Key Differences Between Regression and ANOVA
Characteristic |
Regression |
ANOVA |
---|---|---|
Definition | A statistical technique to determine the relationship between a dependent variable and one or more independent variables. | A statistical technique to analyze the differences between group means in a sample. |
Variable Usage | Used with fixed (independent) variables | Used with group (explanatory) variables that have a random component. |
Types | Linear regression: One independent variable Multiple regression: Multiple independent variables |
Fixed-effects ANOVA: All groups are of interest. Random-effects ANOVA: Groups represent a random sample from a larger population. Mixed-effects ANOVA: Combination of fixed and random effects |
Purpose | Estimate or predict the dependent variable based on the independent variables. Understand the nature of the relationship between variables |
Identify if the group means are statistically different from each other. |
Assumptions | Linear relationship between independent and dependent variables. Normality of errors. Homoscedasticity (constant variance of errors) |
Normality of errors Homoscedasticity (constant variance of errors) |
Output | Regression equation: Shows the relationship between independent and dependent variables. Statistical significance: Indicates if the relationship is statistically noteworthy |
F-statistic: Tests the overall null hypothesis of no difference between group means. Post-hoc tests: Identify specific groups that differ from each other (if necessary) |
Strengths | Estimates and predicts the dependent variable. Understands the nature of the relationship between variables | Compares means across multiple groups. |
Weaknesses | Assumes linear relationship, Sensitive to outliers | Limited to comparing means, not individual data points. |
When to use Regression
- Regression is useful for tracking model performance, deploying, iterating, and other machine-learning operations in addition to training. It is one of the most widely used machine learning techniques.
- The categorical dependent variable is predicted using regression.
- It is employed in cases when the forecast is binary, such as true or false, 0 or 1, yes or no.
- When dealing with fixed or independent variables, regression is used.
When to use ANOVA
- ANOVA is utilised when comparing the means of three or more groups.
- One-way ANOVA may be used to determine the link between an independent variable and one quantitative dependent variable, which is useful for testing a specific hypothesis between groups. Analysing the effect of employee training on customer satisfaction scores is one example.
- When analysing variables with a random component, ANOVA is used.
Regression vs ANOVA
ANOVA and Regression have distinct objectives. Whereas regression employs a binary response variable to predict the category, ANOVA generates a continuous response variable to anticipate its value. In this article, let’s understand the difference between regression and ANOVA.
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