Briefly describe the difference between correlation and regression.
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.
Briefly describe the difference between
Correlation:
A correlation or correlation coefficient is a numerical value that measures and describes the relationship between two variables. The sign of correlation (+/-) indicates the direction of the relationship. "+" indicates a positive correlation and "-" indicates a negative correlation. The numerical value of the relation (from 0 to 1) indicates the strength of the relationship. The type of correlation indicates the form of the relationship. It will be zero when there is no relationship between the two variables.
If there is tendency for two variables to change in the same direction, as one variable increases, the other variable also tends to increase and vice versa then it is a positive relationship. If there is a tendency for two variables to change in opposite direction, increase in one variable tend to be accompanied by decrease in the other then it is called as a negative relationship.
Regression:
Regression analysis estimates the relationship among variables. That is, it estimates the relationship between one dependent variable and one or more independent variables.
The general form of first-order regression model is y-cap = β 0 + β 1 x + ε, Where, the variable y is the dependent variable that is to be modelled or predicted, the variable x is the independent variable that is used to predict the dependent variable, and ε is the error term.
Multiple regression:
A multiple regression analysis is used when two or more independent variables are used to predict a value of a single dependent variable.
Step by step
Solved in 2 steps
