What problems might we encounter when using regression to measure causal effects?
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
What problems might we encounter when using regression to measure causal effects?
Given:
Regression is the Statistical method of detecting exact relationship between dependent variable and independent variable(or variables) i.e. it measures how changing independent variable(or variables) affects the dependent variable and quantifies the actual relationship between two using correlation coefficient.
Regression cares about correlation relationships but correlation does not necessary mean causation but causation implies correlation. For eg, Age and shoe size are correlated but age does not causes increase in shoe size. Causal analysis tries to estimate the effect of interventions and measures how X affects Y. A cause is the variable that produces an event or condition and effect is that variable which is result of that event.
Step by step
Solved in 2 steps
