The Essential Guide to Stata

The Essential Guide to Stata, available at $84.99, has an average rating of 4.38, with 103 lectures, 1 quizzes, based on 739 reviews, and has 4191 subscribers.

You will learn about An essential introduction to Stata Data manipulation in Stata Data visualisation in Stata Data analysis in Stata Regression modelling in Stata Simulation in Stata Count data modelling Categorical data modelling Survival analysis Panel Data Analysis Epidemiology Instrumental Variables Power Analysis Difference-in-Differences This course is ideal for individuals who are Anyone wanting to work with Stata or Economics/Politics/Social Science students working with data or Those working in policy and government analysing data or Business managers using quantitative evidence It is particularly useful for Anyone wanting to work with Stata or Economics/Politics/Social Science students working with data or Those working in policy and government analysing data or Business managers using quantitative evidence.

Enroll now: The Essential Guide to Stata

Summary

Title: The Essential Guide to Stata

Price: $84.99

Average Rating: 4.38

Number of Lectures: 103

Number of Quizzes: 1

Number of Published Lectures: 103

Number of Published Quizzes: 1

Number of Curriculum Items: 104

Number of Published Curriculum Objects: 104

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: ￡34.99

Quality Status: approved

Status: Live

What You Will Learn

An essential introduction to Stata

Data manipulation in Stata

Data visualisation in Stata

Data analysis in Stata

Regression modelling in Stata

Simulation in Stata

Count data modelling

Categorical data modelling

Survival analysis

Panel Data Analysis

Epidemiology

Instrumental Variables

Power Analysis

Difference-in-Differences

Who Should Attend

Anyone wanting to work with Stata

Economics/Politics/Social Science students working with data

Those working in policy and government analysing data

Business managers using quantitative evidence

Target Audiences

Anyone wanting to work with Stata

Economics/Politics/Social Science students working with data

Those working in policy and government analysing data

Business managers using quantitative evidence

Make sure to check out my twitter feed for monthly promo codes and other updates (@easystats3).

The Essential Guide to Data Analytics with Stata

Learning and applying new statistical techniques can be daunting experience.

This is especially true once one engages with “real life” data sets that do not allow for easy “click-and-go” analysis, but require a deeper level of understanding of programme coding, data manipulation, output interpretation, output formatting and selecting the right kind of analytical methodology.

In this course you will receive a comprehensive introduction to Stata and its various uses in modern data analysis. You will learn to understand the many options that Stata gives you in manipulating, exploring, visualizing and modelling complex types of data. By the end of the course you will feel confident in your ability to engage with Stata and handle complex data analytics. The focus of each session will consistently be on creating a “good practice” and emphasising the practical application – and interpretation – of commonly used statistical techniques without resorting to deep statistical theory or equations.

This course will focus on providing an overview of data analytics using Stata.

No prior engagement with is Stata needed. Some prior statistics knowledge will help but is not necessary.

The course is aimed at anyone interested in data analytics using Stata.

Like for other professional statistical packages the course focuses on the proper application – and interpretation – of code.

Some basic quantitative/statistical knowledge will be required; this is not an introduction to statistics course but rather the application and interpretation of such using Stata.

Topics covered include:

1. Getting started with Stata

2. Viewing and exploring data

3. Manipulating data

4. Visualising data

5. Correlation and ANOVA

6. Regression including diagnostics (Ordinary Least Squares)

7. Regression model building

8. Hypothesis testing

9. Binary outcome models (Logit and Probit)

10. Fractional response models (Fractional Logit and Beta Regression)

11. Categorical choice models (Ordered Logit and Multinomial Logit)

12. Simulation techniques (Random Numbers and Simulation)

13. Count data models (Poisson and Negative Binomial Regression)

14. Survival data analysis (Parametric, Cox-Proportional Hazard and Parametric Survival Regression)

15. Panel data analysis (Long Form Data, Lags and Leads, Random and Fixed Effects, Hausman Test and Non-Linear Panel Regression)

16. Difference-in-differences analysis (Difference-in-Difference and Parallel Trends)

17. Instrumental variable regression (Endogenous Variables, Sample Selection, Non-Linear Endogenous Models)

18. Epidemiological tables (Cohort Studies, Case-Control Studies and Matched Case-Control Studies)

19. Power analysis (Sample Size, Power Size and Effect Size)

20. Matrix operations (Matrix operators, Matrix functions, Matrix subscripting)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Getting Started

Lecture 1: The Stata Interface

Lecture 2: Using Help in Stata

Lecture 3: Command Syntax

Lecture 4: .do and .ado Files

Lecture 5: Log Files

Lecture 6: Importing Data

Chapter 3: Exploring Data

Lecture 1: Viewing Raw Data

Lecture 2: Describing and Summarizing

Lecture 3: Tabulating and Tables

Lecture 4: Missing Values

Lecture 5: Numerical Distributional Analysis

Lecture 6: Using Weights

Lecture 7: The New Table Command (Stata 17)

Chapter 4: Manipulating Data

Lecture 1: Recoding an Existing Variable

Lecture 2: Creating New Variables, Replacing Old Variables

Lecture 3: Naming and Labelling Variables

Lecture 4: Extensions to Generate

Lecture 5: Indicator Variables

Lecture 6: Keep and Drop Data/Variables

Lecture 7: Saving Data

Lecture 8: Converting String Data

Lecture 9: Combining Data

Lecture 10: Using Macros and Loops Effectively

Lecture 11: Accessing Stored Information

Lecture 12: Multiple Loops

Lecture 13: Date Variables

Lecture 14: Subscripting over Groups

Chapter 5: Visualising Data

Lecture 1: Graphing in Stata

Lecture 2: Bar Graphs and Dot Charts

Lecture 3: Graphing Distributions

Lecture 4: Pie Charts

Lecture 5: Scatterplots and Lines of Best Fit

Lecture 6: Graphing Custom Functions

Lecture 7: Contour Plots (and Interaction Effects)

Lecture 8: Jitter Data in Scatterplots

Lecture 9: Sunflower Plots

Lecture 10: Combining Graphs

Lecture 11: Changing Graph Sizes

Lecture 12: Graphing by Groups

Lecture 13: Changing Graph Colours

Lecture 14: Adding Text to Graphs

Lecture 15: Scatterplots with Categories

Chapter 6: Testing Means, Correlations and ANOVA

Lecture 1: Association Between Two Categorical Variables

Lecture 2: Testing Means

Lecture 3: Bivariate Correlation

Lecture 4: Analysis of Variance (ANOVA)

Chapter 7: Linear Regression

Lecture 1: Ordinary Least Squares (OLS) Regression

Lecture 2: Factor Variables in OLS Regression

Lecture 3: Diagnostic Statistics for OLS Regression

Lecture 4: Log Dependent Variables and Interaction Effects in OLS Regression

Lecture 5: Hypothesis Testing in OLS Regression

Lecture 6: Presenting Estimates from OLS Regression

Lecture 7: Standardizing Regression Estimates

Lecture 8: Graphing Regression Estimates

Lecture 9: Oaxaca Decomposition Analysis

Lecture 10: Mixed Models: Random Intercepts and Random Coefficients

Lecture 11: Constrained Linear Regression

Chapter 8: Categorical Choice Models

Lecture 1: Binary Choice Models (Logit/Probit Regression)

Lecture 2: Diagnostics and Interpretation of Logit and Probit Regression

Lecture 3: Ordered and Multinomial Choice Models

Chapter 9: Fractional/Proportional Variable Models

Lecture 1: Fractional Logit, Beta Regression and Zero-inflated Beta Regression

Chapter 10: Random Numbers and Simulation

Lecture 1: Random Numbers

Lecture 2: Data Generating Process

Lecture 3: Simulating a Violation of Statistical Assumptions

Lecture 4: Monte Carlo Simulation

Chapter 11: Count Data Models

Lecture 1: Features of Count Data

Lecture 2: Poisson Regression

Lecture 3: Negative Binomial Regression

Lecture 4: Truncated and Censored Count Regression

Lecture 5: Hurdle Count Regression

Chapter 12: Survival Analysis

Lecture 1: What is Survival Analysis?

Lecture 2: Setting up Survival Data

Lecture 3: Descriptive Statistics in Survival Data

Lecture 4: Non-parametric Survival Analysis

Lecture 5: Cox Proportional Hazards Model

Lecture 6: Diagnostics for Cox Models

Lecture 7: Parametric Survival Analysis

Chapter 13: Panel Data Analysis

Lecture 1: Setting up Panel Data

Lecture 2: Panel Data Descriptives

Lecture 3: Lags and Leads

Lecture 4: Linear Panel Estimators

Lecture 5: The Hausman Test

Lecture 6: Non-Linear Panel Estimators

Chapter 14: Difference-in-Differences Analysis

Lecture 1: Difference-in-Differences Estimation

Lecture 2: Parallel Trend Assumption

Instructors

F. Buscha
Professor

Rating Distribution

1 stars: 6 votes

2 stars: 13 votes

3 stars: 71 votes

4 stars: 256 votes

5 stars: 393 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!