This page shows an example mutiple regression analysis with footnotes explaining the output. The analysis uses a data file about scores obtained by elementary schools, predicting api00 from ell meals yr_rnd mobility acs_k3 acs_46 full emer and enroll using the following SAS commands.
proc reg data="c:sasregelemapi2" ; model api00 = ell meals yr_rnd mobility acs_k3 acs_46 full emer enroll ; run; Dependent Variable: api00 api 2000 Analysis of Variance Sum of Mean Sourcea DFb Squaresc Squared F Valuee Pr > Fe Model 9 6740702 748967 232.41 <.0001 Error 385 1240708 3222.61761 Corrected Total 394 7981410 Root MSEf 56.76810 R-Squarei 0.8446 Dependent Meang 648.65063 Adj R-Sqj 0.8409 Coeff Varh 8.75172 Parameter Estimates Parameter Standard Variablek Labell DFm Estimaten Erroro t Valuep Pr > |t|p Intercept Intercept 1 758.94179 62.28601 12.18 <.0001 ell english language learners 1 -0.86007 0.21063 -4.08 <.0001 meals pct free meals 1 -2.94822 0.17035 -17.31 <.0001 yr_rnd year round school 1 -19.88875 9.25844 -2.15 0.0323 mobility pct 1st year in school 1 -1.30135 0.43621 -2.98 0.0030 acs_k3 avg class size k-3 1 1.31870 2.25268 0.59 0.5586 acs_46 avg class size 4-6 1 2.03246 0.79832 2.55 0.0113 full pct full credential 1 0.60972 0.47582 1.28 0.2008 emer pct emer credential 1 -0.70662 0.60541 -1.17 0.2439 enroll number of students 1 -0.01216 0.01679 -0.72 0.4693
a. This is the source of variance, Model, Error, and Total. The Total Variance is partitioned into the variance which can be explained by the independent variables (Model) and the variance which is not explained by the independent variables (Error). Note that the Sums of Squares for the Model and Error add up to the Total Variance, reflecting the fact that the Total Variance is partitioned into Model and Error variance.
b. These are the degrees of freedom associated with the sources of variance. The Total variance has N-1 degrees of freedom (DF). In this case, there were N=395 observations, so the DF for total is 394. The Model degrees of freedom corresponds to the number of predictors minus 1 (K-1). You may think this would be 9-1 (since there were 9 independent variables in the model: ell, meals, yr_rnd, mobility, acs_k3, acs_46, full emer, and enroll). But, the intercept is automatically included in the model (unless you explicitly omit the intercept). Including the intercept, there are 10 predictors, so the model has 10-1=9 degrees of freedom. The Error degrees of freedom is the DFTotal minus the DFModel, 394 – 9 is 385.
c. These are
the Sum of Squares associated with the three sources of variance, Total,
Model and Error. These can be computed in many
ways. Conceptually, these formulas can be expressed as:
SSTotal: The total variability around the mean. S(Y – Ybar)2.
SSError: The sum of squared errors in prediction. S(Y – Ypredicted)2.
SSModel: The improvement in prediction by using the predicted value of Y over just using the mean of Y. Hence, this would be the squared differences between the predicted value of Y and the mean of Y, S(Ypredicted – Ybar)2. Another way to think of this is the SSModel is SSTotal – SSError. Note that the SSTotal = SSModel + SSError. Note that SSModel / SSTotal is equal to .84, the value of R-Square. This is because R-Square is the proportion of the variance explained by the independent variables, hence can be computed by SSModel / SSTotal.
d. These are the Mean Squares, the Sum of Squares divided by their respective DF. For the Model, 6740702.01 / 9 is equal to 748966.89. For the Error, 1240707.79 / 385 equals 3222.6176. These are computed so you can compute the F ratio, dividing the Mean Square Model by the Mean Square Error (or Error) to test the significance of the predictors in the model.
e. The F Value is the Mean Square Model (748966.89) divided by the Mean Square Error (3222.61761), yielding F=232.41. The p value associated with this F value is very small (0.0000). These values are used to answer the question “Do the independent variables reliably predict the dependent variable?”. The p value is compared to your alpha level (typically 0.05) and, if smaller, you can conclude “Yes, the independent variables reliably predict the dependent variable”. You could say that the group of variables ell, meals, yr_rnd, mobility, acs_k3, acs_46, full emer, and enroll can be used to reliably predict api00 (the dependent variable). If the p value were greater than 0.05, you would say that the group of independent variables do not show a significant relationship with the dependent variable, or that the group of independent variables do not reliably predict the dependent variable. Note that this is an overall significance test assessing whether the group of independent variables when used together reliably predict the dependent variable, and does not address the ability of any of the particular independent variables to predict the dependent variables. The ability of each individual independent variable to predict the dependent variable is addressed in the table below where each of the individual variables are listed.
h. Coeff Var. The coefficient of variation for the residuals is defined to be the root mean square error divided by the mean of the dependent variable. It is an indicator of how well the model fits the data. It is useful in comparing different models since it is unitless.
i. R-Square is the proportion of variance in the dependent variable (api00) which can be predicted from the independent variables (ell, meals, yr_rnd, mobility, acs_k3, acs_46, full emer, and enroll). This value indicates that 84% of the variance in api00 can be predicted from the variables ell, meals, yr_rnd, mobility, acs_k3, acs_46, full emer, and enroll. Note that this is an overall measure of the strength of association, and does not reflect the extent to which any particular independent variable is associated with the dependent variable.
R-square. As predictors are added to the model, each predictor will
explain some of the variance in the dependent variable simply due to
chance. One could continue to add predictors to the model which
would continue to improve the ability of the predictors to explain the
dependent variable, although some of this increase in R-square would be
simply due to chance variation in that particular sample. The
adjusted R-square attempts to yield a more honest value to estimate the
R-squared for the population. The value of R-square was .8446,
while the value of Adjusted R-square was .8409. Adjusted R-squared is
computed using the formula 1 – ( (1-R-sq)(N-1 / N – k – 1) ). From
this formula, you can see that when the number of observations is small
and the number of predictors is large, there will be a much greater
difference between R-square and adjusted R-square (because the ratio of
(N-1 / N – k – 1) will be much less than 1. By contrast, when the
number of observations is very large compared to the number of predictors,
the value of R-square and adjusted R-square will be much closer because
of (N-1)/(N-k-1) will approach 1.
Ypredicted = b0 + b1*x1 + b2*x2 + b3*x3 . . .
The column of parameter estimates provides the values for b0, b1, b2, b3, b4, b5, b6, b7, b8 and b9 for this equation. Expressed in terms of the variables used in this example, the regression equation is
api00Predicted = 778.83 -.86*ell -2.95*meals -19.89*yr_rnd -1.30*mobility +1.32*acs_k3 +2.03*acs_46 +.61*full -.71*emer -.01*enroll
These estimates tell you about the
relationship between the independent variables and the dependent
variable. These estimates tell the amount of increase in api00 that would be predicted by a 1 unit increase in the predictor.
Note: For the independent variables which are not significant, the
coefficients are not significantly different from 0, which should be taken
into account when interpreting the coefficients. (See the columns
with the t value and p value about testing whether the coefficients are
ell – The coefficient (parameter estimate) is -.86. So, for every unit increase in ell, a .86 unit decrease in api00 is predicted. Or, for every increase of one percentage point of api00, ell is predicted to be lower by .86. This is significantly different from 0.
meals – For every unit increase in meals, there is a 2.95 unit decrease in the predicted api00.
yr_rnd – For every unit increase of yr_rnd, the predicted value of api00 would be 19.89 units lower.
mobility – For every unit increase in mobility, api00 is predicted to be 1.30 units lower.
acs_k3 – For every unit increase in acs_k3, api00 is predicted to be 1.32 units higher.
acs_46 – For every unit increase in acs_46, api00 is predicted to be 2.03 units higher.
full – For every unit increase in full, api00 is predicted to be .61 unit higher.
emer – For every unit increase in emer, api00 is predicted to be .71 unit lower.
enroll – For every unit increase in enroll, api00 is predicted to be .01 unit lower.
o. These are the standard errors associated with the coefficients. The standard error is used for testing whether the parameter is significantly different from 0 by dividing the parameter estimate by the standard error to obtain a t value (see the column with t values and p values). The standard errors can also be used to form a confidence interval for the parameter.
columns provide the t value and 2 tailed p value used in testing the null
hypothesis that the coefficient/parameter is 0. If you use a 2
tailed test, then you would compare each p value to your preselected value
of alpha. Coefficients having p values less than alpha are
significant. For example, if you chose alpha to be 0.05,
coefficients having a p value of 0.05 or less would be statistically
significant (i.e. you can reject the null hypothesis and say that the
coefficient is significantly different from 0). If you use a 1
tailed test (i.e. you predict that the parameter will go in a particular
direction), then you can divide the p value by 2 before comparing it to
your preselected alpha level. With a 2 tailed test and alpha of
0.05, you can reject the null hypothesis that the coefficient for ell is equal to 0. The coefficient of
-.86 is significantly different from 0. Using a 2 tailed test and alpha of 0.01, the p value of
0.000 is smaller than 0.01 and the coefficient for ell would still be
significant at the 0.01 level. Had you predicted that this coefficient
would be positive (i.e. a one tail test), you would be able to divide the
p value by 2 before comparing it to alpha. This would yield a one
tailed p value of 0.000, which is less than 0.01 and then you could
conclude that this coefficient is greater than 0 with a one tailed alpha
The coefficient for meals is significantly different from 0 using alpha of 0.05 because its p value of 0.000 is smaller than 0.05.
The coefficient for yr_rnd (-19.89) is significantly different from 0 because its p value is smaller than 0.05.
The coefficient for mobility is significantly different from 0 using alpha of 0.05 because its p value of 0.003 is smaller than 0.05.
The coefficient for acs_k3 is not significantly different from 0 using alpha of 0.05 because its p value of .559 is greater than 0.05.
The coefficient for acs_46 is significantly different from 0 using alpha of 0.05 because its p value of 0.011 is smaller than 0.05.
The coefficient for full is not significantly different from 0 using alpha of 0.05 because its p value of .201 is greater than 0.05.
The coefficient for emer is not significantly different from 0 using alpha of 0.05 because its p value of .244 is greater than 0.05.
The coefficient for enroll is not significantly different from 0 using alpha of 0.05 because its p value of .469 is greater than 0.05.
The constant (_cons) is significantly different from 0 at the 0.05 alpha level. However, having a significant intercept is seldom interesting.