# 3.4.3.1 - Minitab: SLR

3.4.3.1 - Minitab: SLR

## Minitab® – Simple Linear Regression

We previously created a scatterplot of quiz averages and final exam scores and observed a linear relationship. Here, we will use quiz scores to predict final exam scores.

1. Open the Minitab file: Exam.mwx (or Exam.csv)
2. Select Stat > Regression > Regression > Fit Regression Model...
3. Double click Final in the box on the left to insert it into the Responses (Y) box on the right
4. Double click Quiz_Average in the box on the left to insert it into the Continuous Predictors (X) box on the right
5. Click OK

This should result in the following output:

#### Regression Equation

Final = 12.1 + 0.751 Quiz_Average

#### Coefficients

Term Coef SE Coef T-Value P-Value VIF
Constant 12.1 11.9 1.01 0.3153
Quiz_Average 0.751 0.141 5.31 0.000 1.00

#### Model Summary

9.71152 37.04% 35.73% 29.82%

#### Analysis of Variance

Regression 1 2664 2663.66 28.24 0.000
Quiz_Average 1 2664 2663.66 28.24 0.000
Error 48 4527 94.31
Total 49 7191

#### Fits and Diagnostics for Unusual Observations

Obs Final Fit Resid Std Resid
11 49.00 70.50 -21.50 -2.25 R
40 80.00 61.22 18.78 2.03 R
47 37.00 59.51 -22.51 -2.46 R

R Large residual

#### Interpretation

In the output in the above example we are given a simple linear regression model of Final = 12.1 + 0.751 Quiz_Average

This means that the y-intercept is 12.1 and the slope is 0.751.

 [1] Link ↥ Has Tooltip/Popover Toggleable Visibility