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 |
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
I need help putting this into Excel as I'm not sure how to find answers to...
Price Sqft Beds Baths Col 840000 2768 4 3.5 1 822000 2500 4 2.5 1 713000 2400 3 3 1 689000 2200 3 2.5 1 685000 2716 3 3.5 1 645000 2524 3 2 1 625000 2732 4 2.5 0 620000 2436 4 3.5 1 587500 2100 3 1.5 1 585000 1947 3 1.5 1 583000 2224 3 2.5 1 569000 3262 4 2 0 546000 1792 3 2 0 540000 1488 3 1.5 0 537000 2907 3 2.5 0...
A realty company would like to develop a regression model to help set weekly rental rates for beach properties during the summer season. The independent variables for this model will be the size of the property in square feet, the number of bedrooms it has, the number of balthrooms it has, and its age. Use the accompanying data, which are from randomly selected rental properties, to complete parts a through d below EER Click the icon to view the data...
USE R SOFTWARE TO SOLVE THE PROBLEM and SHOW ALL YOUR WORK WITH CODE. Build the model one a multiple regression model including the living area (), number of bedrooms (), and number of fireplaces () as predictor variables. summary the statistic Produce an ANOVA table. Report SST, SSR, and SSE , and their corresponding degrees of freedom. Model #2: a multiple regression model including the living area, “Central Air” (an indicator variable coded as 1 if a house has...
A realtor in Arlington, Massachusetts, is analyzing the relationship between the sale price of a home (Price), its square footage (Sqft), the number of bedrooms (Beds), and the number of bathrooms (Baths). She collects data on 36 recent sales in Arlington in the first quarter of 2009 for the analysis. The data is shown in the accompanying table Price 840,000 2,768 822,000 2,500 713,000 2,400 689,000 2,200 685,000 2,716 645,000 2,524 625,000 2,732 620,000 2,436 587,500 2,100 585,000 1,947 583,000...
6 Exercise 14-35 Algo 33.71 points A realtor in Arlington, Massachusetts, is analyzing the relationship between the sale price of a home (Price in $), its square footage (Sqft), the number of bedrooms (Beds), and the number of bathrooms (Baths). She collects data on 36 sales in Arlington in the first quarter of 2009 for the analysis. A portion of the data is shown in the accompanying table. Price 728,000 695,538 Sqft 2,399 2,115 Beds 4 Baths 2.5 2.5 eBook...
Hi I need help with these questions on Excel for linear regression! Gulf Home Data Price Size Number of Niceness Pool? Home ($1000s) (Square Feet) Bathrooms Rating yes=1; no=0 This information is taken from 80 homes recently sold 1 260.9 2666 2.5 7 0 along the Gulf of Mexico coast. You are to analyze 2 337.3 3418 3.5 6 1 the data to discover which of the variables have a 3 268.4 2945 2.0 5 1 statistically...
I need help understanding how to interpret a linear regression using a Hedonic Model. I have a just of what it is but I am not conveying it correctly. Here is the data I had to do a regression: B is Beta by the way where PH = price of the house ($) B1BEDS = bedrooms (number) B2BATHS = bathrooms (number) B3SQFT = area of the house (feet squared) B4LOT = area of the lot (feet squared) B5DISTANCE = distance...
Show your work. Carry out all calculations to at least 3 significant digits. A real estate study was conducted in the school district of Alhambra to determine what variables influenced the market value of a house (denoted by PRICE in $1,000s). Four possibly important variables – HOUSE (the house size in 1,000s of square feet), LOT (the lot size in 1,000s of square feet), BED (the number of bedrooms), BATH (the number of bathrooms), and AGE (the age of the...
Ches A realtor in Arlington, Massachusetts, is analyzing the relationship between the sale price of a home (Price in $), its square footage (Sqft), the number of bedrooms (Beds), and the number of bathrooms (Baths). She collects data on 36 sales in Arlington in the first quarter of 2009 for the analysis. A portion of the data is shown in the accompanying table. Price 672.000 569.077 Sqft 2,212 1.731 Beds Baths 5 1.0 11.5 307,500 850 1 1.0 Click here...
data: Price Size Bedrooms Baths Age $235,000.00 1,530 3 2 6 $375,000.00 2,380 4 3 43 $199,950.00 720 2 1 2 $258,000.00 1,040 2 2 40 $96,500.00 484 1 1 43 $237,000.00 1,584 3 3 23 $829,000.00 2,701 5 3 7 $200,000.00 952 2 2 18 $328,500.00 1,098 3 3 75 $365,000.00 2,004 3 2 35 $116,000.00 640 2 1 41 $885,000.00 3,849 6 4 5 $250,000.00 2,010 3 2 84 $165,000.00 575 2 1 32 $159,900.00 984 2 2...