The Essential Guide to Stata

The Essential Guide to Stata

What you’ll 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

Requirements

There are no requirements

Description

Make sure to check out my twitter feed for monthly promo codes and other updates (@easystats3).The Essential Guide to Data Analytics with StataLearning 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:Getting started with StataViewing and exploring dataManipulating dataVisualising dataCorrelation and ANOVARegression including diagnostics (Ordinary Least Squares)Regression model buildingHypothesis testingBinary outcome models (Logit and Probit)Fractional response models (Fractional Logit and Beta Regression)Categorical choice models (Ordered Logit and Multinomial Logit)Simulation techniques (Random Numbers and Simulation)Count data models (Poisson and Negative Binomial Regression)Survival data analysis (Parametric, Cox-Proportional Hazard and Parametric Survival Regression)Panel data analysis (Long Form Data, Lags and Leads, Random and Fixed Effects, Hausman Test and Non-Linear Panel Regression)Difference-in-differences analysis (Difference-in-Difference and Parallel Trends)Instrumental variable regression (Endogenous Variables, Sample Selection, Non-Linear Endogenous Models)Epidemiological tables (Cohort Studies, Case-Control Studies and Matched Case-Control Studies)Power analysis (Sample Size, Power Size and Effect Size)Matrix operations (Matrix operators, Matrix functions, Matrix subscripting)

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Getting Started

Lecture 2 The Stata Interface

Lecture 3 Using Help in Stata

Lecture 4 Command Syntax

Lecture 5 .do and .ado Files

Lecture 6 Log Files

Lecture 7 Importing Data

Section 3: Exploring Data

Lecture 8 Viewing Raw Data

Lecture 9 Describing and Summarizing

Lecture 10 Tabulating and Tables

Lecture 11 Missing Values

Lecture 12 Numerical Distributional Analysis

Lecture 13 Using Weights

Lecture 14 The New Table Command (Stata 17)

Section 4: Manipulating Data

Lecture 15 Recoding an Existing Variable

Lecture 16 Creating New Variables, Replacing Old Variables

Lecture 17 Naming and Labelling Variables

Lecture 18 Extensions to Generate

Lecture 19 Indicator Variables

Lecture 20 Keep and Drop Data/Variables

Lecture 21 Saving Data

Lecture 22 Converting String Data

Lecture 23 Combining Data

Lecture 24 Using Macro’s and Loop’s Effectively

Lecture 25 Accessing Stored Information

Lecture 26 Multiple Loops

Lecture 27 Date Variables

Lecture 28 Subscripting over Groups

Section 5: Visualising Data

Lecture 29 Graphing in Stata

Lecture 30 Bar Graphs and Dot Charts

Lecture 31 Graphing Distributions

Lecture 32 Pie Charts

Lecture 33 Scatterplots and Lines of Best Fit

Lecture 34 Graphing Custom Functions

Lecture 35 Contour Plots (and Interaction Effects)

Lecture 36 Jitter Data in Scatterplots

Lecture 37 Sunflower Plots

Lecture 38 Combining Graphs

Lecture 39 Changing Graph Sizes

Lecture 40 Graphing by Groups

Lecture 41 Changing Graph Colours

Lecture 42 Adding Text to Graphs

Lecture 43 Scatterplots with Categories

Section 6: Testing Means, Correlations and ANOVA

Lecture 44 Association Between Two Categorical Variables

Lecture 45 Testing Means

Lecture 46 Bivariate Correlation

Lecture 47 Analysis of Variance (ANOVA)

Section 7: Linear Regression

Lecture 48 Ordinary Least Squares (OLS) Regression

Lecture 49 Factor Variables in OLS Regression

Lecture 50 Diagnostic Statistics for OLS Regression

Lecture 51 Log Dependent Variables and Interaction Effects in OLS Regression

Lecture 52 Hypothesis Testing in OLS Regression

Lecture 53 Presenting Estimates from OLS Regression

Lecture 54 Standardizing Regression Estimates

Lecture 55 Graphing Regression Estimates

Lecture 56 Oaxaca Decomposition Analysis

Lecture 57 Mixed Models: Random Intercepts and Random Coefficients

Lecture 58 Constrained Linear Regression

Section 8: Categorical Choice Models

Lecture 59 Binary Choice Models (Logit/Probit Regression)

Lecture 60 Diagnostics and Interpretation of Logit and Probit Regression

Lecture 61 Ordered and Multinomial Choice Models

Section 9: Fractional/Proportional Variable Models

Lecture 62 Fractional Logit, Beta Regression and Zero-inflated Beta Regression

Section 10: Random Numbers and Simulation

Lecture 63 Random Numbers

Lecture 64 Data Generating Process

Lecture 65 Simulating a Violation of Statistical Assumptions

Lecture 66 Monte Carlo Simulation

Section 11: Count Data Models

Lecture 67 Features of Count Data

Lecture 68 Poisson Regression

Lecture 69 Negative Binomial Regression

Lecture 70 Truncated and Censored Count Regression

Lecture 71 Hurdle Count Regression

Section 12: Survival Analysis

Lecture 72 What is Survival Analysis?

Lecture 73 Setting up Survival Data

Lecture 74 Descriptive Statistics in Survival Data

Lecture 75 Non-parametric Survival Analysis

Lecture 76 Cox Proportional Hazard’s Model

Lecture 77 Diagnostics for Cox Models

Lecture 78 Parametric Survival Analysis

Section 13: Panel Data Analysis

Lecture 79 Setting up Panel Data

Lecture 80 Panel Data Descriptives

Lecture 81 Lags and Leads

Lecture 82 Linear Panel Estimators

Lecture 83 The Hausman Test

Lecture 84 Non-Linear Panel Estimators

Section 14: Difference-in-Differences Analysis

Lecture 85 Difference-in-Differences Estimation

Lecture 86 Parallel Trend Assumption

Lecture 87 Difference-in-Differences without Parallel Trends

Section 15: Instrumental Variable Regression

Lecture 88 Instrumental Variable Regression

Lecture 89 Multiple Endogenous Variables

Lecture 90 Non-linear Instrumental Variable Regression

Lecture 91 Heckman Selection Models

Section 16: Epidemiological Tables

Lecture 92 Introduction and Rate Data

Lecture 93 Cumulative Incidence Data

Lecture 94 Case-Control Data

Lecture 95 Case-Control Data with Multiple Exposure

Lecture 96 Matched Case-Control Data

Section 17: Power Analysis

Lecture 97 Power Analysis: Sample Size

Lecture 98 Power Analysis: Power and Effect Size

Lecture 99 Power Analysis: Simple Regression

Section 18: Basic Matrix Operations

Lecture 100 Matrix Operations

Lecture 101 Matrix Functions

Lecture 102 Matrix Subscripting

Lecture 103 Matrix Operations with Data

Section 19: The Big Stata Practice Test

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

Course Information:

Udemy | English | 14h 18m | 5.77 GB
Created by: F. Buscha

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