and significance of the model
Step by step
Solved in 3 steps
- q22- 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 significantQ5. 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?
- 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?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 details
- Question 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.Economics: Industrial Economics Since only 3 questions allowed = Please Answer Parts D. - F. (and include answers to the NOTE PART: sub-questions a.; b.; c. In order to solve the Parts D. - F. questions). Thanks QUESTION: You work for the manager of a small single-product firm. Assume that you have data for your firm on costs, capital rental rate, wages and output. You realize through regression analysis that the data fits very well the following equation: ln(C) = -1.18 + 0.33 ln(r) + 0.67 ln(w) + 0.81 ln(q) D. Explain why its very likely that you misspecified the production technology. To account for the possibility of having misspecified the production function, you run a more general regression using the data for all firms. The data fits the following equation: ln(C) = 1 + 0.33 ln(r) + 0.67 ln(w) + 0.3 ln(q) + 0.03[ln(q)]^2 E.Assuming that w = 25 and r = 500, complete the following table (in excel): q ln(q) ln(C) C AC MC 1,000 2,000 5,000 10,000 20,000 50,000 100,000 200,000…Write (TRUE/FALSE) for each question. An observation with a large standardized residual value always generates a large Cook’sdistance value. (T/F)Leverage value detects unusual x values. (T/F)BIC gives heavier penalty on models with many variables than Cp or AIC. (T/F) As the tuning parameter λ → ∞, the coefficients of ridge regression tend to zero. (T/F)Like the least square coefficient, ridge regression coefficients are scale equivalent. (T/F)The shrinkage penalty is applied to all coefficient except for the intercept. (T/F) Lasso regression reduces the bias by increasing the tuning parameter. (T/F)
- 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.q9- Which property of linear regression is related with the size effects of individual units in a cross-section data? Select one: a. Heteroskedasticity b. Endogeneity c. Autocorrelation d. Non-normality Clear my choiceABC, Inc., sells tea products to various customers. In recent years, profits have been declining. The CFO of the company investigated the reasons for the profit decline and performed regression analysis for sales and costs. She determined that sales depend on product price, delivery speed, customer services, and marketing expenses. She also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales. List two advantages and two limitations of regression analysis.