Difference between correlation and covariance
|
Correlation |
Covariance |
|
a) Ranges from – 1 to + 1 |
a) Doesn’t have range. |
|
b) Is a good measure of relationship between two variables. |
b) One variable increase then other variable also increases for positive covariance. For negative covariance, when one variable increase then other variable decreases. |
|
c) positive: more than zero Zero: no correlation Negative: less than zero |
c) When the two variables are independent, the covariance is zero. |
|
d) -0.4953215 means correlation is negative. |
d) -0.4953215 means covariance is negative and is very close to zero. |
Note: we use
i) t-test: for continuous variable
ii) chi-square test : for categorical variable
iii) ANOVA: for complex testing
iv) Non-parametric test: if data isn’t normally distributed.
v) p-value helps us determine the significance of our statistics test results.
vi) A small p-value means you were able to reject null hypothesis.
vii) A larger p-value means you fail to reject null hypothesis.
viii) Alternate hypothesis is true at 95% confidence interval.
