# Linear Regression and Logistic Regression using R Studio

Linear Regression and Logistic Regression for beginners. Understand the difference between Regression & Classification

4.2/5

## Linear Regression and Logistic Regression using R Studio

### What you’ll learn

Learn how to solve real life problem using the Linear and Logistic Regression technique
Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
Graphically representing data in R before and after analysis
How to do basic statistical operations in R
Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem

### Requirements

This course starts from basics and you do not even need coding background to build these models in R Studio
Students will need to install R and R studio software but we have a separate lecture to help you install the same

### Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Resources

Section 2: Basics of Statistics

Lecture 3 Types of Data

Lecture 4 This is a milestone!

Lecture 5 Types of Statistics

Lecture 6 Describing the data graphically

Lecture 7 Measures of Centers

Lecture 8 Measures of Dispersion

Section 3: Getting started with R and R studio

Lecture 9 Installing R and R studio

Lecture 10 Basics of R and R studio

Lecture 11 Packages in R

Lecture 12 Inputting data part 1: Inbuilt datasets of R

Lecture 13 Inputting data part 2: Manual data entry

Lecture 14 Inputting data part 3: Importing from CSV or Text files

Lecture 15 Creating Barplots in R

Lecture 16 Creating Histograms in R

Section 4: Data Preprocessing before building Linear Regression Model

Lecture 18 Data Exploration

Lecture 19 The Data and the Data Dictionary

Lecture 20 Importing the dataset into R

Lecture 21 Univariate Analysis and EDD

Lecture 22 EDD in R

Lecture 23 Outlier Treatment

Lecture 24 Outlier Treatment in R

Lecture 25 Missing Value imputation

Lecture 26 Missing Value imputation in R

Lecture 27 Seasonality in Data

Lecture 28 Bi-variate Analysis and Variable Transformation

Lecture 29 Variable transformation in R

Lecture 30 Non Usable Variables

Lecture 31 Dummy variable creation: Handling qualitative data

Lecture 32 Dummy variable creation in R

Lecture 33 Correlation Matrix and cause-effect relationship

Lecture 34 Correlation Matrix in R

Section 5: Linear Regression Model

Lecture 35 The problem statement

Lecture 36 Basic equations and Ordinary Least Squared (OLS) method

Lecture 37 Assessing Accuracy of predicted coefficients

Lecture 38 Assessing Model Accuracy – RSE and R squared

Lecture 39 Simple Linear Regression in R

Lecture 40 Multiple Linear Regression

Lecture 41 The F – statistic

Lecture 42 Interpreting result for categorical Variable

Lecture 43 Multiple Linear Regression in R

Lecture 44 Test-Train split

Lecture 46 More about test-train split

Lecture 47 Test-Train Split in R

Section 6: Introduction to the classification Models

Lecture 48 Three classification models and Data set

Lecture 49 Importing the data into R

Lecture 50 The problem statements

Lecture 51 Why can’t we use Linear Regression?

Section 7: Building a Logistic Regression Model

Lecture 52 Logistic Regression

Lecture 53 Training a Simple Logistic model in R

Lecture 54 Results of Simple Logistic Regression

Lecture 55 Logistic with multiple predictors

Lecture 56 Training multiple predictor Logistic model in R

Lecture 57 Confusion Matrix

Lecture 58 Evaluating Model performance

Lecture 59 Predicting probabilities, assigning classes and making Confusion Matrix

Section 8: Test-Train Split

Lecture 60 Test-Train Split

Lecture 61 Test-Train Split in R

Lecture 62 The final milestone!

Lecture 63 Bonus Lecture

People pursuing a career in data science,Working Professionals beginning their Data journey,Statisticians needing more practical experience,Anyone curious to master Linear and Logistic Regression from beginner to advanced level in a short span of time

#### Course Information:

Udemy | English | 6h 13m | 2.36 GB