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LinearRegression/linear_regression_proposal.md

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+### :pushpin: Step 1
+
+**TITLE:**    
+Intro to Multiple Linear Regression Analysis in Python
+
+**TOPIC:**    
+Statistics
+
+**DESCRIPTION (5-7+ sentences):**    
+A step-by-step tutorial on multiple linear regression analysis; the process involves checking assumptions, calculating coefficients using different techniques, making predictions, and evaluating performances under different models. These models include OLS, Lasso/Ridge, and Elastic Net.
+
+### :pushpin: Step 2
+
+:family: **TARGET AUDIENCE (3-5+ sentences):**    
+People who have a basic understanding of elementary statistics topics such as confidence intervals and hypothesis testing, and are interested in obtaining estimates from multiple predictors with basic regression techniques.
+
+### :pushpin: Step 3
+
+> Outline your learning/teaching structure: 
+
+**Beginning (2-3+ sentences):**    
+Begin by explaining the usage of multiple linear regression analysis and preparing an example dataset.
+
+**Middle (2-3+ sentences):**    
+Show how the analysis can be applied to the dataset using a simplest model. Apart from performing the regression itself, its conditions, interpretations, and applications are also explained. 
+
+**End (2-3+ sentences):**    
+Introduce some more complicated techniques such as stepwise regression and regularizations, and compare their results and performances.