11) A regression of income (in thousands of dollars) on education (in years) is performed on...
11) A regression of income (in thousands of dollars) on education (in years) is performed on data collected from 27 individuals. It had an intercept of 31.8 and a slope of 6.1. Answer the following questions:
a) Fill in the missing information in the ANOVA table.
|
Sum of Squares |
df |
Mean Squares |
F |
|
|
Regression |
2,000 |
|||
|
Residual |
||||
|
Total |
20,000 |
b) What is the predicted income for someone with 11 years of education?
c) what is the coefficient of determination?
d) Using the context of the problem, explain the meaning of the slope being 6.1.
Homework Answers
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11) A regression of income (in thousands of dollars) on
education (in years) is performed on...
Investigate the relationship between the respondent’s education (EDUC) and the education received...
Investigate the relationship
between the respondent’s education (EDUC) and the education
received by his or her father and mother (PAEDUC and MAEDUC,
respectively). Calculate the correlation coefficient, the
coefficient of determination, and the regression equation
predicting the respondent’s education with father’s education only.
Interpret your results. Determine the multiple correlation
coefficient, the multiple coefficient of determination, and the
regression equation predicting the respondent’s education with
father’s and mother’s education. Interpret your results. Did taking
into account the respondent’s mother’s education...
ANOVA df SS Regression 1 0.72 Residual 10 62.6 Total 11 63.32 Coefficients Std Error Intercept...
ANOVA df SS Regression 1 0.72 Residual 10 62.6 Total 11 63.32 Coefficients Std Error Intercept 14.64 146.76 No. of accounts (000) 1.99 5.87 This printout is for data relating the number of ATM withdrawals (in thousands) to the number of accounts (in thousands) at that branch. Predict the number of withdrawals if the number of accounts is 24.528 thousand. State the answer in thousands correct to two decimal places.
Please show full details steps for better understanding. Thank you. Regression Coefficients Estimates Model formula: mpg...
Please show full details steps for better understanding. Thank
you.
Regression Coefficients Estimates Model formula: mpg - cyl + disp + hp + am Term Coefficient Estimate Standard Error t Value (Intercept) 30.476 2.8655 10.636 cyl -0.8345 0.75709 -1.1022 disp -0.0077447 0.010716 -0.72272 Pr > It! 3.7246e-11 0.28008 0.47607 hp -0.032962 0.015614 -2.1111 0.044166 am 3.4453 1.4539 2.3697 0.025205 Model Summary: Coefficient of Determination (R-Squared) Model formula: mpg - cyl + disp + hp + am Residual Standard Error DF...
correlations and linear regression education and income 3. Use the following data set and complete the table. (Inc...
correlations and linear regression education and income
3. Use the following data set and complete the table. (Income is the dependent variable and education is the independent variable) Education (Years) I x -* Yi- } (x; - x)(y;-)) (x; -x² Income ($1000) 15 46 13 17 58 46 13 10 40 63 15 18 11 9 11 15 45 12 50 16 9 14 48 58 14 16 50 11 a. Calculate x, y, and the standard deviations for both...
A researcher is developing a regression equation to predict income (Y) in thousands of dollars from...
A researcher is developing a regression equation to predict income (Y) in thousands of dollars from age (X1) and educational status (X2). Educational status is a dummy variable with 0 = high school or less and 1 = some education beyond high school. Y = 11.4 + 0.6X1 + 4.7X2 + e. What is the predicted income for a 26-year-old with a college degree? a.)$27,700 b.)$31,700 c.)$41,860 d.)$24,220
1. A multiple regression analysis between yearly income (Y in $1,000s), college grade point average (X1),...
1. A multiple regression analysis between yearly income (Y in $1,000s), college grade point average (X1), age of the individuals (X2), and the gender of the individual (X3; zero representing female and one representing male) was performed on a sample of ten students, and the following results were obtained: Coefficients Standard Error p-value Intercept 4.0928 1.4400 X1 10.0230 1.6512 X2 0.1020 0.1225 X3 ‐4.4811 1.4400 ANOVA DF SS MS Regression 360.59 Residual error 23.91 a. Write the regression...
7. Multiple regression analysis is used to study how an individual's income (y, in thousands of...
7. Multiple regression analysis is used to study how an individual's income (y, in thousands of dollars) is influenced by age (x1, in years), level of education (22, ranging from 1 to 5), and the individual's gender (23, where 0 = female and 1 = male). The following shows parts of the regression output for a sample of 20 individuals. 21 Variable Coefficient 0.63 0.92 -0.51 S Sres = 112, SSexp = 84 Standard Error 0.09 0.19 0.92 23 (a)...
A real estate builder wishes to determine how house size is influenced by family income, family...
A real estate builder wishes to determine how house size is influenced by family income, family size, and education of the head of household. House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is measured in years. A partial computer output is shown below. SUMMARY OUTPUT Regression Statistics Multiple R 0.865 R Square 0.748 Adjusted R Square _____ Standard Error 5.195 Observations 50 ANOVA df SS MS F Significance F Regression...
Using the Excel’s Regression Tool, develop the estimated regression equation to show how income (y annual...
Using the Excel’s Regression Tool, develop the estimated regression equation to show how income (y annual income in $1000s) is related to the independent variables education (x1 level of education attained in number of years), age (x2 in years), and gender x3 dummy variable, 1= female, 0 = male. Develop the dummy variable for the gender variable first. Use the t test to test whether each of the coefficients obtained in part (a) are significant at .05 level of significance....
Dep.= % WRK Indep.= % MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R...
Dep.= % WRK Indep.= % MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Significance df SS MS F F Regression 102.1488 148.9539 Residual Total 12.0000 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept % MGT 0.4543 SE CI CI PI PI Predicted Predicted Lower Upper Lower Upper x0 Value Value 95% 95% 95% 95% 67.0000 67.8474 65.8779 69.8169 72.0000 70.1189 68.2003 72.0375 76.0000 71.9361 69.7884 74.0838 Dep.= % MGT...
Investigate the relationship
between the respondent’s education (EDUC) and the education
received by his or her father and mother (PAEDUC and MAEDUC,
respectively). Calculate the correlation coefficient, the
coefficient of determination, and the regression equation
predicting the respondent’s education with father’s education only.
Interpret your results. Determine the multiple correlation
coefficient, the multiple coefficient of determination, and the
regression equation predicting the respondent’s education with
father’s and mother’s education. Interpret your results. Did taking
into account the respondent’s mother’s education...
Please show full details steps for better understanding. Thank
you.
Regression Coefficients Estimates Model formula: mpg - cyl + disp + hp + am Term Coefficient Estimate Standard Error t Value (Intercept) 30.476 2.8655 10.636 cyl -0.8345 0.75709 -1.1022 disp -0.0077447 0.010716 -0.72272 Pr > It! 3.7246e-11 0.28008 0.47607 hp -0.032962 0.015614 -2.1111 0.044166 am 3.4453 1.4539 2.3697 0.025205 Model Summary: Coefficient of Determination (R-Squared) Model formula: mpg - cyl + disp + hp + am Residual Standard Error DF...
correlations and linear regression education and income
3. Use the following data set and complete the table. (Income is the dependent variable and education is the independent variable) Education (Years) I x -* Yi- } (x; - x)(y;-)) (x; -x² Income ($1000) 15 46 13 17 58 46 13 10 40 63 15 18 11 9 11 15 45 12 50 16 9 14 48 58 14 16 50 11 a. Calculate x, y, and the standard deviations for both...
7. Multiple regression analysis is used to study how an individual's income (y, in thousands of dollars) is influenced by age (x1, in years), level of education (22, ranging from 1 to 5), and the individual's gender (23, where 0 = female and 1 = male). The following shows parts of the regression output for a sample of 20 individuals. 21 Variable Coefficient 0.63 0.92 -0.51 S Sres = 112, SSexp = 84 Standard Error 0.09 0.19 0.92 23 (a)...
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