- Use Minitab to carry out the multiple regression on depression scores
that was used as an example in the
Check that you get the same result as was reported then. The data and results
are reproduced at the end of this examples sheet.
- Use Minitab's HELP facility to find out how to use the READ command
to read data into Minitab from a text file.
- The Singer file /singer1/eps/psybin/stats/teengamb.DAT contains, for
each of 47 teenagers, the following information:
- respondent number
- gender (0=male, 1=female)
- status (arbitrary scale based on parents' occupation. Higher numbers
=> higher status)
- income (pocket money+earnings) in pounds/wk
- verbal intelligence (number of words out of 12 correctly defined)
- estimate (from questionnaire answers) of expenditure on all forms of
gambling, in pounds/yr
Each line of the file contains all 6 data items for a single person.
These are real data, collected during an undergraduate project some years
ago, and since published (Ide-Smith & Lea, 1988, Journal of Gambling
Behavior, 4, 110-118). Set up your Minitab worksheet with columns with
appropriate names, and read these data into it using READ. Note that you
don't need to type the file extension (.DAT) because this is the default
for READ, but if you do type it, you must use CAPITALS. The rest of the
filename must be typed in lower case.
Use Minitab's DESCRIBE command to get an overview of these data.
Use multiple regression to see whether gambling can be predicted from
status, income, and verbal intelligence.
How good is the prediction?
Which variables have significant effects?
Give a description in words, as precise as you can make it, of the
most significant effect.
Use HELP to remind yourself what Minitab's TWOT command does and how
Use TWOT to find out how gambling seems to have been affected by gender
in this sample when all other variables are ignored.
Repeat the multiple regression, but this time include gender as a predictor
variable in the REGRESS command. This will allow you to assess the effect
of gender with the other variables taken into account. Compare the results
with those you got when you used TWOT.
Using this second regression equation, predict how much money you would
have been expected to spend on gambling when you were 14. Is the prediction
accurate? If not, why do you think it fails?
How would you have analysed these data if you hadn't had multiple regression
The published article is in the library, but you won't get quite the
same results as we did, because I've cut out the data from some respondents
who would have given you problems.
Worked example of an elementary
MTB > set c1
DATA> 74 82 15 23 35 54 12 28 66 43 55 31 83 29 53 32
MTB > set c2
DATA> 120 55 350 210 185 110 730 150 61 175 121 225 45 325 171 103
MTB > set c3
DATA> 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1
MTB > set c4
DATA> 33 28 47 55 32 63 59 68 27 32 42 51 47 33 51 20
MTB > name c1 'depress'
MTB > name c2 'income'
MTB > name c3 'm0f1'
MTB > name c4 'age'
MTB > regress c1 3 c2-c4
The regression equation is
depress = 68.3 0.0934 income + 3.31 m0f1 - 0.162 age
Predictor Coef Stdev t-ratio p
Constant 68.28 15.44 4.42 0.001
income -0.09336 0.02937 -3.18 0.008
m0f1 3.306 8.942 0.37 0.718
age -0.1617 0.3436 -0.47 0.646
s = 17.70 R= 52.0% R= 39.9%
Analysis of Variance
SOURCE DF SS MS F p
Regression 3 4065.4 1355.1 4.32 0.028
Error 12 3760.0 313.3
Total 15 7825.4
SOURCE DF SEQ SS
income 1 3940.5
m0f1 1 55.5
age 1 69.4
Obs. income depress Fit Stdev.Fit Residual St.Resid
7 730 12.00 -6.10 15.57 18.10 2.15RX
R denotes an obs. with a large st. resid.
X denotes an obs. whose X value gives it large influence.
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Document revised 27th August 1997