Statistics and Probability Economics 216

Stat 216

Practice quiz.

Two variable relationships

1. An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost for apartments based on the size of the apartment as defined by square footage. A sample of 20 apartments in a particular residential neighborhood was selected and the data are given below.

a. Construct a scatterplot and include a title and axis labels.

b. Calculate the regression equation. Write out empirical equation representing the relationship between square feet and price of an apartment rental.

c. Interpret the meaning of the intercept (a) and the slope coefficient (b) in your equation you calculated in b. You must be very specific (labeling slope and y intercept is not an acceptable interpretation).

d. Predict the mean monthly rent for an apartment that has 1000 square feet.

e. Using the printout from your regression equation (see Part 2 on the In Lab exercise regression analysis sheet) please determine if the slope coefficient is statistically different from zero.

f. Your friends Jim and Jennifer are considering signing a lease for an apartment in this residential neighborhood. They are trying to decide between two apartments, one with 1000 square feet for a monthly rent of $1,175 and the other with 1200 square feet for a monthly rent of $1525. Based on your statistical analysis above (parts a-e), what would you recommend to them? Explain why.

To complete question #1, you need to turn in the excel printout of the scatterplot, and the regression equation. Cutting and pasting these into a word document would be best.

2. (5 points) Suppose you were given from a data set the following information:

Mean of X	45
Mean of Y	200
Standard deviation of X	12
Standard deviation of Y	35
Correlation coefficient	.6

Calculate the regression line using the data above. (Find both the intercept and the slope).

Apartment	Size	Monthly Rent
	(in Square feet)	(in $)
1	850	950
2	1450	1600
3	1085	1200
4	1232	1500
5	718	950
6	1485	1700
7	1136	1650
8	726	935
9	700	875
10	956	1150
11	1100	1400
12	1285	1650
13	1985	2300
14	1369	1800
15	1175	1400
16	1225	1450
17	1245	1100
18	1259	1700
19	1150	1200
20	896	1150

Data for Question #1: