In least squares regression, which of the following is not a required assumption about the error term $\epsilon$?

Related MCQs

• This question is related to questions 4 and 21 above. The relationship between number of beers consumed (x) and blood alcohol content (y) was studied in 16 male college students by using least squares regression. The following regression equation was obtained from this study: $$$ \hat{y} = -0.0127 + 0.0180x$$ Another guy, his name Dudley, has the regression equation written on a scrap of paper in his pocket. Dudley goes out drinking and has 4 beers. He calculates that he is under the legal limit (0.08) so he decides to drive to another bar. Unfortunately Dudley gets pulled over and confidently submits to a road-side blood alcohol test. He scores a blood alcohol of 0.085 and gets himself arrested. Obviously, Dudley skipped the lecture about residual variation. Dudley’s residual is:
• The relationship between number of beers consumed (x) and blood alcohol content (y) was studied in 16 male college students by using least squares regression. The following regression equation was obtained from this study: $$$\hat{y} = -0.0127 + 0.0180x$$ The above equation implies that:
• The data are the same as for question 4 above. The relationship between number of beers consumed (x) and blood alcohol content (y) was studied in 16 male college students by using least squares regression. The following regression equation was obtained from this study: $$$ \hat{y} = -0.0127 + 0.0180x$$ Suppose that the legal limit to drive is a blood alcohol content of 0.08. If Ricky consumed 5 beers the model would predict that he would be:
• Regression analysis was applied to return rates of sparrowhawk colonies. Regression analysis was used to study the relationship between return rate (x: % of birds that return to the colony in a given year) and immigration rate (y: % of new adults that join the colony per year). The following regression equation was obtained. $$$ \hat{y} = 31.9 - 0.34x$$ Based on the above estimated regression equation, if the return rate were to decrease by 10% the rate of immigration to the colony would:
• A fitted least squares regression line
• Suppose that you have carried out a regression analysis where the total variance in the response is 133452 and the correlation coefficient was 0.85. The residual sums of squares is:
• Regression analysis was applied between $ sales (y) and $ advertising (x) across all the branches of a major international corporation. The following regression function was obtained. $$$ \hat{y} = 5000 + 7.25x$$ If the advertising budgets of two branches of the corporation differ by $30,000, then what will be the predicted difference in their sales?
• You have carried out a regression analysis; but, after thinking about the relationship between variables, you have decided you must swap the explanatory and the response variables. After refitting the regression model to the data you expect that:
• A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation: $$$ \hat{y} = 50,000 - 8X$$ The above equation implies that an
• In regression, the equation that describes how the response variable (y) is related to the explanatory variable (x) is: