P-value:
During the class, Professor have explained the concept of p-value which can be summarized as the probability under no effect or null hypothesis, whose result is equal to or more precise than what was actually observed.
The P in p-value stands for probability and it is a measure of how likely it is that any observed difference value between groups is due to a chance. In simple terms, it provides you the outcome from your data which will be statistically significant or due to a uneven event.
Conceptual working steps of p-value:
- Formulating a null hypothesis (H0)
- Collecting and analyzing data
- Calculating the p-value
- Comparing the p-value to a significance level (A)
-If p-value ≤ A: You reject the null hypothesis. There is evidence to support your alternative hypothesis.
– If p-value > A: You fail to reject the null hypothesis. There is no enough evidence to support your alternative hypothesis.
Linear Regression and Multiple Regression:
If the linear regression is used to predict one dependent variable using one independent variable, then it is called SIMPLE LINEAR REGRESSION. The formula is Y = a + b X , in which Y is dependent, X is independent, b is slope and a is intercept.
If the linear regression is used to predict one dependent variable using two or more independent variable, then it is called MULTIPLE LINEAR REGRESSION. The formula is Y = a + b1X1 + b2X2 + … + bnXn, where Y is dependent, X is independent, a is intercept and b1, b2, etc are the slopes.