Mulberry Realty sells homes in the northeast section of the United States. A fre
ID: 3295633 • Letter: M
Question
Mulberry Realty sells homes in the northeast section of the United States. A frequently asked question by prospective buyers is: If we buy this house, how much can we expect to pay for it? The research division of Mulberry tried to create a model to help the agency better estimate the selling price of a house based upon its particular characteristics. The agency used data collected from some recent house sales. The variables included: (PRICE) which is the selling price of the house in thousands of dollars; (SQUARE FT) which is the size of the house measured in square feet; (ROOMS) which is the number of rooms in the house; (BDRMS) which is the number of bedrooms in the house; and (AGE) which is the number of years old that the house is based upon the year of its original construction. Parentheses contain the variable names (in bold) as listed in the data set.
The data set for this scenario is included in an Excel file.
Use the Data Analysis in Excel and run the appropriate multiple regression model. This is a computer deliverable. Please make sure you include the normal probability plot.
In addition, use Data Analysis in Excel and create the correlation matrix for the entire data set, including the dependent and independent variables. This is a computer deliverable.
Based upon the output generated, please answer the following questions.
m. Which independent variable is the most strongly correlated with the dependent variable
n. Which two independent variables are the most strongly correlated?
o. Which of the two independent variables are the most weakly correlated?
p. For the two variables that answer part l, test whether or not the population correlation coefficient differs from zero. Use alpha = 0.05
DATA INFO:
PRICE SQUA FT ROOMS BDRMS AGE
53.5 1008 5 2 35
49 1290 6 3 36
50.5 860 8 2 36
49.9 912 5 3 41
52 1204 6 3 40
55 1204 5 3 10
80.5 1764 8 4 64
86 1600 7 3 19
69 1255 5 3 16
149 3600 10 5 17
46 864 5 3 37
38 720 4 2 41
49.5 1008 6 3 35
105 1950 8 3 52
152.5 2086 7 3 12
85 2011 9 4 76
60 1465 6 3 102
58.5 1232 5 2 69
101 1736 7 3 67
79.4 1296 6 3 11
125 1996 7 3 9
87.9 1874 5 2 14
80 1580 5 3 11
94 1920 5 3 14
74 1430 9 3 16
69 1486 6 3 27
63 1008 5 2 35
67.5 1282 5 3 20
35 1134 5 2 74
142.5 2400 9 4 15
92.2 1701 5 3 15
56 1020 6 3 16
Explanation / Answer
m. price is higly corrleated with area sqyuare feet
n. strongly correlated variables are price and square feet area.
o. weakly correlated are price and age
PRICE SQUA ROOMS BDRMS AGE PRICE 1 SQUA 0.879368 1 ROOMS 0.595611 0.688785 1 BDRMS 0.503484 0.702027 0.677488 1 AGE -0.28529 -0.13492 0.186551 0.037808 1Related Questions
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