AIOU SOLVED ASSIGNMENT 2 CODE 395 AUTUMN 2016 BUSINESS STATISTICS – 2 REGRESSION
Differentiate between regression and correlation problems giving examples.
Correlation and linear regression are the most commonly used techniques for investigating the relationship between two quantitative variables.
The goal of a correlation analysis is to see whether two measurement variables co vary, and to quantify the strength of the relationship between the variables whereas regression expresses the relationship in the form of an equation. For example, in students taking a Maths and English test, we could use correlation to determine whether students who are good at Math’s tend to be good at English as well, and regression to determine whether the marks in English can be predicated for given marks in Math’s.
AIOU CODE 395 SOLVED ASSIGNMENT 2 FOR AUTUMN 2016
In correlation analysis, we estimate a sample correlation coefficient, more specifically the Pearson Product Moment correlation coefficient. The sample correlation coefficient, denoted r, ranges between -1 and +1 and quantifies the direction and strength of the linear association between the two variable. The correlation between two variables can be positive (i.e., higher levels of one variable are associated with higher levels of the other) or negative (i.e., higher levels of one variable are associated with lower levels of the other).
AIOU SOLVED ASSIGNMENT 1 CODE 395
The sign of the correlation coefficient indicates the direction of the associate. The magnitude of the correlation coefficient indicates the strength of the association.
For example, a correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r= -0.2 suggest a weak, negative association. A correlation close to zero suggests no linear association between two continuous variables.