Minitab and Simple Linear Regression
Steps to input data into Minitab:
1. Enter x values in one column and the
corresponding y values in the adjacent column.
2. Full the pull-down menu, select Stat, then
Regression, then Regression again.
3. Enter the correct column names for the response
and predictor variables, then click OK.
4. In order to get a plot of the data with the best fit
line, select Stat, then Regression, then Fitted Line
Plot and enter the correct variables as above. Note:
When you choose this option, you will see the
output of the ANOVA table, but will not see the
summary stats for standard errors.
Example #2 (from lecture notes)
Regression Analysis: Score versus Age
The regression equation is
Score = 110 - 1.13 Age
Predictor
Constant
Age
Coef
109.874
-1.1270
S = 11.0229
SE Coef
5.068
0.3102
R-Sq = 41.0%
T
21.68
-3.63
P
0.000
0.002
R-Sq(adj) = 37.9%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
19
20
SS
1604.1
2308.6
3912.7
MS
1604.1
121.5
F
13.20
P
0.002
Exercise 10.5
Regression Analysis: ln(bits) versus Year
The regression equation is
ln(bits) = - 873 + 0.446 Year
Predictor
Constant
Year
Coef
-872.93
0.446390
S = 0.173951
SE Coef
13.64
0.006858
R-Sq = 99.9%
T
-64.01
65.09
P
0.000
0.000
R-Sq(adj) = 99.9%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
4
5
SS
128.19
0.12
128.31
MS
128.19
0.03
F
4236.55
P
0.000
Exercise 10.12
Regression Analysis: gas versus deg-days
The regression equation is
gas = 1.23 + 0.202 deg-days
Predictor
Constant
deg-days
Coef
1.2324
0.20221
S = 0.434537
SE Coef
0.2860
0.01145
R-Sq = 97.8%
T
4.31
17.66
P
0.004
0.000
R-Sq(adj) = 97.5%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
7
8
SS
58.907
1.322
60.229
MS
58.907
0.189
F
311.97
P
0.000
Unusual Observations
Obs
1
deg-days
15.6
gas
5.200
Fit
4.387
SE Fit
0.160
Residual
0.813
St Resid
2.01R
R denotes an observation with a large standardized residual.