3.4.3.1 - SLR in Minitab Express

MinitabExpress  – 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 data set:
  2. On a PC or Mac: Select STATISTICS > Regression > Simple Regression
  3. Double click Final in the box on the left to insert it into the Response (Y) box on the right
  4. Double click Quiz_Average in the box on the left to insert it into the Predictor (X) box on the right
  5. Under the Graphs tab, click the box for Residual plots (We'll learn more about these in Lesson 10)
  6. Click OK

This should result in the following output:

Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 2663.66 2663.66 28.24 <0.0001
Error 48 4527.06 94.31    
Total 49 7190.72      
Model Summary
S R-sq R-sq(adj)
9.71152 37.04% 35.73%
Coefficients
Term Coef SE Coef T-Value P-Value
Constant 12.12 11.94 1.01 0.3153
Quiz_Average 0.7513 0.1414 5.31 <0.0001
Regression Equation

Final = 12.12 + 0.7513 Quiz_Average

Fits and Diagnostics for Unusual Observations
Obs Final Fit Resid Std Resid  
11 49 70.4975 -21.4975 -2.25 R
40 80 61.2158 18.7842 2.03 R
47 37 59.5050 -22.5050 -2.46 R

R Large residual

Video Walkthrough

Select your operating system below to see a step-by-step guide for this example.

In the output in the above example we are given a simple linear regression model of Final = 12.12 + 0.7513 Quiz_Average

This means that the y-intercept is 12.12 and the slope is 0.7513.