Statistics add-in software for statistical analysis in Excel
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Fitting a multiple linear regression

Fit a multiple linear regression model to describe the relationship between many quantitative predictor variables and a response variable.

  1. Select a cell in the dataset.
  2. On the Analyse-it ribbon tab, in the Statistical Analyses group, click Fit Model, and then click Multiple Regression.
    The analysis task pane opens.
  3. In the Y drop-down list, select the response variable.
  4. In the Available variables list, select the predictor variables:
    • To select a single variable, click the variable.
    • To select multiple variables, click the first variable then hold down the CTRL key and click each additional variable.
    • To select a range of variables, click the first variable then hold down the SHIFT key and click the last variable in the range.
  5. Click Add Predictor.
  6. Repeat steps 4 through 5 for each additional set of variables.
  7. Optional: To assign a weight to each row for the analysis, in the Weight drop-down list, select the weight variable.
  8. Click Calculate.
Related concepts
Summary of fit
Parameter estimates
Effect of model hypothesis test
Residual plot
Related tasks
Plotting residuals
Identifying outliers and other influential points
Making predictions
Saving variables
Related reference
Dataset layout
Advanced models
Available in Analyse-it Editions
Standard edition
Method Validation edition
Quality Control & Improvement edition
Ultimate edition

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  •  Linear fit
  •  Simple regression models
  •  Fitting a simple linear regression
  •  Advanced models
  •  Fitting a multiple linear regression
  •  Performing ANOVA
  •  Performing 2-way or higher factorial ANOVA
  •  Performing ANCOVA
  •  Fitting an advanced linear model
  •  Scatter plot
  •  Summary of fit
  •  Parameter estimates
  •  Effect of model hypothesis test
  •  ANOVA table
  •  Predicted against actual Y plot
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  •  Effect leverage plot
  •  Effect means
  •  Plotting main effects and interactions
  •  Multiple comparisons
  •  Multiple comparison procedures
  •  Comparing effect means
  •  Residual plot
  •  Residuals - normality
  •  Residuals - independence
  •  Plotting residuals
  •  Outlier and influence plot
  •  Identifying outliers and other influential points
  •  Prediction
  •  Making predictions
  •  Making inverse predictions
  •  Saving variables
  •  Logistic / Probit fit
  •  Study design
  •  Method comparison / Agreement
  •  Measurement systems analysis (MSA)
  •  Reference interval
  •  Diagnostic performance
  •  Survival/Reliability
  •  Control charts
  •  Process capability
  •  Pareto analysis
  •  Study Designs
  •  Bibliography



Version 6.15
Published 18-Apr-2023
statistics software, statistical software for Excel
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