What statistical tool should be used to determine the influence of independent variable to dependent variable?
The statistical tool commonly used to determine the influence of an independent variable on a dependent variable is called regression analysis. Regression analysis is a statistical technique that explores the relationship between two or more variables, specifically the relationship between a dependent variable and one or more independent variables.
To perform regression analysis, you will typically need a dataset that contains observations of both the dependent and independent variables. The choice of regression model depends on the type of data and the nature of the relationship you are trying to examine.
There are several types of regression analysis you can consider, such as:
1. Simple Linear Regression: This is used when you have a single independent variable and want to model a linear relationship between that variable and the dependent variable.
2. Multiple Linear Regression: This is used when you have multiple independent variables and want to model a linear relationship between all the variables and the dependent variable.
3. Polynomial Regression: This is used when the relationship between the independent and dependent variables is non-linear and can be better approximated by a polynomial function.
4. Logistic Regression: This is used when the dependent variable is binary or categorical, and you want to model the probability of a certain outcome based on the independent variables.
Once you have chosen the appropriate regression model, you can use statistical software such as R, Python (with libraries like sklearn or statsmodels), or Excel to estimate the regression coefficients, perform hypothesis tests, and assess the overall fit of the model. Interpretation of the regression output can help you determine the influence of the independent variable(s) on the dependent variable, such as the direction and magnitude of the relationship, statistical significance, and predictive power.