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Question1)One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below is the Excel output for the regression model.
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Question 2
A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects the following information:


Question 3
A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects information to build a regression model. 

Question 4
The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of 4 students. For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below.


Question 5
A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel output is provided below msoparamarginbottom:.0001pt; msopagination:widoworphan; fontsize:11.0pt; fontfamily:"Calibri","sansserif"; msoasciifontfamily:Calibri; msoasciithemefont:minorlatin; msofareastfontfamily:"Times New Roman"; msofareastthemefont:minorfareast; msohansifontfamily:Calibri; msohansithemefont:minorlatin; msobidifontfamily:"Times New Roman"; msobidithemefont:minorbidi;} Regression Statistics Multiple R 0.865 R Square 0.748 Adjusted R Square 0.726 Standard Error 5.195 Observations 50 ANOVA


Question 6
The department head of the accounting department wanted to see if she could predict the GPA of students using the number of course units (credits) and total SAT scores of each. She takes a sample of students and generates the following Microsoft Excel output:


Question 7
A large national bank charges local companies for using their services. A bank official reported the results of a regression analysis designed to predict the bank's charges (Y)measured in dollars per monthfor services rendered to local companies. One independent variable used to predict service charges to a company is the company's sales revenue (X) measured in millions of dollars. Data for 21 companies who use the bank's services were used to fit the model. The results of the simple linear regression are provided below. 

Question 8
One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below is the Excel output for the regression model.


Question 9
The department head of the accounting department wanted to see if she could predict the GPA of students using the number of course units (credits) and total SAT scores of each. She takes a sample of students and generates the following Microsoft Excel output:


Question 10
One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below is the Excel output for the regression model.


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