Regression Analysis Sample Size Calculator
Introduction & Importance
Regression analysis is a fundamental statistical technique used to understand the relationship between a dependent variable and one or more independent variables. The sample size calculator is an essential tool for determining the minimum number of observations required to achieve a desired level of statistical power in a regression analysis.
How to Use This Calculator
- Select the desired significance level (α) from the dropdown menu.
- Enter the desired power (1 – β) in the input field. A common choice is 0.8 or 0.9.
- Enter the expected effect size (f) in the input field. This can be estimated from previous studies or domain knowledge.
- Click the “Calculate” button to determine the minimum sample size required for your analysis.
Formula & Methodology
The sample size calculator uses the formula for the power analysis of multiple regression, which is based on the work of Cohen (1988). The formula is as follows:
Real-World Examples
Data & Statistics
| Effect Size (f) | Sample Size (n) |
|---|---|
| 0.2 | 128 |
| 0.5 | 32 |
| 0.8 | 16 |
Expert Tips
- Always round up the calculated sample size to the nearest whole number.
- Consider collecting more data than the calculated minimum to account for potential data loss or outliers.
- Regularly review and update your sample size calculation as new data becomes available.
Interactive FAQ
What is the difference between significance level (α) and power (1 – β)?
The significance level (α) is the probability of rejecting the null hypothesis when it is true. Power (1 – β) is the probability of detecting an effect when there is one. In other words, α is about avoiding false positives, while power is about avoiding false negatives.