Business Statistics A-Z
Master Business Statistics techniques with hands on lessons in GRETL Software
Business Statistics A-Z free download
Master Business Statistics techniques with hands on lessons in GRETL Software
Business Statistics A-Z: Master Business Statistics techniques with hands on lessons 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.
