Are boys, on average, heavier than girls at birth, all other factors being equal? If so, by how much? Is the di§erence between boys and girls estimated from this regression statistically significant at the 5% level?
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Are boys, on average, heavier than girls at birth, all other factors being
equal? If so, by how much? Is the di§erence between boys and girls
estimated from this regression statistically significant at the 5% level?
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- In multiple linear regression, the Variance Inflation Factor (VIF) measures multicollinearity among the X-variables. What is multicollinearity, and why do we want to avoid it?Two new variables, the market value of the firm (a measure of firm size, in millions of dollars) and stock return (a measure of firm performance, in percentage points), are added to the regression: In(Earnings) = 3.86 – 0.28Female + 0.37In(MarketValue) + 0.004Return, (0.03) (0.04) (0.004) (0.003) n = 46,670, R = 0.345. If MarketValue increases by 1.88%, what is the increase in earnings? If MarketValue increases by 1.88%, earnings increase by 0.70 % (Round your response to two decimal places.) The coefficient on Female is now – 0.28. Why has it changed from the first regression? O A. Female is correlated with the two new included variables. O B. MarketValue is important for explaining In(Earnings). O C. The first regression suffered from omitted variable bias. OD. All of the above. Assume that the coefficient estimated in the second regression is correct. Forget about the effect of the Return variable, whose effect seems small and statistically insignificant. Calculate the correlation…Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft.The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained:The regression equation is ????? = ? + ???????? + ?????? ???? + ???? ????Predictor Coef SE Coef T PConstant 37718 14177 2.66 **Bedrooms 2306 6994 0.33 0.742House Size 74.3 52.98 * 0.164Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq(adj)=54.6% Source DF SS MS F PRegression 3 76501718347 25500572782 *** ****Residual…
- A vocational counselor uses the number of days without employment to predict her clients' feelings of self efficacy, measured on a scale of 1 to 5, with higher numbers meaning that clients feel more secure in their job related abilities. The slope of the regression line is –1.02. Which statement is the best interpretation for this finding? a. For every 1-point increase in self-efficacy, there is an associated decrease in the number of days of unemployment. b. For every additional day of unemployment, there is an associated decrease in self-efficacy of 1.02 points. c. The least number of days a person can be unemployed and still feel self-efficacious is 3.98 points d. The decrease in self-efficacy of 1.02 points is caused by each additional day of unemploymentWhich statement is not correct for multiple regression model? When we interpret this categorical variable, we would say the change we observe when one switches from the reference category to the specific category. The reference category will not be displayed in our model results. If we include a categorical variable with more than two values (e.g., religion) as an independent variable, we want to include a dummy variable for all categories except for two. We can include more than two categorical variables in the model. The value of the independent variable not included in the model is called "reference category."For linear regression with one variable, the unpredicted portion of the Y-score variance (MS residual) has df = n - 2. True False Submit Answer
- You are analyzing a dataset containing 379 datapoints, and want to use 13 predictor variables to create a multiple variable linear regression model of the data. You conduct an ANOVA analysis, and yield a R² of 38%. Using this information, what would be the F statistic of your analysis?Exercise 6. Regression Fallacy. Historically, scores on the two midterms had a correlation of 0.48. Suppose that Jeri scored 2.1 standard deviations below the mean on the first midterm. (a) How many standard deviations [above or below?] the mean would you predict for her second midterm?10
- Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained:The regression equation is ????? = ? + ???????? + ?????? ???? + ???? ????Predictor Coef SE Coef T PConstant 37718 14177 2.66 **Bedrooms 2306 6994 0.33 0.742House Size 74.3 52.98 * 0.164Lot Size -4.36 17.02 -0.26 0.798S= 25023 R-Sq=56.0% R-Sq(adj)=54.6%Source DF SS MS F PRegression 3 76501718347 25500572782 *** ****Residual Error 96 60109046053 626135896Total 99a) Write out the regression equation. b) Fill in the missing values *, **, *** and ****. c) Use the p-value approach to determine if ? is significant at the 5% significance leveld) Is ? significantly different from -0.5? e) Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. f) Give an interpretation to the term “R-sq” and…Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did…A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. a. Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is 8.6 cars. b. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is 0.234 Therefore, about _________% of the variation in monthly sales is explained by the years of sales experience. (Type an integer or decimal rounded to one decimal place as needed.)