STATA: Practical Application and Interpretation

STATA: Practical Application and Interpretation, available at $44.99, has an average rating of 5, with 30 lectures, based on 1 reviews, and has 339 subscribers.

You will learn about Students will be able to navigate the STATA interface and use its basic commands. Students will demonstrate the ability to import and manage datasets within STATA. Students will perform descriptive and inferential statistical analyses using STATA. Students will interpret statistical results and understand their implications. Students will create and customize graphs and charts to present data effectively. This course is ideal for individuals who are For all who is affiliated with the field of teaching, and doing research. It is particularly useful for For all who is affiliated with the field of teaching, and doing research.

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Summary

Title: STATA: Practical Application and Interpretation

Price: $44.99

Average Rating: 5

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Students will be able to navigate the STATA interface and use its basic commands.
Students will demonstrate the ability to import and manage datasets within STATA.
Students will perform descriptive and inferential statistical analyses using STATA.
Students will interpret statistical results and understand their implications.
Students will create and customize graphs and charts to present data effectively.

Who Should Attend

For all who is affiliated with the field of teaching, and doing research.

Target Audiences

For all who is affiliated with the field of teaching, and doing research.

Course Description

This course provides a comprehensive introduction to STATA, a powerful statistical software widely used in various fields such as economics, sociology, political science, and public health. The course focuses on the practical application of STATA, covering data management, statistical analysis, and interpretation of results. Students will learn how to perform a range of statistical tests, create meaningful visualizations, and draw valid conclusions from their data. Through hands-on exercises and real-world examples, students will develop the skills necessary to effectively use STATA for their research and professional needs.

Course Objectives

To provide students with a thorough understanding of the STATA interface and its functionalities.
To teach students how to import, manage, and manipulate datasets in STATA.
To equip students with the knowledge to perform a variety of statistical tests and analyses using STATA.
To enable students to interpret and communicate the results of their statistical analyses effectively.
To develop students’ ability to create and customize data visualizations in STATA.

Introduction to STATA

Overview of STATA

Installation and setup

Navigating the STATA interface

Basic commands and syntax

Data types and structures in STATA

Data Management in STATA

Importing data from various sources (Excel, CSV, etc.)
Data cleaning and preparation
Data transformation (sorting, merging, reshaping)
Handling missing data

Descriptive Statistics

Summary statistics (mean, median, mode, etc.)
Frequency distributions and cross-tabulations
Measures of dispersion (variance, standard deviation, etc.)
Data visualization (histograms, bar charts, pie charts)

Inferential Statistics I

Hypothesis testing
t-tests (one-sample, independent, paired)
Chi-square tests
Analysis of variance (ANOVA)

Inferential Statistics II

Correlation analysis
Simple linear regression
Multiple regression analysis
Assumptions and diagnostics for regression models

Advanced Statistical Tests

Logistic regression
Factor analysis
Time series analysis
Panel data analysis

Data Visualization in STATA

Creating and customizing graphs (scatter plots, line graphs, etc.)
Enhancing visualizations with titles, labels, and legends
Exporting and presenting graphs
Using graph commands and options

Practical Applications and Case Studies

Applying STATA to real-world datasets
Conducting comprehensive analyses
Interpreting and reporting results
Case studies in various fields (economics, public health, etc.)

Advanced Data Management Techniques

Working with large datasets
Automating tasks with do-files and macros
Data validation and quality control
Using loops and programs in STATA

Preparing Reports and Documentation

Creating tables and reports in STATA
Documenting and annotating your work
Best practices for reproducible research
Sharing and collaborating on STATA projects

Special Topics in STATA

Survey data analysis
Handling complex survey designs
Advanced econometric techniques
Customizing STATA with user-written commands

Review and Final Project

Review of key concepts and techniques
Q&A and troubleshooting
Final project presentations
Course wrap-up and feedback

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Introduction about all the tabs of data

Lecture 3: Importing data into STATA

Lecture 4: Data issues

Chapter 2: Multicollinearity

Lecture 1: Multicollinearity definition and its Causes

Lecture 2: Identification of Multicollinearity

Lecture 3: Effects of Multicollinearity

Lecture 4: Detection of Multicollinearity

Lecture 5: Solutions to Multicollinearity

Lecture 6: Multicollinearity in STATA Practical Sessions

Chapter 3: Stationarity of the data

Lecture 1: Checking the stationary of time series data

Lecture 2: Converting Data into First Difference and seeing its stationarity

Lecture 3: Converting non-stationary Series into Stationary Series

Lecture 4: Converting Time series into Panel data

Lecture 5: Checking the Stationarity of the Panel Data

Chapter 4: Linear Regression model and Diagnostic Testing

Lecture 1: Descriptive Statistics, interpretations.

Lecture 2: Linear Regression Model

Lecture 3: Obtaining the Residuals and checking their Normality

Lecture 4: Checking that Residuals are Constant or Heteroscedastic

Lecture 5: Checking that Residuals are serially correlated or not

Lecture 6: Removal of serial Correlation (Auto correlation)

Chapter 5: Cointegration tests

Lecture 1: Johansen Test of Cointegration

Lecture 2: Granger Causality Test

Chapter 6: VAR (Vector Auto regressive Model) and Vector Error Correction Model (VECM)

Lecture 1: Lag selection Criteria 1st Method

Lecture 2: Lag Selection Criteria 2nd Method

Lecture 3: Conditions for Applying Vector Auto Regressive (VAR)

Lecture 4: Vector Auto Regressive (VAR) Estimation and Interpretation

Lecture 5: Diagnostic Testing of VAR

Lecture 6: Vector Error Correction Model (VECM)

Lecture 7: Forecasting in VAR Model

Instructors

Dr. Nasir Munir
Assistant Professor at SZABIST University

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Frequently Asked Questions

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