Reference no: EM13844870

I have 4 problems to be solved. solve the problems in separate sheet. The first page is provided with the instruction. I would perfer the expert to answer on the excel sheet.

Instruction

Complete Problem 3

In addition to the questions in this problem, respond to the following:

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.

Complete Problem 4.

Use Excel's regression option to perform the regression. Use one Excel spreadsheet file for the calculations and explanations, with one worksheet per problem. Use the problem number for each worksheet name. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell).

Problem 1:

The following data give the selling price, square footage , number of bedrooms, and age of house that have sold in a nieghbourhood in the past 6 months. Develop three regression models to predict the selling price based upon each of the other factors individually. Which of these is best?

SELLING	SQUARE		AGE
PRICE($)	FOOTAGE	BEDROOMS	(YEARS)
84,000	1,670	2	30
79,000	1339	2	25
91,500	1712	3	30
120,000	1840	3	40
127,500	2300	3	18
132,500	2234	3	30
145,000	2311	3	19
164,000	2377	3	7
155,000	2736	4	10
168,000	2500	3	1
172,000	2500	4	3
174,000	2479	3	3
175,000	2400	3	1
177,000	3124	4	0
184,000	2500	3	2
195,000	4062	4	10
195,000	2854	3	3

Problem 2:

Use the data in problem 1 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 1. Should the number of bedrooms be included in the model? Why or Why not?

Problem 3:

Use the data in problem 1 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 yr old, 2,000 square foot house with three bedrooms.

In addition to the questions in this problem, respond to the following:

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.

Problem 4:

In 2012, the total payroll for the New York Yankees was almost $200 million, while the total payroll for the Oakland Athletics (a team know 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 payroll of $79 million.

Bsed on the results of the computer output, discuss the relationship between payroll and victories.

		PAYROLL		NUMBER OF
TEAM		($MILLIONS)		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 Angels		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