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Inputting the Toluca Company data.
input x y 80 399 30 121 50 221 90 376 70 361 60 224 120 546 80 352 100 353 50 157 40 160 70 252 90 389 20 113 110 435 100 420 30 212 50 268 90 377 110 421 30 273 90 468 40 244 80 342 70 323 end label var x "lot size" label var y "work hrs" save toluca
Table 1.1, page 21.
quietly summarize x
generate xdif = x - r(mean)
quietly summarize y
generate ydif = y - r(mean)
generate crp = ydif*xdif
generate xdif2 = xdif^2
generate ydif2 = ydif^2
list
x y xdif ydif crp xdif2 ydif2
1. 80 399 10 86.72 867.2 100 7520.358
2. 30 121 -40 -191.28 7651.2 1600 36588.04
3. 50 221 -20 -91.28 1825.6 400 8332.038
4. 90 376 20 63.72 1274.4 400 4060.239
5. 70 361 0 48.72 0 0 2373.638
6. 60 224 -10 -88.28 882.8 100 7793.358
7. 120 546 50 233.72 11686 2500 54625.04
8. 80 352 10 39.72 397.2 100 1577.678
9. 100 353 30 40.72 1221.6 900 1658.119
10. 50 157 -20 -155.28 3105.6 400 24111.88
11. 40 160 -30 -152.28 4568.4 900 23189.2
12. 70 252 0 -60.28 0 0 3633.678
13. 90 389 20 76.72 1534.4 400 5885.958
14. 20 113 -50 -199.28 9964 2500 39712.52
15. 110 435 40 122.72 4908.8 1600 15060.2
16. 100 420 30 107.72 3231.6 900 11603.6
17. 30 212 -40 -100.28 4011.2 1600 10056.08
18. 50 268 -20 -44.28 885.6 400 1960.718
19. 90 377 20 64.72 1294.4 400 4188.679
20. 110 421 40 108.72 4348.8 1600 11820.04
21. 30 273 -40 -39.28 1571.2 1600 1542.918
22. 90 468 20 155.72 3114.4 400 24248.72
23. 40 244 -30 -68.28 2048.4 900 4662.158
24. 80 342 10 29.72 297.2 100 883.2784
25. 70 323 0 10.72 0 0 114.9184
Figure 1.10, page 22.
graph twoway scatter y x, xlabel(0(50)150) ylabel(0(100)600)
graph twoway (scatter y x) (lfit y x), xlabel(0(50)150) ylabel(0(100)600)
Fig. 1.11, page 23.
regress y x
Source | SS df MS Number of obs = 25
-------------+------------------------------ F( 1, 23) = 105.88
Model | 252377.581 1 252377.581 Prob > F = 0.0000
Residual | 54825.4592 23 2383.71562 R-squared = 0.8215
-------------+------------------------------ Adj R-squared = 0.8138
Total | 307203.04 24 12800.1267 Root MSE = 48.823
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x | 3.570202 .3469722 10.29 0.000 2.852435 4.287969
_cons | 62.36586 26.17743 2.38 0.026 8.213711 116.518
------------------------------------------------------------------------------
Table 1.2, page 24.
predict yhat
predict res, residual
generate res2 = res^2
list x y yhat res res2
x y yhat res res2
1. 80 399 347.982 51.01798 2602.834
2. 30 121 169.4719 -48.47192 2349.527
3. 50 221 240.876 -19.87596 395.0538
4. 90 376 383.6841 -7.684041 59.04448
5. 70 361 312.28 48.72 2373.638
6. 60 224 276.578 -52.57798 2764.444
7. 120 546 490.7901 55.2099 3048.133
8. 80 352 347.982 4.01798 16.14416
9. 100 353 419.386 -66.38606 4407.109
10. 50 157 240.876 -83.87596 7035.177
11. 40 160 205.1739 -45.17394 2040.685
12. 70 252 312.28 -60.28 3633.678
13. 90 389 383.6841 5.315959 28.25942
14. 20 113 133.7699 -20.7699 431.3887
15. 110 435 455.0881 -20.08808 403.531
16. 100 420 419.386 .6139394 .3769216
17. 30 212 169.4719 42.52808 1808.638
18. 50 268 240.876 27.12404 735.7136
19. 90 377 383.6841 -6.684041 44.6764
20. 110 421 455.0881 -34.08808 1161.997
21. 30 273 169.4719 103.5281 10718.06
22. 90 468 383.6841 84.31596 7109.181
23. 40 244 205.1739 38.82606 1507.463
24. 80 342 347.982 -5.98202 35.78457
25. 70 323 312.28 10.72 114.9184
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