Learn Regression Analysis For Business

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Tags: Regression Analysis

Learn statistics and build regression models step by step through real business scenarios

Last updated 2022-01-10 | 4.4

- Learn about different types of Regression Models and their use
- Run Regression Analysis in several computer applications
- Learn in detail to build Linear Regression Model and Logistic Model which are highly used in business analysis

What you'll learn

Learn about different types of Regression Models and their use
Run Regression Analysis in several computer applications
Learn in detail to build Linear Regression Model and Logistic Model which are highly used in business analysis
Interpret the results of the Regression Analysis and translate them to business recommendations
Build Regression Models and use them in your business
Assess quality and efficiency of your model using several measures and indicators
Hands on three real business scenarios

* Requirements

* Basic knowledge of using Excel

Description

A complete hands on practical exercises to learn statistics and build regression models which are highly used in business data analysis. This course is designed to start with the very basics and then add up information gradually till the professional level.  Accordingly students who have fair background in statistics can choose to jump to the practical part of the course to learn building regression models in detail. 

In this course you will learn descriptive and inference statistics, such as central and variability measures, visualize data, calculate confidence intervals and test hypotheses. Furthermore,  you will learn to build different types of regression models and use them in data analysis. You will start first with Simple Linear Regression. After that Multiple Linear Regression when you use several independent variables to predict target values. After that you will learn Logistic Regression for classification. You will learn step by step how to understand a business problem from data observations and determine the variables you need to include in the regression analysis.

You will also learn how to interpret model coefficients from business point of view and assess regression model's prediction power using several indicators, such as: R-squared and p-value. After that you will be able  to prepare your business recommendations that can be used by decision makers.  You will learn using important data analysis applications like Microsoft Excel, Gretl and R.

Who this course is for:

  • This course is for all those who need to learn statistics and regression modeling for data science and business analysis

Course content

11 sections • 113 lectures

Introduction to Statistics 22 lectures

Introduction to the Course Preview 02:30

Control the Pace of a Lesson Preview 00:44

What is Statistics Preview 01:30

Sample And Population Preview 01:15

Sample and population

Descriptive and Inferential Statistics Preview 01:47

Descriptive and Inferential Statistics

Descriptive and Inferential Statistics

Data Types Preview 03:54

Data Types

Data Types

Visualize Data Preview 02:00

Histogram Preview 04:38

Central Tendency Measures Preview 03:07

Variability Measures Preview 03:16

Calculate Central and Variability Measures (Practical) Preview 03:09

Symmetry and skewness in Data Preview 01:47

Symmetry and skewness in Data

Symmetry and skewness in Data

Correlation and Covariance Preview 04:37

Correlation and Covariance

Course Rating Preview 00:16

Inferential Statistics 9 lectures

Introduction to Inferential Statistics Preview 02:11

Discrete Probability Distributions Preview 03:15

Normal Distribution Preview 04:55

Normal Distribution

Normal Distribution

Variable standardization Preview 02:27

Variable standardization Demo Preview 00:51

Link to Interactive Mathematics Website:

https://www.intmath.com/counting-probability/normal-distribution-graph-interactive.php

Variable standardization

Link to the Website Demo Preview 00:01

Central Limit Theorem 19 lectures

Introduction to Central Limit Theorem Preview 03:38

Central Limit Theorem

Estimators Preview 01:27

Estimators

Central Limit Theorem

Introduction to Confidence Interval Preview 05:04

Calculate Confidence Interval for one Sample with a Known Population Variance Preview 05:49

Calculate Confidence Interval

Introduction to the Business Problem Preview 02:19

Calculate Confidence Interval in Excel Preview 08:12

t - Distribution Preview 03:52

Student and Normal Distributions

Calculate Confidence Interval for one Sample with a Unknown Population Variance Preview 03:45

Reduce Margin of Error Preview 02:23

Confidence Interval for two Dependent Samples Preview 01:14

Calculate Confidence Interval for two Dependent Samples in Excel Preview 04:17

Confidence Interval for two Independent Samples with a Known Population Variance Preview 02:56

Calculate Confidence Interval for two Independent Samples Known Var in Excel Preview 04:31

Confidence Interval for two Independent Samples Unknown Population Variance Preview 05:01

Statistical Hypothesis Testing 11 lectures

What is a Statistical Hypothesis Preview 02:29

Types of Hypotheses Preview 03:54

P-Value Preview 02:22

Link to z-value Calculator Preview 00:01

Testing a Hypothesis for one Sample, Variance is Known Preview 01:42

Testing the Hypothesis in Excel Preview 04:08

Testing a Hypothesis for one Sample, Variance is Unknown Preview 02:58

Testing a Hypothesis for two Dependent Samples Preview 04:40

Link to t-value Calculator Preview 00:01

Testing a Hypothesis for two Independent Samples, Variance is Known Preview 03:58

Testing a Hypothesis for two Independent Samples, Variance is Unknown Preview 03:11

Introduction to Regression Modeling 11 lectures

Regression Model Preview 05:54

Predictors

Run Descriptive Simple Statistics and Understand Scatter Plot Preview 06:58

Regression Slop and Residuals Preview 06:14

Ordinary Least Squares

Verifying Significance of Predictors Preview 03:43

When the predictor variable is considered significant?

R_Squared Preview 07:00

R_Squared

Types of Regressions Preview 04:22

Course Rating Preview 00:16

Simple Linear Regression Analysis 12 lectures

Stats Applications Preview 01:23

Install gretl Preview 01:53

Build Simple Linear Regression in gretl Preview 07:34

Build Simple Linear Regression in Excel Preview 02:50

Introduction to R Preview 00:46

Install R Preview 01:33

Install R Studio Preview 02:15

Overview on R Studio Preview 01:33

How to use (~) symbol Preview 01:24

Build Simple Linear Regression in R Preview 07:44

Get the Predicted Salaries Preview 03:15

Visualize the Model Preview 03:52

Multiple Linear Regression Analysis 13 lectures

What is Multiple Linear Regression Preview 02:41

Dummy Variables Preview 04:11

Assumptions of Multiple Linear Regression Preview 07:29

Stepwise Approach Preview 05:48

Overview on the Business Problem Preview 01:42

Test Multicollinearity between Independent Variables Preview 02:32

Build Multiple Linear Regression in Gretl - 1 Preview 07:35

Build Multiple Linear Regression in Gretl - 2 Preview 05:52

Coefficients Interpretation Preview 02:27

Create Graphs in gretl Preview 03:37

Create Predicted Values Preview 02:05

Create Multiple Regression Model in R- Part1 Preview 07:44

Create Multiple Regression Model in R- Part2 Preview 04:57

Logistic Regression Analysis 7 lectures

Logistic Regression Preview 07:59

Confusion Matrix Preview 04:09

Overview on the business problem data Preview 01:16

Build the Logistic Model in gretl Preview 06:22

Create Graphs and Forecast the Values for the Model Preview 03:52

Create the Logistic Regression in R Preview 05:16

Create the Confusion Matrix for the model Preview 04:47

Business Problem (Create a Regression Model to Predict homes Prices) 3 lectures

Overview on the Business Problem Preview 00:58

Build the Regression Model in gretl Preview 03:20

Build the Regression Model in R Preview 01:37

Homework 1 3 lectures

Overview on the Business Problem Preview 00:29

Build the Model in gretl Preview 05:34

Build the Model in R Preview 02:45

Homework 2 3 lectures

Overview on the Business Problem Preview 01:02

Build the Model in gretl Preview 04:12

Build the Model in R Preview 01:22