Question

I need help putting this into Excel as I'm not sure how to find answers to these questions. I've only put part of the table in, otherwise it's too long. Any help is greatly appreciated!

A) Develop the following simple linear regression models to predict the sale price of a house based upon a 90% level of confidence.

A1) Write the regression equation for each model.
A2) Sale price based upon square feet of living area.
A3) Sale price based upon number of bedrooms.
A4) Sale price based upon number of bathrooms.

B) Develop the following multiple linear regression models to predict the sale price of a house based upon a 90% level of confidence.

B1) Write the regression equation for each model.
B2) Sale price based upon square feet of living area and number of bedrooms.
B3) Sale price based upon square feet of living area and number of bathrooms.
B4) Sale price based upon number of bedrooms and number of bathrooms.
B5) Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms.

C) Discuss the joint statistical significance of each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
D) Discuss the individual statistical significance of the coefficient for each independent variable for each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
E) Compare any of the preceding simple and multiple linear regression models that were found to be jointly and individually statistically significant at a 90% level of confidence and select the preferred regression model. Explain your selection using the appropriate regression statistics.
F) Interpret the coefficient for each independent variable (or variables) associated with your selected preferred regression model.
G) Using the preferred regression model, predict the sale price of a house with the following values for the independent variables: 3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms. (Hint: You should only use the values for those independent variables that are specifically associated with your selected preferred regression model.)

Selling Price	Living Area (Sq Feet)	No. Bathrooms	No Bedrooms
$145,000	1,152	1	2
$103,000	1,290	1.5	3
$210,000	2,396	1.5	4
$559,000	3,090	4	4
$218,000	1,428	1	3
$262,138	1,631	2.5	3
$125,000	1,368	1	3
$130,000	1,134	1	3
$157,500	1,697	1.5	3
$193,000	1,666	2.5	3
$275,000	1,738	2.5	4
$240,000	1,457	1.5	2
$200,136	1,632	2.5	3
$395,000	2,186	2.5	3
$366,703	2,117	2.5	3
$103,150	936	1	3
$310,000	3,347	2.5	6
$142,900	1,824	2.5	4
$359,770	2,592	3	3
$240,000	2,022	2.5	3
$235,000	1,578	2	3
$500,075	3,400	3	3
$240,000	1,744	2.5	3
$270,000	2,560	2.5	3
$225,000	1,398	2.5	3
$280,000	2,494	2.5	3
$225,000	2,208	2.5	4
$248,220	2,550	2.5	3
$275,000	1,812	2.5	2
$137,000	1,290	1	2
$150,000	1,172	2	2
$649,000	4,128	3.5	3
$195,000	1,816	2.5	3
$373,200	2,628	2.5	4
$169,450	1,254	2.5	3
$144,200	1,660	1.5	4
$189,900	1,850	1.5	3
$166,000	1,258	2	3
$160,000	1,219	2	3
$327,355	1,850	2.5	3
$247,000	2,103	2.5	3
$318,000	1,806	2.5	3
$341,000	1,674	1.5	2
$288,650	2,242	2.5	3
$157,000	1,408	1.5	3
$449,000	3,457	2.5	3
$142,000	1,728	1.5	3
$389,000	2,354	2.5	3
$476,000	2,246	2.5	3
$249,230	1,902	2.5	2
$139,900	1,178	1	3
$301,900	2,896	3.5	4
$425,000	2,457	3	3
$121,000	936	1	3
$150,000	934	1	2
$138,000	1,279	1	3
$199,900	1,888	2	3
$145,000	1,686	1.5	4
$465,000	2,310	3	2
$158,000	1,200	1.5	3
$375,000	1,944	2.5	3
$147,783	1,184	1	3
$339,000	2,000	2.5	3
$267,000	1,744	2.5	3
$290,000	1,620	2.5	3
$271,295	2,359	2.5	4
$278,140	2,230	2.5	3
$138,000	1,147	1.5	2
$367,500	2,205	2.5	3
$428,500	3,243	2.5	4
$140,000	1,431	1.5	3
$140,000	936	1	3
$145,000	1,368	1.5	3
$280,000	1,800	2.5	3
$190,400	1,920	1.5	3
$267,000	1,416	2	3
$164,900	616	1	2
$280,000	1,488	1.5	4
$225,000	1,320	1.5	3
$148,000	1,250	1.5	2
$165,000	1,286	1	3
$340,455	2,886	2.5	4
$133,000	1,279	1	3
$115,540	1,074	1	3
$240,000	1,304	1	3
$600,000	3,191	3.5	3
$71,500	1,056	1	3
$189,900	1,468	2.5	3
$325,000	1,598	1.5	3
$280,000	1,160	1.5	3
$255,000	1,028	1	2
$300,000	2,014	1	6
$106,000	1,296	1	3
$139,000	932	1	2
$165,000	1,290	1.5	3
$169,000	1,448	1.5	2
$159,400	1,368	1	3
$300,000	3,046	1.5	4
$161,000	1,272	1.5	3
$230,500	1,822	2.5	4
$220,000	1,579	1.5	4
$177,900	1,660	1.5	4
$350,000	2,452	2.5	3
$335,000	2,004	2	3
$597,185	4,210	3.5	4
$173,000	1,640	1.5	4
$518,000	2,847	1.5	3
$212,000	1,816	2	3
$149,900	2,028	2	4
$140,958	775	1	2
$95,000	1,368	1	2
$161,600	936	1.5	3
$351,400	1,680	2.5	2
$155,000	1,368	1	3
$198,500	1,326	1.5	3
$276,000	1,579	2	3
$167,000	1,286	1	3
$227,000	2,132	2.5	3
$405,100	2,000	2.5	3
$395,000	2,222	2.5	3
$360,000	2,641	2	4
$775,000	2,472	2.5	3
$285,000	1,926	3	2
$300,000	1,534	2.5	3
$214,000	1,490	1.5	3
$229,000	1,126	2	3
$330,000	1,435	1.5	3
$146,000	1,136	1.5	3
$381,500	2,162	2.5	3
$430,000	2,328	2.5	3
$650,000	2,754	2.5	3
$194,500	1,504	1.5	4
$279,000	2,473	3.5	4
$183,000	2,072	2.5	4
$195,000	1,480	2.5	3
$470,000	2,096	2.5	3
$167,000	1,050	1	2
$309,278	1,650	2.5	2
$217,500	1,596	1.5	3
$200,000	979	2	3
$199,995	1,393	1.5	2
$179,000	864	1	2
$329,000	2,744	2.5	4
$374,900	2,300	3	3
$165,000	1,612	1	3
$647,000	2,640	2.5	4
$315,000	775	1	1
$208,000	2,470	2.5	4
$405,000	3,124	3	4
$535,000	3,254	2.5	4
$184,020	1,544	2.5	3
$95,000	1,410	1.5	6
$87,550	906	1	2
$131,000	1,512	1	3
$127,000	1,232	1	3
$104,695	921	1	2
$120,000	1,287	1	3
$278,500	2,600	3	4
$75,500	1,302	2	3
$121,900	1,144	1	3
$208,000	1,703	2	3
$151,000	1,553	2	3
$138,000	1,040	1	2
$229,000	2,124	3	4
$130,000	1,344	2	3
$89,900	884	1	2
$145,000	1,812	1.5	3
$86,000	880	1	3
$95,000	1,256	2	3
$195,000	1,826	2.5	3
$107,325	1,134	1.5	3
$131,000	1,916	2.5	4
$166,900	1,272	1.5	3
$127,000	960	1	3
$119,000	1,152	1.5	2
$92,509	840	1	2
$174,000	2,106	2	5
$180,000	1,632	2	3
$155,000	1,092	2	3
$146,500	1,634	1.5	3
$162,500	1,890	2.5	3
$210,000	2,224	2.5	5
$165,000	1,475	2	3
$123,000	1,480	1.5	3
$150,000	1,368	1	3
$128,900	1,200	1	3
$88,100	840	1	2
$175,000	1,176	2	3
$120,975	1,380	2	3
$147,543	1,786	2.5	3
$118,000	1,480	1.5	3
$199,000	1,574	1.5	3
$79,900	950	1.5	2
$279,000	3,178	3.5	4
$90,000	1,342	1.5	3
$91,900	1,336	1.5	2
$153,000	1,542	2.5	3
$170,000	2,272	3.5	4

Answer 1

A)

A2)

Sale price based upon square feet of living area.

The regression equation is given by

Y = 142.2543959X - 23064.61598

Y = Sales price in dollars

X = Square feet of living area.

A3)

Sale price based upon the number of bedrooms.

The regression equation is given by

Y = 40113.90385X + 105696.5769

Y = Sales price in dollars

X = No of Bedrooms

A4)

Sale price based upon the number of bathrooms

The regression equation is given by

Y = 113369.8846X + 9854.809717

Y = Sales price in dollars

X = No of Bathrooms

B)

B2)

Sale price based upon square feet of living area and number of bedrooms.

Regression equation is given by

Y = 213.9101105X1 - 74833.98496X2 + 90336.18553

Y = Sales price in dollars

X1 = Square feet of living area.

X2 = No of Bedrooms

B3)

Sale price based upon square feet of living area and number of bathrooms.

Regression equation is given by

Y = 73.07796607X1 + 71746.36155X2 - 40288.5095

Y = Sales price in dollars

X1 = Square feet of living area.

X2 = No of Bathrooms

B4)

Sale price based upon the number of bedrooms and number of bathrooms.

Regression equation is given by

Y = -6569.109081X1 + 116149.1231X2 + 25732.37296

Y = Sales price in dollars

X1 = No of Bedrooms

X2 = No of Bathrooms

B5)

Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms

The regression equation is given by

Y = 148.2555647X1 - 61665.83682X2 + 55016.32877X3 + 57174.06061

Y = Sales price in dollars

X1 = Square feet of living area.

X2 = No of Bedrooms

X3 = No of Bathrooms

C)

At 90% Significance, Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms

D)

At the significance of 95%, Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms

E)

The preferred model depends on the relapse condition is characterized dependent on esteem. Higher the estimation of the better the model appropriateness clarifying the variety in the needy variable (for example Deals cost). So the most favored model is given by

Y = 148.2555647X1 - 61665.83682X2 + 55016.32877X3 + 57174.06061

F)

Favored regression condition is given by

Y = 148.2555647X1 - 61665.83682X2 + 55016.32877X3 + 57174.06061

Y = Sales cost in dollars

X1 = Square feet of living region.

X2 = No of Bedrooms

X3 = No of Bathrooms

This implies

Variety in Sales cost is affected by 148.2 occasions in Square feet of the living zone.

Variety in Sales cost is affected by - 61665.8 occasions No of Bedrooms.

Variety in Sales cost is affected by 55016.3 occasions No of Bathrooms

G)

Regression condition is given by

Y = 148.2555647X1 - 61665.83682X2 + 55016.32877X3 + 57174.06061

Here X1 = 3000sqft

X2 = 3 Bedrooms

X3 = 2.5 Bathrooms

So Y = $454484.1