Only moderately well since r is 0.608.
Suppose that the price (in $thousands) and size (in square feet) of a random sample of
houses in Viroqua, Wisconsin was analyzed by a new statistician using Minitab.
The
group plans to us the data to help set prices for homes based on their size.
Descriptive Statistics: Size
Variable
N
N*
Mean
SE Mean
StDev
Variance
Minimum
Q1
Median
Q3
Size
10
0
1993
110
349
121907
1526
1699
1957
2271
Variable
Maximum
Size
2595
Descriptive Statistics: Price
Variable
N
N*
Mean
SE Mean
StDev
Variance
Minimum
Q1
Median
Price
10
0
219.1
19.0
60.1
3612.1
148.0
163.5
200.5
Variable
Q3
Maximum
Price
269.8
315.0
Correlation: 0.9041
Regression Analysis
Predictor
Coef
SE Coef
T
Pvalue
Constant
90.88
52.62
1.73
0.12
Size
0.15556
0.02605
5.97
0.00
2
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document12. True or False: Because there is a minus sign on 90.88, that means the slope of the best
fitting line here is negative.
False; this is the sign on the Yintercept, not the sign for slope.
13. Based on the above output, a scatterplot of this data set that will be used for prediction
purposes would have which variable on which axis?
a.
Price on X axis, Size on Y axis
b.
Size on X axis, Price on Y axis
c.
It doesn’t matter. The regression line won’t change if you switched X and Y
d.
“Size” variable on the X axis and “Constant” variable on the Y axis
14. What is the equation of the bestfitting regression line?
Find this in two ways. First,
using the regression analysis part of the output, and then using the descriptive statistics part
of the output.
Equation either way should be Price
(in thousands) = 90.88 + 0.156 Size (in sq feet)
15. For each square foot increase in house size, how much does the price increase (in
dollars) on average? Explain your answer.
0.156 is the slope, which is change in price (thousands) per one more square foot of
house. So one more square foot costs 0.156 thousand dollars. Or, one more square foot
costs 0.156 x 1,000 = 156 dollar increase in price.
16.
Does the Yintercept have an interpretation here? Why or why not?
No since you also would never have a house with X = 0 square feet, so you wouldn’t
plug X=0 into this equation and expect to interpret Y. (Which is a good thing because
the Yintercept turns out to be 90.88 which you cannot interpret in this problem.)
Let x be the change in a stock market index in January and let y be the change in the
stock market index for the entire year. Descriptive statistics from 1960 to 1997 are shown
below.
Descriptive Statistics: Jan, Year
Total
Variable
Count
Mean
StDev
Minimum
Median
Maximum
Jan
37
0.018
0.016
0.002
0.020
0.200
Year
37
0.091
0.010
0.520
0.101
0.393
Pearson correlation of Jan and Year = 0.796
PValue = 0.895
17. You want to use the January change in stock market index (x) to predict the change in
the stock market index for the entire year (y). Find the equation of the bestfitting line.
Slope
This is the end of the preview.
Sign up
to
access the rest of the document.
 Fall '11
 Johnson
 Statistics, Econometrics, Linear Regression, Regression Analysis, Descriptive statistics, Errors and residuals in statistics

Click to edit the document details