Here is the question and chart The following data give the selling price, square
ID: 3154370 • Letter: H
Question
Here is the question and chart
The following data give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood 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
PRICE ($)
SQUARE FOOTAGE
BEDROOMS
AGE (YEARS)
84,000
1,670
2
30
79,000
1,339
2
25
91,500
1,712
3
30
120,000
1,840
3
40
127,500
2,300
3
18
132,500
2,234
3
30
145,000
2,311
3
19
164,000
2,377
3
7
155,000
2,736
4
10
168,000
2,500
3
172,500
2,500
4
3
174,000
2,479
3
3
175,000
2,400
3
177,500
1,124
4
0
184,000
2,500
3
2
195,500
4,062
4
10
195,000
2,854
3
3
This is what I came up with, am I doing it right?
SUMMARY OUTPUT
Price Vs Bedrooms
66611.2936
SELLING
PRICE ($)
SQUARE FOOTAGE
BEDROOMS
AGE (YEARS)
84,000
1,670
2
30
79,000
1,339
2
25
91,500
1,712
3
30
120,000
1,840
3
40
127,500
2,300
3
18
132,500
2,234
3
30
145,000
2,311
3
19
164,000
2,377
3
7
155,000
2,736
4
10
168,000
2,500
3
172,500
2,500
4
3
174,000
2,479
3
3
175,000
2,400
3
177,500
1,124
4
0
184,000
2,500
3
2
195,500
4,062
4
10
195,000
2,854
3
3
Explanation / Answer
Yes, the approach is correct.
The best model is the one whose r squared value is the highest.(keeping in mind that the P value should also be less than 0.05).
So the model with the highest R squared value among these 3 models is the best one.