Step by Step procedure in Conducting the Hypothesis Testing: prerequisites: Part 1: What is Hypothesis Testing and When it is Used? Part 2: How to decide Null and Alternate Hypothesis? https://www.isixsigma.com/ Before diving into the steps, first let’s understand the Important Terminology we need to understand: Null Hypothesis: It is a well-established fact which is believed by all the people. It can also defined as an Assumption that treats everything equally and similarly. Alternate Hypothesis: It is a new claim made against the Null Hypothesis. If we have enough evidence against the Null Hypothesis we will reject Null Hypothesis. P-value: Probability of Null Hypothesis being true. Significance level: probability of rejecting the Null Hypothesis when it is true. It is a critical point where we decide whether the Null Hypothesis is rejected or not. Generally, the significant level will be 0.05 which means a 5 percent risk while concluding
It is the basic algorithm at which everybody would like to start their learning in Data Science. Now, what exactly the Linear Regression is 1. Linear Regression is the supervised learning algorithm where it’s main aim is to find the line that best fits the given data. 2. Here ‘Fitting the best line for given data’ means finding the relation between dependent and independent variables present in the data. Note 1: you need to use Linear regression only when your dependent and independent variables have linear relationship. Note 2: Here Independent variables can be both discreet or continuous data, but dependent variables should be continuous data. Ok, Let me explain with good example, Source: https://miro.medium.com/max/327/1*cFq7XW-Z69fDBil9wjyEBQ.png In the above example, If we observe the data, As ‘years of Experience’ is in