Advanced Statistics And Econometrics For Business.

Learn statistical techniques that will give you the edge using GRETL Software

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