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

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.

1

Introduction to the Course

2

Control the Pace of a Lesson

3

What is Statistics

4

Sample And Population

5

Sample and population

6

Descriptive and Inferential Statistics

7

Descriptive and Inferential Statistics

8

Descriptive and Inferential Statistics

9

Data Types

10

Data Types

11

Data Types

12

Visualize Data

13

Histogram

14

Central Tendency Measures

15

Variability Measures

16

Calculate Central and Variability Measures (Practical)

17

Symmetry and skewness in Data

18

Symmetry and skewness in Data

19

Symmetry and skewness in Data

20

Correlation and Covariance

21

Correlation and Covariance

22

Course Rating

1

Introduction to Inferential Statistics

2

Discrete Probability Distributions

3

Normal Distribution

4

Normal Distribution

5

Variable standardization Demo

Link to Interactive Mathematics Website:

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

6

Normal Distribution

7

Variable standardization

8

Variable standardization

9

Link to the Website Demo

1

Introduction to Central Limit Theorem

2

Central Limit Theorem

3

Estimators

4

Estimators

5

Central Limit Theorem

6

Introduction to Confidence Interval

7

Calculate Confidence Interval for one Sample with a Known Population Variance

8

Calculate Confidence Interval

9

Introduction to the Business Problem

10

Calculate Confidence Interval in Excel

11

t - Distribution

12

Student and Normal Distributions

13

Calculate Confidence Interval for one Sample with a Unknown Population Variance

14

Reduce Margin of Error

15

Confidence Interval for two Dependent Samples

16

Calculate Confidence Interval for two Dependent Samples in Excel

17

Confidence Interval for two Independent Samples with a Known Population Variance

18

Calculate Confidence Interval for two Independent Samples Known Var in Excel

19

Confidence Interval for two Independent Samples Unknown Population Variance

1

What is a Statistical Hypothesis

2

Types of Hypotheses

3

P-Value

4

Link to z-value Calculator

5

Testing a Hypothesis for one Sample, Variance is Known

6

Testing the Hypothesis in Excel

7

Testing a Hypothesis for one Sample, Variance is Unknown

8

Testing a Hypothesis for two Dependent Samples

9

Link to t-value Calculator

10

Testing a Hypothesis for two Independent Samples, Variance is Known

11

Testing a Hypothesis for two Independent Samples, Variance is Unknown

1

Regression Model

2

Predictors

3

Run Descriptive Simple Statistics and Understand Scatter Plot

4

Regression Slop and Residuals

5

Ordinary Least Squares

6

Verifying Significance of Predictors

7

When the predictor variable is considered significant?

8

R_Squared

9

R_Squared

10

Types of Regressions

11

Course Rating

1

Stats Applications

2

Install gretl

3

Build Simple Linear Regression in Gretl

4

Build Simple Linear Regression in Excel

5

Introduction to R

6

Install R

7

Install R Studio

8

Overview on R Studio

9

How to use ~ symbol

10

Build Simple Linear Regression in R

11

Get the Predicted Salaries

12

Visualize the Model

1

What is Multiple Linear Regression

2

Dummy Variables

3

Assumptions of Multiple Linear Regression

4

Stepwise Approach

5

Overview on the Business Problem

6

Test Multicollinearity between Independent Variables

7

Build Multiple Linear Regression in Gretl - 1

8

Build Multiple Linear Regression in Gretl - 2

9

Coefficients Interpretation

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