Question

Data were collected from a random sample of 340 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDR denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote the lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the condition of the house is reported as "poor."

An estimated regression yields:

Price = 113.2 + 0.461BDR + 22.2Bath + 0.148Hsize + .002Lsize

(22.7) (2.79) (8.49) (.010) (.00046)

+.086Age - 46.4Poor, R^2 = .68, SER = 39.4

(.295) (10.0)

1. The 99% confidence interval for the effect of lot size on price is (___,___)? (Round to two decimal places.)

2. The degree of freedom to test if the coefficients on BDR and Age are statistically different from zero at the 10% level is?

Answer #1

1)

The 99% confidence interval for the effect of lot size on price

df = n-k = 340-7 = 333, alpha = 0.01

tc = 2.5907

CI = b4 +/ tc*Sb4

Here b4 = 0.002, Sb4 = 0.00046

CI = 0.002 +/- 2.5907*0.00046

CI = (0.0008, 0.0032)

CI = (0.00, 0.00)

2)

The degrees fredom = n-k = 340-7 = 333

BDR:

t test = b1/Sb1 = 0.461/2.79 = 0.1652

P value = 0.8689 > 0.1, Do not Staristically Significant

Age:

t test = b5/Sb5 = 0.086/0.295 = 0.2915

P value = 0.7708 > 0.1, Do not Staristically Significant

Homes For Sale
Data were collected from a random sample of 120 homes for sale
in the United States. The variables in the data set include the
following:
Price: asking price (in thousands of dollars)
Size: livable area (in thousands of square feet)
Beds: number of bedrooms
Bath: number of bathrooms
Research question
Can the asking price of a house be predicted using the size,
number of bedrooms, and number of bathrooms?
what descriptive statistics and graphs will you
produce?...

Homes For Sale
Data were collected from a random sample of 120 homes for sale
in the United States. The variables in the data set include the
following:
Price: asking price (in thousands of dollars)
Size: livable area (in thousands of square feet)
Beds: number of bedrooms
Bath: number of bathrooms
<Research Questions>
1. Is there a relationship between the size of a house and
asking price?
2. Can the asking price of a house be predicted using the size,...

Statistical Methods of Business II – Case Study –
Indiana Real Estate
Ann Perkins, a realtor in Brownsburg, Indiana, would like to use
estimates from a multiple regression model to help prospective
sellers determine a reasonable asking price for their homes. She
believes that the following four factors influence the asking price
(Price) of a house:
The square footage of the house (SQFT)
The number of bedrooms (Bed)
The number of bathrooms (Bath)
The lot size (LTSZ) in acres
She...

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