Regression analysis requires the following data:
1. *Dependent variable* (Outcome or Response variable): The variable being predicted or explained.
2. *Independent variables* (Predictor or Explanatory variables): The variables used to predict the dependent variable.
3. *Sample size*: A sufficient number of observations (data points) to ensure reliable estimates.
4. *Data type*: Quantitative data (numerical or categorical) for both dependent and independent variables.
5. *No missing values*: Complete data for all variables, or appropriately handled missing values.
6. *Normality*: Dependent variable should be normally distributed (or transformed to normality).
7. *Linearity*: Relationship between dependent and independent variables should be linear.
8. *Homoscedasticity*: Constant variance of residuals across all levels of independent variables.
9. *No multicollinearity*: Independent variables should not be highly correlated with each other.
10. *Random sampling*: Data should be collected through random sampling to ensure representativeness.
Note: The specific data requirements may vary depending on the type of regression analysis (e.g., linear, logistic, multiple) and the research question being addressed.
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