Understand concepts of least square regression and Hypothesis testing to our model like t-test, ANOVA, F-test, R square
What you'll learn
Least Square Regression
Build OLS in Statsmodel
Hypothesis Testing
t test
ANOVA
F Statistics
Degree of Freedom of the Model
Descriptionting Regression Line above the scatter Description (Fitted Values)
Predicting Results
Answer Question statistically
Requirements
Understanding Statistics
Beginner to Python
Description
In this course, you will learn concepts of linear regression. Starting from
Case Study on Big Mac
Statistical Questions
Least Square Regression
Hypothesis testing: t- test
ANOVA and F-test
Correlation
R Square
You will learn the approaches towards regression with case study. First we start with understanding linear equation and the optimization function value sum of squared errors. With that we find the values of the coefficient and makes least square regression. Then we starts building our linear regression in python.
For the model we build we necessary test like hypothesis testing.
t-test for coefficient significance
ANOVA and F-test for model significance.
And finally, we answer the question statically. Hope we are seeing you inside the course !!!
Who this course is for:
Beginner of Python Developer who want to learn Data Science
Solving question related to linear regression

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