|
|
|
||||
|
|
|||||
Inputting the Kenton Food Company data, table 16.1, page 677.
clear input sales design store 11 1 1 17 1 2 16 1 3 14 1 4 15 1 5 12 2 1 10 2 2 15 2 3 19 2 4 11 2 5 23 3 1 20 3 2 18 3 3 17 3 4 27 4 1 33 4 2 22 4 3 26 4 4 28 4 5 end
Calculating the factor means and the grand mean.
tabstat sales, statistics(sum mean n) by(design)
Summary for variables: sales
by categories of: design
design | sum mean N
---------+------------------------------
1 | 73 14.6 5
2 | 67 13.4 5
3 | 78 19.5 4
4 | 136 27.2 5
---------+------------------------------
Total | 354 18.63158 19
----------------------------------------
Figure 16.3, page 677.
graph twoway scatter design sales, ylabel(0(1)4) xlabel(0(10)40)
Anova table for the Kenton Food data including tests, pages 683-686.
anova sales design
Number of obs = 19 R-squared = 0.7881
Root MSE = 3.24756 Adj R-squared = 0.7457
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 588.221053 3 196.073684 18.59 0.0000
|
design | 588.221053 3 196.073684 18.59 0.0000
|
Residual | 158.20 15 10.5466667
-----------+----------------------------------------------------
Total | 746.421053 18 41.4678363
Obtaining the residuals from the anova model of the Kenton Food data, table 16.2, page 680.
predict r, resid
list design store r
design store r
1. 1 1 -3.6
2. 1 2 2.4
3. 1 3 1.4
4. 1 4 -.6
5. 1 5 .4
6. 2 1 -1.4
7. 2 2 -3.4
8. 2 3 1.6
9. 2 4 5.6
10. 2 5 -2.4
11. 3 1 3.5
12. 3 2 .5
13. 3 3 -1.5
14. 3 4 -2.5
15. 4 1 -.2
16. 4 2 5.8
17. 4 3 -5.2
18. 4 4 -1.2
19. 4 5 .8
Calculating the treatment effect (taui) for the Incentive pay example, page 694.
di (70+58+90+84)/4 75.5 di 70-75.5 -5.5 di 58-75.5 -17.5 di 90-75.5 14.5 di 84-75.5 8.5
Applying deviation coding to the variable design in the Kenton Food Company data to be used in the regression approach, page 698. For more information on deviation and other coding schemes please see http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter5/statareg5.htm
gen x1=0
replace x1=1 if design==1
replace x1=-1 if design==4
(5 real changes made)
gen x2=0
replace x2=1 if design==2
replace x2=-1 if design==4
(5 real changes made)
gen x3=0
replace x3=1 if design==3
replace x3=-1 if design==4
(5 real changes made)
list sales design store x1-x3
sales design store x1 x2 x3
1. 11 1 1 1 0 0
2. 17 1 2 1 0 0
3. 16 1 3 1 0 0
4. 14 1 4 1 0 0
5. 15 1 5 1 0 0
6. 12 2 1 0 1 0
<output omitted>
18. 26 4 4 -1 -1 -1
19. 28 4 5 -1 -1 -1
reg sales x1 x2 x3
Source | SS df MS Number of obs = 19
-------------+------------------------------ F( 3, 15) = 18.59
Model | 588.221053 3 196.073684 Prob > F = 0.0000
Residual | 158.20 15 10.5466667 R-squared = 0.7881
-------------+------------------------------ Adj R-squared = 0.7457
Total | 746.421053 18 41.4678363 Root MSE = 3.2476
------------------------------------------------------------------------------
sales | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | -4.075 1.27081 -3.21 0.006 -6.783668 -1.366332
x2 | -5.275 1.27081 -4.15 0.001 -7.983668 -2.566332
x3 | .825 1.370629 0.60 0.556 -2.096427 3.746427
_cons | 18.675 .7485263 24.95 0.000 17.07955 20.27045
------------------------------------------------------------------------------
UCLA Researchers are invited to our Statistical Consulting Services
We recommend others to our list of Other Resources for Statistical Computing Help
These pages are Copyrighted (c) by UCLA Academic Technology Services