Homework Answers
We need to test if the number of years of education is significantly related to the number of children. Hence,
Our hypotheses will be:
Ho: 
= 0
H1: 
Option E is correct.
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linear regression to examine this question the output from shown beow E n Stotishics...
HELP ASAP The following is Excel output from a fitted linear regression model relating the sale price of a home (y...
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The following is Excel output from a fitted linear regression model relating the sale price of a home (y in thousands of dollars) to age of the home in years. Intercept Age Coefficients 213.365436 -1.207517218 Standard Errort Stat P value 1.450657792 147.0819 0 0.02997096940.2897 1.28 279 Lower 95% Upper 95% 210.5209309 216.2099011 1.26620524 1160749195 Refer to the information above on the regression using age to predict selling price of houses. Which of the following is the correct way...
QUESTION 12 Below is some of the regression output from a regression of the amount various...
QUESTION 12 Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with...
The following output is a regression of hourly EARNINGS on HEIGHT. a. Create a 99% confidence...
The following output is a regression of hourly EARNINGS on HEIGHT. a. Create a 99% confidence interval for the slope coefficient B1 and interpret it. b. Is the slope coefficient on HEIGHT statistically significant at a level of significance of 1%? c. Years of schooling S also affects EARNINGS but has not been added to the regression. Where does the effect of Soccur in this regression output? SUMMARY OUTPUT Regression Statistics Multiple R 0.241124399 R Square 0.058140976 Adjusted R Square...
A real estate agent wants to use a multiple regression model to predict the selling price...
A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: age of the home in years Bath: total number of bathrooms LotArea: total square footage of the lot on which the house is built TotRms_AbvGrd: total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the...
The following output is regression of hourly EARNINGS ON HEIGHT a. Create a 99% confidence interval...
The following output is regression of hourly EARNINGS ON HEIGHT a. Create a 99% confidence interval for the slope coufficient and interpretit D. is the slope cofficient on HEIGHT statistically significant at a level of significance of 197. Years of schooling Salso acts EARNINGS but has not been added to the regression. Where does the effect of Soccur in this regression output? SUMMARY OUTPUT Regression Statistics Multiple R 0.241124399 R Square 0.058140976 Adjusted R Square 0.056390309 Standard Error 15.96556082 Observations...
Question 2: Hypothesis testing (30 pts) Consider the following simple linear regression model with E[G-0 and...
Question 2: Hypothesis testing (30 pts) Consider the following simple linear regression model with E[G-0 and var(G)-σ2. The output of linear where €1, €2, . . . ,en regression from R takes the form are i.i.d. errors Cal1: lm(formula y ~ x + 1) Residuals: Min 1Q Median 3Q Max 2.0606-0.3287-0.1148 0.5902 1.2809 Coefficients: Estimate Std. Error t value Prlt (Intercept) 0.507932 0.340896 1.49 0.147 0.049656 0.003455 14.37 1.89e-14 Signif. codes: 0.0010.010.05 .'0.1''1 Residual standard error: 0.7911 on 28 degrees...
QUESTION 27 Q27. A manager at a local bank analyzed the relationship between monthly salary (y,...
QUESTION 27 Q27. A manager at a local bank analyzed the relationship between monthly salary (y, in $) and length of service (x, measured in months) for 30 employees. She estimates the model: Salary = Bo + B1 Service + ε. The following ANOVA table below shows a portion of the regression results. df SS M S F Regression 555,420 555,420 7.64 Residual 27 1,962,873 72,699 Total 28 2 ,518,293 Coefficients Standard Error t-stat p-value Intercept 784.92 322.25 2.44 0.02...
need answer for this question In order to examine the relationship between the selling price of a used car and its age, an analyst uses data from 25 recent transactions and estimates Price...
need answer for this question
In order to examine the relationship between the selling price of a used car and its age, an analyst uses data from 25 recent transactions and estimates Price Bo+ B1Age+ E. A portion of the regression results is shown in the accompanying table. [You may find it useful to reference the ttable.] 3 Standard Error 745.43 120.93 t Statp-value 28.456 1.98E-19 Coefficients 21,211.93 -1,206.24 Intercept Age Skipped 8.01E-10 a. Specify the competing hypotheses in order...
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The following is Excel output from a fitted linear regression model relating the sale price of a home (y in thousands of dollars) to age of the home in years. Intercept Age Coefficients 213.365436 -1.207517218 Standard Errort Stat P value 1.450657792 147.0819 0 0.02997096940.2897 1.28 279 Lower 95% Upper 95% 210.5209309 216.2099011 1.26620524 1160749195 Refer to the information above on the regression using age to predict selling price of houses. Which of the following is the correct way...
The following output is a regression of hourly EARNINGS on HEIGHT. a. Create a 99% confidence interval for the slope coefficient B1 and interpret it. b. Is the slope coefficient on HEIGHT statistically significant at a level of significance of 1%? c. Years of schooling S also affects EARNINGS but has not been added to the regression. Where does the effect of Soccur in this regression output? SUMMARY OUTPUT Regression Statistics Multiple R 0.241124399 R Square 0.058140976 Adjusted R Square...
A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: age of the home in years Bath: total number of bathrooms LotArea: total square footage of the lot on which the house is built TotRms_AbvGrd: total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the...
The following output is regression of hourly EARNINGS ON HEIGHT a. Create a 99% confidence interval for the slope coufficient and interpretit D. is the slope cofficient on HEIGHT statistically significant at a level of significance of 197. Years of schooling Salso acts EARNINGS but has not been added to the regression. Where does the effect of Soccur in this regression output? SUMMARY OUTPUT Regression Statistics Multiple R 0.241124399 R Square 0.058140976 Adjusted R Square 0.056390309 Standard Error 15.96556082 Observations...
Question 2: Hypothesis testing (30 pts) Consider the following simple linear regression model with E[G-0 and var(G)-σ2. The output of linear where €1, €2, . . . ,en regression from R takes the form are i.i.d. errors Cal1: lm(formula y ~ x + 1) Residuals: Min 1Q Median 3Q Max 2.0606-0.3287-0.1148 0.5902 1.2809 Coefficients: Estimate Std. Error t value Prlt (Intercept) 0.507932 0.340896 1.49 0.147 0.049656 0.003455 14.37 1.89e-14 Signif. codes: 0.0010.010.05 .'0.1''1 Residual standard error: 0.7911 on 28 degrees...
QUESTION 27 Q27. A manager at a local bank analyzed the relationship between monthly salary (y, in $) and length of service (x, measured in months) for 30 employees. She estimates the model: Salary = Bo + B1 Service + ε. The following ANOVA table below shows a portion of the regression results. df SS M S F Regression 555,420 555,420 7.64 Residual 27 1,962,873 72,699 Total 28 2 ,518,293 Coefficients Standard Error t-stat p-value Intercept 784.92 322.25 2.44 0.02...
need answer for this question
In order to examine the relationship between the selling price of a used car and its age, an analyst uses data from 25 recent transactions and estimates Price Bo+ B1Age+ E. A portion of the regression results is shown in the accompanying table. [You may find it useful to reference the ttable.] 3 Standard Error 745.43 120.93 t Statp-value 28.456 1.98E-19 Coefficients 21,211.93 -1,206.24 Intercept Age Skipped 8.01E-10 a. Specify the competing hypotheses in order...
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