Using Stata for Quantitative Analysis - A Primer

About this Course

  • 58 students enrolled
  • 1 instructors

Course Description

This course allows students to familiarize themselves with STATA as an alternative software to perform quantitative analysis. In this course, students will explore the STATA interface and attempt the frequently used commands in STATA. Students will also learn to import dataset into STATA, to obtain summary statistics of the data, to run Ordinary Least Square (OLS) regression and to interpret its key results, and to perform basic diagnostics tests for the OLS estimation. Several diagnostics tests are discussed such as tests for heteroskedasticity, serial correlation, multicollinearity, model misspecification, and normality of the residuals. Via a specially designed series of activities, audience will get a chance of building their own Do file, an important feature of STATA. This course is suitable for final year undergraduate students especially when completing their research for final year project. Similarly, this course can be a refresher for postgraduate students using quantitative analysis methods before embarking their postgraduate research study.

Course Learning Outcomes

1 ) Finally, students will be able to perform several diagnostic tests for OLS estimation such as tests for heteroskedasticity, serial correlation, multicollinearity, model misspecification (like omitted variable bias & outliers), and normality in residuals.
2 ) Upon completion of this course, students will be familiar with Stata interface, its menus, buttons and windows. Students will be able to import a dataset into Stata, and to perform some frequently used commands.
3 ) Next, students will be able to run a simple OLS regression in Stata, learn to identify its key results, and interpret them.
4 ) Then, students will be able to understand the concept of Ordinary Least Square (OLS) regression and the Classical Linear Regression Model (CLRM) assumptions.

Course Details

STATUS : Open
DURATION : FLEXIBLE
EFFORT : 15 hours of guided learning
MODE : 100% Online
COURSE LEVEL : Beginner
LANGUAGE : English
CLUSTER : Business & Management ( SP )

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 Syllabus

Stata interface
Dropdown menus and buttons
Frequently-used commands
Topic 1 notes
Creating a Do file - Step 1
Topic 1 - Activity
Assessment

Importing via File & Import menu
Importing via copy-paste & Example datasets
Topic 2 notes
Creating a Do file - Step 2
Topic 2 - Activity
Assessment

OLS regression
OLS scatter plot
CLRM assumptions
Topic 3 notes
Creating a Do file - Step 3
Topic 3 - Activity
Assessment

Summary statistics of variables
OLS estimation
Topic 4 notes
Creating a Do file - Step 4
Topic 4 - Activity
Assessment

Five key results of OLS estimation
Model fitness & R-squared
The 3S of independent variables
Topic 5 notes
Topic 5 - Activity
Assessment

Heteroskedasticity tests
Serial correlation tests
Other diagnostics tests
Topic 6 notes - Heteroskedasticity & Serial correlation tests
Topic 6 notes - Other diagnostics tests
Creating a Do file - final step
Topic 6 - Activity
Assessment

Our Instructor

PROFESOR MADYA DR MAHYUDIN BIN AHMAD

 Course Instructor
UiTM Kampus Arau

 Frequently Asked Questions

A1 : Stata is a data analysis software similar to EViews, SPSS, and R. Stata combines the point-and-click approach (like in EViews & SPSS) and programming technique (like in R) to running data analysis, hence giving the users the flexibility they need depending on their level of competencies.

A2 : OLS is an acronym for Ordinary Least Square regression, the most common technique for estimating a linear relationship between one or more independent variables and a dependent variable.

A3 : The Classical Linear Regression Model (CLRM) assumptions outline several requirements for the OLS estimation to fulfill so that its estimator can be the Best Linear Unbiased Estimator (BLUE). Violations of the assumptions may lead to several problems such as heteroskedasticity, serial correlation, model misspecification, omitted variable bias, the presence of outliers or non-normality in residuals.

A4 : Do file is considered one of the important features in Stata where Stata users can write down notes and commands in planning their analytical work and the flow of their analysis. From the Do file, users can simply execute the commands of their interests either selected commands or the whole commands listed in the Do file.