Click here to see a video on how multiple linear regression works using SPC for Excel

Our regression software, SPC for Excel, performs multiple linear regression. A small portion of the output is shown above. More examples of the output are given below. Multiple linear regression allows you to examine what independent variables (x) impact a response variable (y) and by how much. The output includes an in-depth analysis of residuals with potential outliers in red as well as multiple charts to analyze the results. You can easily remove independent variables as well as observations from the data and rerun the regression. You can also transform the y variables. A complete list of regression features is given below.

Multiple Linear Regression Features

• ANOVA for Model
• Variables Output
  • Coefficients
  • Standard error
  • t statistic, p value
  • 95% confidence limit
  • VIF
  • Standardized coefficients

• Regression statistics
  • R
  • R square
  • Adjusted R square
  • Mean, standard error
  • Coefficient of variation
  • Durbin-Watson statistic
  • PRESS
  • R square prediction

• Residuals analysis
  • Residuals
  • Leverage
  • Standardized residuals
  • Internally studentized residuals
  • Externally studentized residuals
  • DFFITS
  • Cook's distance
  • Standard error of estimated mean
  • 95% confidence limits of estimate means
  • Potential outliers in red

• Residuals charts (raw, standardized, internally or externally studentized residuals)
  • Normal plot
  • Versus predicted values
  • Versus observation number
  • Versus predictor variables
• Other charts
  • Predicted vs actual
  • DFFITS, Cook's distance and leverage versus observation number
• Remove variables
• Remove observations
• Transform y variables
• p values < 0.05 in red

Other Regression Output

Regression Statistics

Partial Output of Residual Analysis Worksheet

Normal Plot of Residuals

Externally Studentized Residuals versus Observation Number