Stat Computing > SAS > Textbook Examples > Applied Linear Statistical Models, by Neter, Kutner, Nachtsheim, and Wasserman

SAS Textbook Examples
Applied Linear Statistical Models by Neter, Kutner, et. al.
Chapter 1: Linear Regression with One Predictor Variable

options nodate nocenter;

Inputting the Toluca Company data.

data ch1tab01;
  input x y;
  label x = 'Lot Size'
        y = 'Work Hrs';
cards;
   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
;
run;

Table 1.1, p. 21.

proc sql;
  create table temp as
  select *, x - mean(x) as xdif, y - mean(y) as ydif, (x - mean(x))*( y - mean(y)) as crp,
           (x - mean(x))*(x - mean(x)) as sqdevx, (y - mean(y))*(y - mean(y)) as sqdevy 
  from ch1tab01;
quit;
proc print data = temp;
  var x y xdif ydif crp sqdevx sqdevy;
run;

Obs     x      y     xdif      ydif         crp    sqdevx     sqdevy

  1     80    399      10      86.72      867.2      100      7520.36
  2     30    121     -40    -191.28     7651.2     1600     36588.04
  3     50    221     -20     -91.28     1825.6      400      8332.04
  4     90    376      20      63.72     1274.4      400      4060.24
  5     70    361       0      48.72        0.0        0      2373.64
  6     60    224     -10     -88.28      882.8      100      7793.36
  7    120    546      50     233.72    11686.0     2500     54625.04
  8     80    352      10      39.72      397.2      100      1577.68
  9    100    353      30      40.72     1221.6      900      1658.12
 10     50    157     -20    -155.28     3105.6      400     24111.88
 11     40    160     -30    -152.28     4568.4      900     23189.20
 12     70    252       0     -60.28        0.0        0      3633.68
 13     90    389      20      76.72     1534.4      400      5885.96
 14     20    113     -50    -199.28     9964.0     2500     39712.52
 15    110    435      40     122.72     4908.8     1600     15060.20
 16    100    420      30     107.72     3231.6      900     11603.60
 17     30    212     -40    -100.28     4011.2     1600     10056.08
 18     50    268     -20     -44.28      885.6      400      1960.72
 19     90    377      20      64.72     1294.4      400      4188.68
 20    110    421      40     108.72     4348.8     1600     11820.04
 21     30    273     -40     -39.28     1571.2     1600      1542.92
 22     90    468      20     155.72     3114.4      400     24248.72
 23     40    244     -30     -68.28     2048.4      900      4662.16
 24     80    342      10      29.72      297.2      100       883.28
 25     70    323       0      10.72        0.0        0       114.92

Fig. 1.10a, p. 22.

symbol1 v=dot h=.8 c=blue;
proc gplot data = ch1tab01;
  plot y*x;
run;
quit;

Fig. 1.10b, p. 22.

proc reg data = ch1tab01;
  model y = x;
  plot y*x;
run;
quit;

Fig. 1.11, p.23.

The REG Procedure
Model: MODEL1
Dependent Variable: y Work Hrs

                             Analysis of Variance

                                    Sum of           Mean
Source                   DF        Squares         Square    F Value    Pr > F

Model                     1         252378         252378     105.88    <.0001
Error                    23          54825     2383.71562
Corrected Total          24         307203

Root MSE             48.82331    R-Square     0.8215
Dependent Mean      312.28000    Adj R-Sq     0.8138
Coeff Var            15.63447

                               Parameter Estimates

                                  Parameter       Standard
Variable     Label        DF       Estimate          Error    t Value    Pr > |t|

Intercept    Intercept     1       62.36586       26.17743       2.38      0.0259
x            Lot Size      1        3.57020        0.34697      10.29      <.0001

Obtaining the residuals and the squared residuals, table 1.2, p. 24.

proc reg data = ch1tab01 noprint;
  model y = x;
  output out=temp p=yhat r=residual;
run;
quit;
data temp1;
  set temp;
  rsq = residual**2;
run;
proc print data = temp1;
  var x y yhat residual rsq;
run;

Obs     x      y       yhat     residual         rsq

  1     80    399    347.982      51.018     2602.83
  2     30    121    169.472     -48.472     2349.53
  3     50    221    240.876     -19.876      395.05
  4     90    376    383.684      -7.684       59.04
  5     70    361    312.280      48.720     2373.64
  6     60    224    276.578     -52.578     2764.44
  7    120    546    490.790      55.210     3048.13
  8     80    352    347.982       4.018       16.14
  9    100    353    419.386     -66.386     4407.11
 10     50    157    240.876     -83.876     7035.18
 11     40    160    205.174     -45.174     2040.68
 12     70    252    312.280     -60.280     3633.68
 13     90    389    383.684       5.316       28.26
 14     20    113    133.770     -20.770      431.39
 15    110    435    455.088     -20.088      403.53
 16    100    420    419.386       0.614        0.38
 17     30    212    169.472      42.528     1808.64
 18     50    268    240.876      27.124      735.71
 19     90    377    383.684      -6.684       44.68
 20    110    421    455.088     -34.088     1162.00
 21     30    273    169.472     103.528    10718.06
 22     90    468    383.684      84.316     7109.18
 23     40    244    205.174      38.826     1507.46
 24     80    342    347.982      -5.982       35.78
 25     70    323    312.280      10.720      114.92

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