1. Standardized versus Unstandardized Regression Coefficients

2. Predicting % Body Fat Which predictor has the stronger effect? Model
Unstandardized
Coefficients B
Std. Error 1
(Constant)
11.967
Ankle_Inches
2.233
1.229
Age_Years
.541
.131
Which predictor has the stronger effect?

3. Which Predictor is Stronger?
With unstandardized coefficients, it makes no sense to compare inches with years. Standardizing solves that problem. Now both predictors are on the same metric – standard deviations.

4. Inches and Days
Unstandardized
Coefficients
Standardized B
Std. Error
Beta
(Constant)
11.967
Ankle_Inches
2.233
1.229
.220
Age_Days
.001
.000
.501
With the unstandardized slopes, the effect of age in days looks trivial, but it is not.

5. MM and Years Unstandardized Coefficients Standardized B Std. Error
Beta
(Constant)
11.967
Ankle_mm
.088
.048
.220
Age_Years
.541
.131
.501
Notice the great drop in the standardized slope for ankle, but its standardized slope remains the same.