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- 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.In a regression problem with 1 output variable and with a total number of 100 possible input variables, what is the number of all possible models with three input variables?If the t ratio for the slope of a simple linear regression equation is equal to 1.614 and the critical values of the t distribution at the 1% and 5% levels of significance, respectively, are 3.499 and 2.365, then the slope is not significantly different from zero. significantly different from zero at both the 1% and the 5% levels. significantly different from zero at the 1% level but not at the 5% level. significantly different from zero at the 5% level but not at the 1% level. please show how you come up with the answer , do not just guess !!!
- Suppose that an economist has been able to gather data on the relationship between demand and price for a particular product. After analyzing scatterplots and using economic theory, the economist decides to estimate an equation of the form Q= aPb, where Q is quantity demanded and P is price. An appropriate regression analysis is then performed, and the estimated parameters turn out to be a = 1000 and b = - 1.3. Now consider two scenarios: (1) the price increases from $10 to $12.50; (2) the price increases from $20 to $25. a. Do you predict the percentage decrease in demand to be the same in scenario 1 as in scenario 2? Why or why not? b. What is the predicted percentage decrease in demand in scenario 1? What about scenario 2? Be as exact as possible.Given the following table, use the matrix method to derive the constant and slope parametersof the sample regression function: Module test mark = f (Weekly study hours). X and Y standfor weekly study hours and module test mark respectively.X (Weekly study hours) Y (Module test mark)1 402 403 454 605 706 75Using the data below, what is the slope coefficient from a regression of Quantity on Price? It is probably easiest to use either Gretl or Jamovi as in the powerpoint example. (round your answer to the nearest 0.1, and it can be either positive or negative.) Quantity 17 24 23 39 55 54 60 76 71 80 Price 93 77 62 64 47 35 38 12 29 2
- If a regression equation contains an irrelevant variable, the parameter estimates will be Select one: a. Consistent and unbiased but inefficient b. Consistent and asymptotically efficient but biased c. Consistent, unbiased and efficient. d. InconsistentABC, 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.A 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?
- Interpret the slope (m) and R2 values given in the equation of linear regression for both costs and benefits.Q5. Show that µY = Yµ − µY · 1. Data Mining Regression Evaluation chapterThe regression equation to predict sales based on temperature is: Predicted sales = -2419.01+ 98.02 (temperature). A correct interpretation of the slope would be that 1. as temperature goes up by 1 degree, sales are predicted to go down by 2419.01. 2. as temperature goes down by 1 degree, sales are predicted to go up by 2419.01. 3. as temperature goes up by 1 degree, sales are predicted to go down by 98.02. 4. as temperature goes up by 1 degree, sales are predicted to go up by 98.02. 5. None of the answer choices provides a correct interpretation of the slope.