• This slide deck gives a few examples of how you can approach reporting the results of a regression.

• But remember to always check journal guidelines for specific requirements.

## 
## Call:
## lm(formula = Y ~ X, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.042 -2.528 -1.076  3.466 10.059 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.21098    4.00823   1.051    0.302    
## X            0.43298    0.07943   5.451  8.1e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.197 on 28 degrees of freedom
## Multiple R-squared:  0.5148, Adjusted R-squared:  0.4975 
## F-statistic: 29.71 on 1 and 28 DF,  p-value: 8.099e-06

A simple linear regression was conducted to predict Y from X. The model was significant, \(F(1, 28) = 29.71\), \(p < .001\), and explained approximately 51 % of the variance in Y (\(R^2 = 0.51\)). X was a significant predictor of Y (\(\beta = 0.43\), \(p < .001\)).”

## 
## Call:
## lm(formula = Y ~ X1 + X2, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.7968 -2.2966 -0.8143  2.0634 10.5008 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -22.45098    8.81960  -2.546 0.016932 *  
## X1            0.45888    0.07923   5.792 3.67e-06 ***
## X2            0.91491    0.21752   4.206 0.000256 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.75 on 27 degrees of freedom
## Multiple R-squared:  0.6033, Adjusted R-squared:  0.5739 
## F-statistic: 20.53 on 2 and 27 DF,  p-value: 3.8e-06

• “A multiple regression was conducted to predict Y from X1 and X2. The overall model was significant, \(F(2, 27) = 20.53\), \(p < .001\), explaining approximately 60% of the variance in Y (\(R^2 = 0.6\)). X1 (\(\beta = 0.46\), \(p < .001\)) and X2 (\(\beta = 0.91\), \(p < .001\)) were both significant predictors of Y.”