Stat 133 Recitation
2.3 and 2.4
Regression (Part 1)
We are comparing years of education and hours on the internet in the last month, to
see if a relationship exists. If a relationship does exist, we want to predict Internet use
using education level. The output is given below.
Descriptive Statistics: Education, Internet
Variable
Mean
StDev
Variance
Minimum
Maximum
Educatio
11.000
1.920
3.687
7.000
17.000
Internet
26.316
9.411
88.570
2.000
54.000
Pearson’s Correlation: 0.642
1. Interpret the correlation between Education and Internet use
2. Would a line fit this data well? Explain by interpreting the correlation.
3.
Define which variable is X and which one is Y.
4.
Find the slope of the best fitting line.
5. Find the Yintercept of the best fitting line.
6. Find the equation of the best fitting line.
7. Use the line to predict Internet use for someone with 16 years of education.
*8. For what education levels is it appropriate to use this line to make predictions? Use the
statistics given in the problem to answer this.
9. The linear regression of the computer output for the Internet/Education data is shown
below. Use this output to find the equation of the best fitting line and use it to verify your
answer to #6 above.
Predictor
Coef
SE Coef
T
P
Constant
8.290
2.665
3.11
0.002
Education
3.1460
0.2387
13.18
0.000
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View Full DocumentStat 133 Recitation
2.3 and 2.4
Data was collected on amount of rainfall (inches) and amount of corn produced
(bushels per acre) for a number of years in Kansas. The output is shown below.
Predictor
Coef
SE Coef
T
P
Constant
89.543
6.703
13.36
0.000
Rainfall
0.12800
0.01375
9.31
0.000
Correlation of Rainfall and Corn = 0.608
10. If rainfall increases by 1 inch, how much do you predict corn production will change
by?
a. 89.543 bushels per acre
b. 0.128 bushels per acre
c. 0.608 bushels per acre
d. None of the above or can’t tell without more information
11. How well does this slope to at making a good prediction about the change in corn
production for each additional inch of rain?
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
12. True or False: Because there is a minus sign on 90.88, that means the slope of the best
fitting line here is negative.
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 Fall '07
 rumseyjohnson
 Statistics, Econometrics, Linear Regression, Regression Analysis, Descriptive statistics, Errors and residuals in statistics

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