An APAStyle Results Section for Multiple Regression 
A multiple regression analysis was conducted to evaluate how well the strength
measures predicted physical injury level. The predictors were the five strength
indices, while the criterion variable was the overall injury index. The linear
combination of strength measures was significantly related to the injury index,
F(5, 94) = 4.18, p < .01. The sample multiple correlation
coefficient was .43, indicating that approximately 18% of the variance of the
injury index in the sample can be accounted for by the linear combination of
strength measures.
In Table 1, indices to indicate the relative strength of
the individual predictors are presented. All the bivariate correlations between
the strength measures and the injury index were negative, as expected, although
only four of the five indices were statistically significant (p < .05).
Only the partial correlation between the strength measure for the
gluteus/hamstring muscles and the injury index was significant. On the basis of
these correlational analyses, it is tempting to conclude that the only useful
predictor is the strength measure for the gluteus/hamstring muscles. It alone
accounts for 15% (.392 = .15) of the variance of the injury index, while the
other variables contribute only an additional 3% (18%  15% = 3%).
Results for Two Unordered Sets of Predictors
Two multiple regression analyses were conducted to predict the overall injury
index. One analysis included the three lowerbody strength measures as
predictors (quadriceps, gluteus/hamstrings, and the abdominal/lower back), while
the second analysis included the two upperbody strength measures
(arms/shoulders and grip strength). The regression equation with the lowerbody
measures was significant, R^{2} = .16, adjusted R^{2}
= .13, F (3, 96) = 6.07, p < .001. However, the regression
equation with the upperbody measures was not significant, R^{2}
= .06, adjusted R^{2 }= .04, F(2, 97) = 3.06, p =
.051. Based on these results, the lowerbody measures appear to be better
predictors of the injury index.
Next, a multiple regression analysis was conducted with all five strength measures as predictors. The linear combination of the five strength measures was significantly related to the injury index, R^{2} = .18, adjusted R^{2} = .14, F(5, 94) = 4.18, p < .005. The lowerbody strength measures predicted significantly over and above the upperbody measures, R^{2}change = .12, F(3, 94) = 4.69, p < .001, but the upperbody strength measures did not predict significantly over and above the lowerbody measures, R^{2} change = .02, F(2, 94) = 1.29, NS. Based on these results, the upperbody measures appear to offer little additional predictive power beyond that contributed by a knowledge of lowerbody measures.
Of the lowerbody strength measures, the strength measure for the gluteus/hamstring muscles was most strongly related to the injury index. Supporting this conclusion is the strength of the bivariate correlation between the gluteus/hamstring measure and the injury index, which was  .39, p < .001, as well as the comparable correlation partialling out the effects of the other two lowerbody measures, which was .33, p < .005.
Results for Two Ordered Sets of Predictors
A multiple regression analysis was conducted to predict the overall injury index
from previous medical difficulties and age. The results of this analysis
indicated that medical difficulties and age accounted for a significant amount
of the injury variability, R^{2} = .16, F(2, 97) = 9.35,
p <.001, indicating that older women who had more medical problems tended
to have higher scores on the overall injury index.
A second analysis was conducted to evaluate whether the strength measures predicted injury over and above previous medical difficulties and age. The five strength measures accounted for a significant proportion of the injury variance after controlling for the effects of medical history and age, R^{2} change = .15, F(5, 92) = 3.97, p < .005. These results suggest that women who have similar medical histories and are the same age are less likely to have injuries if they are stronger.
Tips for Writing an APA Results Section
for Multiple Regression
Here are some guidelines for writing a results section for multiple regression
analyses. Some researchers initially provide a description of the general
overall analytic strategy that includes different regression analyses conducted
to answer the research questions. This general description is necessary to the
degree that the analyses are unconventional, complex or both.
The steps required to write a results section:
(1)  Describe the test or tests, the
variables, and the purpose of the test or tests.


(2)  Report the descriptive statistics
(e.g., means, standard deviations, and bivariate correlations). These
statistics may be reported prior to the presentation of the multiple
regression analyses or in conjunction with them (e.g., along with the
standardized beta weights).


(3)  Report the results of the overall
regression analyses. If several analyses have been conducted, report the
results for each one separately.


(4)  Describe and summarize the general
conclusions of the overall analysis. For example: “The multiple regression results suggest that women who have similar medical histories and are the same age are less likely to have injuries if they are stronger.” 

(5)  Report the contributions of the
individual predictors.


(6)  Present the results graphically, if possible. You may present graphs to show relationships among variables. The graph should be inserted in the text where appropriate. For example, if the graph pertains to assumptions, you would insert it in the section where you discuss assumptions. 
Last update: April 19, 2004