Need regression problem help 4 total

hw_problems_wk4.xlsx

4-22

Problem 4-22 Data
The following data give the selling price, square footage, number of bedrooms, and age of houses
that have sold in a neighborhood in the past 6 months. Develop three regression models to predict the selling price based on upon
each of the other factors invidually. Which of these is best?
Selling Price($)	Square Footage	Bedrooms	Age (Years)
84,000	1,670	2	30
79,000	1,339	2	25
91,500	1,712	3	30
120,000	1,840	3	40
127,500	2,300	3	18
132,500	2,234	3	30
145,000	2,311	3	19
164,000	2,377	3	7
155,000	2,736	4	10
168,000	2,500	3	1
172,500	2,500	4	3
174,000	2,479	3	3
175,000	2,400	3	1
177,500	3,124	4	0
184,000	2,500	3	2
195,500	4,062	4	10
195,000	2,854	3	3

Use the data in Problem 4-22 and develop a regression model to predict selling price based on the square footage

and number of bedrooms. Use this to predict the selling price of a 2,000 square foot house with three bedrooms. Compare this model

with the models in Problem 4-22. Should the number of bedrooms be included in the model? Why or why not?

Use the data in Problem 4-22 and develop a regression model to predict selling price based on the square footage,
number of bedrooms, and age. Use this to predict the selling price of a 10-year-old, 2,000-square-foot house with three bedrooms.
1	State the linear equation.
2	Explain the overall statistical significance of the model.
3	Explain the statistical significance for each independent variable in the model
4	Interpret the Adjusted R2.
5	Is this a good predictive equation(s)? Which variables should be excluded (if any) and why? Explain.

In 2012, the total payroll for the New York Yankess was almost $200 million, while the total payroll for the Oakland Athletics
(a team known for using baseball analytics or sabermetrics) was about $55 million, less than one-third of the Yankees payroll.
In the following table, you will see the payrolls (in millions) and the total number of victories for the baseball teams in the American
League in the 2012 season. Develop a regression model to predict the total number of victories based on the payroll. Use the
model to predict the number of victories for a team with a team with a payroll of $79 million. Based on the results of the computer
output, discuss the relationship between payroll and victories.
Team	Payroll ($MILLIONS)	NUMBER OF VICTORIES
Baltimore Orioles	81.4	93
Boston Red Sox	173.2	69
Chicago White Sox	96.9	85
Cleveland Indians	78.4	68
Detroit Tigers	132.3	88
Kansas City Royals	60.9	72
Los Angeles Royals	154.5	89
Minnesota Twins	94.1	66
New York Yankees	198	95
Oakland Athletics	55.4	94
Seattle Mariners	82	75
Tampa Bay Rays	64.2	90
Texas Rangers	120.5	93
Toronto Blue Jays	75.5	73