Total cholesterol level = a + b(Caloric intake) + c(physical activity) + d(BMI); is an example of:
|A||Simple linear regression|
|B||Simple curvilinear regression|
|C||Multiple linear regression|
|D||Multiple logistic regression|
- Regression analysis is used to predict the value of one variable (dependent variable) from the knowledge of the other (independent variable).
- The regression equation is: Y = a + b X, where ‘Y’ = dependent variable, ‘X’ = independent variable, ‘a’ = y-axis intercept and ‘b’ = regression coefficient.
When there is a sufficient degree of correlation to believe the results approximate to a straight line, it is the regression co-efficient, which is used to measure its slope.
Regression co-efficient can be of any magnitude, therefore and does not have a maximum value like a correlation co-efficient of 1.
Types of regression:
Based on the number of independent variables
a. Simple: only 1 independent variable is used to calculate the value of dependent variable
b. Multiple regression: While multiple linear regression relates more than one independent explanatory or predictor variable used to calculate the value of dependent variable , as in our question, where total cholesterol level relates to more than one independent variables i.e. calorie intake, physical activity and BMI.
Based on the type of dependent and independent variable:
a. Linear regression: Both dependent and independent variables are quantitative in nature
b. Logistic regression:Dependent variable is qualitative or dichotomous and independent variable is also qualitative or quantitative or a mix of the two.
c. Curvilinear regression: Curvilinear regression deals with the problem of predicting the most likely factor for a single particular variable.