Statistics and Econometrics for Business using GRETL

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Wrikki Lahiri

5:06:13

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1 - Introduction

1 - Introduction.mp4

07:26

2 - Introduction to GRETL Software

2 - Downloading and Installing GRETL.mp4

01:44

3 - GRETL Walkthrough.mp4

03:47

4 - Mathematical Operations in GRETL.mp4

04:17

3 - Types of Data

5 - Different types of data.mp4

03:30

4 - Association and Correlation

6 - Association and Correlation Intuition.mp4

09:28

7 - Correlation in GRETL.mp4

04:23

5 - Data Screening

8 - Data Screening.mp4

12:54

9 - Dealing with missing data in GRETL.mp4

06:19

6 - Linear Regression

10 - Simple Linear Regression Intuition.mp4

31:06

11 - Simple Linear Regression in GRETL.mp4

10:21

12 - Multiple Linear Regression Intuition.mp4

16:41

13 - Multiple Linear regression in GRETL.mp4

17:29

14 - Moderation Intuition.mp4

06:11

15 - Moderation in GRETL.mp4

07:35

16 - Mediation Intuition.mp4

08:19

17 - Mediation in GRETL.mp4

05:57

7 - Discrete Coice models

18 - Binary Logistic Regression or Logit Model Intuition.mp4

15:40

19 - Binary Logistic Regression in GRETL.mp4

11:12

20 - Multinomial Logistic Regression Model Intuition.mp4

09:18

21 - Multinomial Logistic Regression in GRETL.mp4

10:01

22 - Probit Regression Intuition.mp4

04:32

23 - Probit Model in GRETL.mp4

02:49

24 - Ordered Logit Model Intuition.mp4

06:47

25 - Ordered Logit Model in GRETL.mp4

06:23

8 - Linear Regression Assumptions and Violations

26 - Linear Regression Assumptions and Violations.mp4

07:05

9 - Time Series Analysis

27 - Autocorrelation.mp4

05:00

28 - Autoregression and Time Series Analysis Intuition.mp4

05:24

29 - Time Series Analysis in GRETL.mp4

04:56

10 - Panel Data Regression

30 - Panel Data Intuition.mp4

08:29

31 - Variations in Panel Data.mp4

03:26

32 - Types of Panel Data Models Intuition.mp4

10:02

33 - Panel Data Regression in GRETL.mp4

05:56

11 - Instrumental Variable Regression

34 - Instrumental Variable Regression and Endogeneity Intuition.mp4

08:29

35 - Instrumental Variable Regression in GRETL.mp4

06:04

12 - Count Data Models

36 - Count Data Regression Intuition.mp4

04:01

37 - Count Data Regression Poisson Regression in GRETL.mp4

04:59

13 - SurvivalDuration Regression Models

38 - SurvivalDuration Models Intuition.mp4

05:09

39 - SurvivalDuration Models in GRETL.mp4

03:04

14 - Practice Activity

Description

Go from zero to hero in statistics and econometrics using GRETL (A free alternative to SAS, STATA, and SPSS)

What You'll Learn?

Students will learn econometrics techniques
Students will learn advanced statistics techniques
Students will gain hands on experience in conducting statistical and econometrics analysis on GRETL Software
Students will learn about different kinds of regression techniques for different kinds of data
Students will learn advanced forms of binary choice modelling ( Multinomial logistic regression, ordinal models, profit models)
Students will learn time series analysis
students will learn how to deal with panel data and panel data regression
Students will learn about instrumental variable regression and count data models

Who is this for?

People with knowledge of basic statistics who want to learn intermediate and advanced statistics

People who want to learn econometrics

People who want to learn techniques in statistics that go beyond linear and logistic regression

People who want to prepare for data science careers by learning advanced statistical modelling

People who want to learn advanced business intelligence and data analysis skills

People who want to learn how to deal with different types of data such as panel data and time series data

People who want to learn regression techniques for different types of discrete, ordinal, panel and time series data

More details

Description
Statistics and Econometrics for Business using GRETL software: Go from zero to hero in statistics is a course that exposes students to statistical and econometrics concepts (basic, intermediate and advanced) that are used to solve business problems. In this course students will learn statistical concepts and techniques, and econometrics tools and techniques through a mix of lectures on theoretical concepts and intuitions underlying statistical techniques, and practical application of statistical methods in solving real world business problems. The course covers basic to advanced level concepts, and allows students to learn both concepts and applications. After finishing this course students will have learnt how to use different statistical models to analyse any type of data to solve business problems; and how to study trends in data and use these trends to infer about the business setting they are studying. The course will also allow students to gain a better understanding of key concepts and the nuances in statistical methods. Statistics isn't a one size fits all discipline, and hence for different types of data and contexts, different analytical tools and models are required. This course goes beyond the simple linear regression and logistic regression techniques that are taught in most data analysis and data science classes, and exposes the students to advanced techniques meant for datasets which aren't appropriate for linear regression. The course also has hands on practical lessons on the GRETL ( GNU Regression, time series and econometrics library) software , through which students will learn how to use GRETL to implement advanced statistics and econometrics models. The course covers the following topics:
1. Hypothesis Testing
2. Correlation.
3. Simple Linear Regression.
4. Multiple linear regression.
5. Logistic Regression.
6. Multinomial Logistic Regression.
7. Ordinal Logit Model.
8. Probit Model.
9. Limitations of Linear Regression.
10. Time Series analysis and autocorrelation.
11. Panel Dta Regression.
12. Fixed effect models.
13. Random effect models.
14. Instrumental Variable Regression.
15. Count Data Models.
16. Duration Model.
Who this course is for:
People with knowledge of basic statistics who want to learn intermediate and advanced statistics
People who want to learn econometrics
People who want to learn techniques in statistics that go beyond linear and logistic regression
People who want to prepare for data science careers by learning advanced statistical modelling
People who want to learn advanced business intelligence and data analysis skills
People who want to learn how to deal with different types of data such as panel data and time series data
People who want to learn regression techniques for different types of discrete, ordinal, panel and time series data

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Wrikki Lahiri is a doctoral student at the City, University of London, and has worked as a data scientist in industry before embarking on his PhD. He has a Master of Science degree in Industrial and Systems Engineering and a Bachelor's degree in Mechanical Engineering. Wrikki's research involves big data mining, entrepreneurial finance, and Behavioural Psychology and Economics. For his research projects Wrikki scrapes, and analyses big data on crowdfunding and social media using machine learning, statistical, and data mining methods. Other research projects Wrikki is working on investigates entrepreneurial habits and motivation and productivity in startup employees. As a doctoral student Wrikki has taught undergraduate statistics and Wrikki also has teaching experience through providing private tuition to undergraduates and high school students in statistics and maths. Wrikki teaches statistics, data mining methods, entrepreneurial psychology and entrepreneurial finance.

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