Megan Hanson, 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: (1) the square footage of the house (SQFT); (2) the number of bedrooms (Bed); (3) the number of bathrooms (Bath); and (4) the lot size (LTSZ) in acres. She randomly collects online listings of 50 single-family homes. This information can be found in the attached excel file. In a report, use the sample information to:

1. Provide summary descriptive statistics on the asking price, the square footage, the number of bedrooms, the number of bathrooms, and the lot size.

2. Estimate a multiple regression model where Price is the response variable and SQFT, Bed, Bath, and LTSZ are the explanatory variables.

3. Interpret the resulting coefficient of determination.

4. Conduct joint and individual significance tests at the 5% significance level.

Price SQFT Bed Bath LTSZ
399900 5.026 4 4.5 0.3
375000 3.2 4 3 5
372000 3.22 5 3 5
370000 4.927 4 4 0.3
325000 3.904 3 3 1
325000 2.644 3 2.5 5
319500 5.318 3 2.5 2.5
312900 3.144 4 2.5 0.3
299900 2.8 4 3 5
294900 3.804 4 3.5 0.2
269000 3.312 5 3 1
250000 3.373 5 3.5 0.2
249900 3.46 2 2.5 0.6
244994 3.195 4 2.5 0.2
244900 2.914 3 3 0.3
239900 2.881 4 5 0.3
234900 1.772 3 2 3.6
234000 2.248 3 2.5 0.3
229900 3.12 5 2.5 0.2
219900 2.942 4 2.5 0.2
209900 3.332 4 2.5 0.2
209850 3.407 3 2.5 0.3
206900 2.092 3 2 0.3
200000 3.859 4 2 0.2
194900 3.326 4 2.5 0.1
184900 1.874 3 2 0.5
179900 1.892 3 1.5 0.7
179500 2.5 4 2.5 0.5
165000 2.435 4 2.5 0.4
159900 2.714 3 2.5 0.2
159900 1.85 3 2.5 0.5
155000 3.068 4 3.5 0.2
154900 2.484 4 2.5 0.3
152000 1.529 4 2 0.4
149900 2.876 4 2.5 0.2
148500 2.211 4 2.5 0.1
146900 1.571 3 2 0.2
145500 1.503 4 2 0.5
144900 1.656 3 2 0.5
144900 1.521 3 2 0.6
139900 1.315 3 2 0.2
137900 1.706 3 2 0.3
132900 2.121 4 2.5 0.1
129900 1.306 3 2 0.5
129736 1.402 3 2 0.5
125000 1.325 3 2 0.3
119500 1.234 3 2 0.2
110387 1.292 3 1 0.2
106699 1.36 3 1.5 0.1
102900 1.938 3 1 0.1