Understanding P-Values is essential for improving regression models.
In 2 minutes, I'll crush your confusion.
Let's go:
1. The p-value:
A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.
2. Null Hypothesis (H₀):
The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under H₀, the regressor does not affect the outcome.
Correlation is the skill that has singlehandedly benefitted me the most in my career.
In 3 minutes I'll demolish your confusion (and share strengths and weaknesses you might be missing).
Let's go:
1. Correlation:
Correlation is a statistical measure that describes the extent to which two variables change together. It can indicate whether and how strongly pairs of variables are related.
2. Types of correlation:
Several types of correlation are used in statistics to measure the strength and direction of the relationship between variables. The three most common types are Pearson, Spearman Rank, and Kendall's Tau. We'll focus on Pearson since that is what I use 95% of the time.