What Explanatory variable is
An explanatory variable, also known as an independent variable, is a variable that can be used to explain or predict the values of a dependent variable. It is a variable that is manipulated or changed in order to observe the effects on the dependent variable.
Steps for using explanatory variables:
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Identify the dependent variable and the independent variables. The dependent variable is the variable you are trying to explain or predict, and the independent variables are the variables that you think may explain or predict the dependent variable.
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Estimate the relationship between the dependent variable and the independent variables. This can be done using statistical methods such as linear regression, logistic regression, or other methods.
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Test the hypothesis that the independent variable explains or predicts the dependent variable. This can be done using statistical tests such as t-tests, chi-square tests, or other tests.
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Interpret the results. Look at the results of the tests and see if the independent variable does indeed explain or predict the dependent variable. If so, then you can conclude that the independent variable is an explanatory variable.
Examples
- In a study of student achievement, explanatory variables might include gender, socio-economic status, and home environment.
- In a study of heart disease, explanatory variables might include age, family history, cholesterol levels, and smoking status.
- In a study of stock market performance, explanatory variables might include industry sector, economic conditions, and investor sentiment.