Example:  Wages vs Length of Service and Size of Company

Coding of size of company:  small = 0  large = 1

Regression Analysis: Wages versus LOS, size, LOS*size

The regression equation is
Wages = 35.9 + 0.104 LOS + 13.6 size - 0.0483 LOS*size

Predictor      Coef  SE Coef      T      P
Constant     35.914    3.562  10.08  0.000
LOS         0.10424  0.03632   2.87  0.006
size         13.631    4.910   2.78  0.007
LOS*size   -0.04828  0.05634  -0.86  0.395

S = 10.9612   R-Sq = 26.6%   R-Sq(adj) = 22.7%

Analysis of Variance
Source          DF      SS     MS     F      P
Regression       3  2438.1  812.7  6.76  0.001
Residual Error  56  6728.3  120.1
Total           59  9166.4

Source    DF  Seq SS
LOS        1   843.5
size       1  1506.3
LOS*size   1    88.2

Regression Analysis: Wages versus LOS, size

The regression equation is
Wages = 37.5 + 0.0842 LOS + 10.2 size

Predictor     Coef  SE Coef      T      P
Constant    37.466    3.061  12.24  0.000
LOS        0.08417  0.02770   3.04  0.004
size        10.228    2.882   3.55  0.001

S = 10.9357   R-Sq = 25.6%   R-Sq(adj) = 23.0%

Analysis of Variance
Source          DF      SS      MS     F      P
Regression       2  2349.9  1174.9  9.82  0.000
Residual Error  57  6816.6   119.6
Total           59  9166.4

Source  DF  Seq SS
LOS      1   843.5
size     1  1506.3

Unusual Observations
Obs  LOS  Wages    Fit  SE Fit  Residual  St Resid
15   70  97.68  53.59    1.85     44.09      4.09R
22  222  54.95  56.15    4.57     -1.21     -0.12 X
29   98  34.34  55.94    2.05    -21.60     -2.01R
42  228  67.91  56.66    4.71     11.25      1.14 X
47  204  50.17  64.87    4.26    -14.69     -1.46 X

R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.