Question

In: Statistics and Probability

An agent for a residential real estate company in a large city has the business objective...

An agent for a residential real estate company in a large city has the business objective of developing more accurate estimates of the monthly rental cost for apartments. Toward that goal, the agent would like to use the size of an apartment, as defined by square footage to predict the monthly rental cost. The agent selects a sample of 25 apartments in a particular residential neighborhood and collects the following data:

Size (square feet)

Rent ($)

850

1950

1450

2600

1085

2200

1232

2500

718

1950

1485

2700

1136

2650

726

1935

700

1875

956

2150

1100

2400

1285

2650

1985

3300

1369

2800

1175

2400

1225

2450

1245

2100

1259

2700

1150

2200

896

2150

1361

2600

1040

2650

755

2200

1000

1800

1200

2750

(a) Determine the coefficient of determination, r2, and interpret its meaning.

(b) Determine the standard error of the estimate (Syx).

(c) How useful do you think this regression model is for predicting the monthly rent?

(d) Can you think of other variables that might explain the variation in monthly rent?

Solutions

Expert Solution

a)

x

Y

X^2

Y^2

XY

850

1950

722500

3802500

1657500

1450

2600

2102500

6760000

3770000

1085

2200

1177225

4840000

2387000

1232

2500

1517824

6250000

3080000

718

1950

515524

3802500

1400100

1485

2700

2205225

7290000

4009500

1136

2650

1290496

7022500

3010400

726

1935

527076

3744225

1404810

700

1875

490000

3515625

1312500

956

2150

913936

4622500

2055400

1100

2400

1210000

5760000

2640000

1285

2650

1651225

7022500

3405250

1985

3300

3940225

10890000

6550500

1369

2800

1874161

7840000

3833200

1175

2400

1380625

5760000

2820000

1225

2450

1500625

6002500

3001250

1245

2100

1550025

4410000

2614500

1259

2700

1585081

7290000

3399300

1150

2200

1322500

4840000

2530000

896

2150

802816

4622500

1926400

1361

2600

1852321

6760000

3538600

1040

2650

1081600

7022500

2756000

755

2200

570025

4840000

1661000

1000

1800

1000000

3240000

1800000

1200

2750

1440000

7562500

3300000

Total

28383

59660

34223535

145512350

69863210


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