Advanced Statistics And Econometrics For Business

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
Requirements
Knowledge of basic statistics- mean, median, mode, skew, kurtosis
Knowledge of hypothesis testing
Knowledge of statistical plots such as scatter plots
A Mac or windows computer for installing GRETL Software
Description
Advanced Statistics and Econometrics for Business is a course that exposes students to the advanced (and some intermediate level) statistical and econometrics concepts 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 intermediate 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. Correlation.2. Simple Linear Regression.3. Multiple linear regression.4. Logistic Regression.5. Multinomial Logistic Regression.6. Ordinal Logit Model.7. Probit Model.8. Limitations of Linear Regression.9. Time Series analysis and autocorrelation.10. Panel Dta Regression.11. Fixed effect models.12. Random effect models.13. Instrumental Variable Regression.14. Count Data Models.15. Duration Model.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Introduction to GRETL Software
Lecture 2 Downloading and Installing GRETL
Lecture 3 GRETL Walkthrough
Lecture 4 Mathematical Operations in GRETL
Section 3: Types of Data
Lecture 5 Different types of data
Section 4: Association and Correlation
Lecture 6 Association and Correlation Intuition
Lecture 7 Correlation in GRETL
Section 5: Data Screening
Lecture 8 Data Screening
Lecture 9 Dealing with missing data in GRETL
Section 6: Linear Regression
Lecture 10 Simple Linear Regression Intuition
Lecture 11 Simple Linear Regression in GRETL
Lecture 12 Multiple Linear Regression Intuition
Lecture 13 Multiple Linear regression in GRETL
Lecture 14 Moderation Intuition
Lecture 15 Moderation in GRETL
Lecture 16 Mediation Intuition
Lecture 17 Mediation in GRETL
Section 7: Discrete Coice models
Lecture 18 Binary Logistic Regression or Logit Model Intuition
Lecture 19 Binary Logistic Regression in GRETL
Lecture 20 Multinomial Logistic Regression Model Intuition
Lecture 21 Multinomial Logistic Regression in GRETL
Lecture 22 Probit Regression Intuition
Lecture 23 Probit Model in GRETL
Lecture 24 Ordered Logit Model Intuition
Lecture 25 Ordered Logit Model in GRETL
Section 8: Linear Regression Assumptions and Violations
Lecture 26 Linear Regression Assumptions and Violations
Section 9: Time Series Analysis
Lecture 27 Autocorrelation
Lecture 28 Autoregression and Time Series Analysis Intuition
Lecture 29 Time Series Analysis in GRETL
Section 10: Panel Data Regression
Lecture 30 Panel Data Intuition
Lecture 31 Variations in Panel Data
Lecture 32 Types of Panel Data Models Intuition
Lecture 33 Panel Data Regression in GRETL
Section 11: Instrumental Variable Regression
Lecture 34 Instrumental Variable Regression and Endogeneity Intuition
Lecture 35 Instrumental Variable Regression in GRETL
Section 12: Count Data Models
Lecture 36 Count Data Regression Intuition
Lecture 37 Count Data Regression (Poisson Regression) in GRETL
Section 13: Survival/Duration Regression Models
Lecture 38 Survival/Duration Models Intuition
Lecture 39 Survival/Duration Models in GRETL
Section 14: Practice Activity
People with knowledge of basic statistics and hypothesis testing 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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