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Learn how to master Stata like a professional

An essential introduction to Stata

Data manipulation in Stata

Data analysis in Stata

Regression modelling

Stata code

Advanced Stata code

Fast, and to the point, useful tips to use in Stata

Data management

Programming

Graphics

Statistics

Basic plot types

Intermediate plot types

Advanced plot types

There are no requirements

The Complete Guide to 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 consists of three sub-courses that will 1) teach you the essentials of Stata 2) provide you with tips and tricks for Stata and 3) teach you advanced data visualization techniques.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)There are also 125 tips and tricks for Stata. These tips are aimed at helping you become a Stata master! They cover a wide range of issues the following topics:Data managementGraphingStatistics Programming. Each tip is designed to be stand-alone and will take no more than 2 minutes.Finally, you will be shown some of the most important data visualization methods and learn what ae the advantages and disadvantages of each technique are. A wide variety of graphs are highlighted in great detail including:HistogramsDensity plotsSpike plotsRootogramsBox plotsViolin plotsStem-and-Leaf plotsQuantile plotsBar graphsPie chartsDot chartsRadar plotsScatter plotsHeat plotsHex plotsSunflower plotsLines of best fitArea plotsLine plotsRange plotsRainbow plotsJitter plotsTable plotsBaloon plotsMosaic plotsChernoff facesSparkling plotsBubble plotsand moreDepending on your desired learning outcomes you may wish to focus on specific parts.To gain a basic understanding of Stata watch sections 2, 3, 4, 5, 6, 7 and 8To learn advanced Stata concepts watch sections 8, 9, 10, 11, 12, 13, 14, 15, 16 and 17To learn fast tips for Stata watch sections 18, 19, 20 and 21To learn all about data visualisation in Stata watch sections 5, 21, 22, 23, 24, 25 and 26To learn data management concepts watch sections 3, 4 and 18

Section 1: Introduction

Lecture 1 Introduction

Section 2: Essential Stata - 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: Essential Stata - 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: Essential Stata - 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: Essential Stata - Visualizing 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 Chag Graph Sizes

Lecture 40 Graphing by Groups

Lecture 41 Chag Graph Colours

Lecture 42 Adding Text to Graphs

Lecture 43 Scatterplots with Categories

Section 6: Essential Stata - 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: Essential Stata - 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: Essential Stata - 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

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

Section 9: Essential Stata - 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 10: Essential Stata - 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 11: Essential Stata - 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 12: Essential Stata - 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 13: Essential Stata - Difference-in-Differences Analysis

Lecture 85 Difference-in-Differences Estimation

Lecture 86 Parallel Trend Assumption

Lecture 87 Difference-in-Differences without Parallel Trends

Section 14: Essential Stata - 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 15: Essential Stata - 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 16: Essential Stata - Power Analysis

Lecture 97 Power Analysis: Sample Size

Lecture 98 Power Analysis: Power and Effect Size

Lecture 99 Power Analysis: Simple Regression

Section 17: Essential Stata - Basic Matrix Operations

Lecture 100 Matrix Operations

Lecture 101 Matrix Functions

Lecture 102 Matrix Subscripting

Lecture 103 Matrix Operations with Data

Section 18: Tips and Tricks - Data Management

Lecture 104 How to create a code book

Lecture 105 How to create a label book

Lecture 106 How to list only variable names

Lecture 107 How to describe unopened data

Lecture 108 How to search in variables

Lecture 109 How to drop/keep variables sequentially

Lecture 110 How to check a digital data signature

Lecture 111 How to verify data

Lecture 112 How to compare two datasets

Lecture 113 How to compare variables

Lecture 114 How to use tabulate to generate dummy variables

Lecture 115 How to avoid many logical OR operators

Lecture 116 How to number labels

Lecture 117 How to use labels in expressions

Lecture 118 How to attach one value label to many variables

Lecture 119 How to store single values

Lecture 120 How to use Stata's hand-calculator

Lecture 121 How to use text with Stata's hand-calculator

Lecture 122 How to select column of data in a do-file

Lecture 123 How to rectangularize data

Lecture 124 How to check if variables uniquely identify observations

Lecture 125 How to drop duplicate observations

Lecture 126 How to draw a sample

Lecture 127 How to transpose a dataset

Lecture 128 How to quickly expand and interact many variables

Lecture 129 How to create publication quality tables in word

Lecture 130 How to create publication quality tables in excel

Lecture 131 How to export regression results

Lecture 132 How to create and use long strings

Lecture 133 How to use emojis

Lecture 134 How to quickly create new groups

Lecture 135 How to delete files from within Stata

Lecture 136 How to display file directory content

Lecture 137 How to clone a variable

Lecture 138 How to re-order variables

Lecture 139 How to add notes to data

Section 19: Tips and Tricks - Statistics

Lecture 140 How to create many one-way tables quickly

Lecture 141 How to create many two-way tables quickly

Lecture 142 How to sort and plot one-way tables

Lecture 143 How to expand data instead of using weights

Lecture 144 How to contract data to frequencies and percentages

Lecture 145 How to compute immediate statistics without loading data

Lecture 146 How to compute elasticities

Lecture 147 How to set the default confidence level

Lecture 148 How to show base levels of factor variables

Lecture 149 How to estimate a constrained linear regression

Lecture 150 How to bootstrap any regression

Lecture 151 How to interpolate missing values

Lecture 152 How to compute row statistics

Lecture 153 How to compute standardized coefficients after linear regression

Lecture 154 How to compute faster maal effects

Lecture 155 How to reduce collinearity in polynomial variables

Lecture 156 How to use contrasting mas

Lecture 157 How to use pairwise comparison with mas

Lecture 158 How to define the constant in a regression

Lecture 159 How to visualise complex polynomial models

Lecture 160 How to identify outliers from a regression

Lecture 161 How to predict within and outside a regression sample

Lecture 162 How to inspect

Section 20: Tips and Tricks - Programming

Lecture 163 How to hide unwanted output

Lecture 164 How to force show wanted output

Lecture 165 How to hide a graph

Lecture 166 How to suppress error messages

Lecture 167 How to force do-files to run to the end

Lecture 168 How to execute programmes outside Stata

Lecture 169 How to check memory usage

Lecture 170 How to reduce files sizes

Lecture 171 How stamp commands

Lecture 172 How to set a stopwatch

Lecture 173 How to pause Stata

Lecture 174 How to debug error messages

Lecture 175 How to pause for large output

Lecture 176 How to add custom ado folders

Lecture 177 How to create a custom user profile

Lecture 178 How to add comments to do-files

Lecture 179 How to loop over non-integer values

Lecture 180 How to monitor a loop

Lecture 181 How to show more in the results window

Lecture 182 How to display coefficient legends

Lecture 183 How to squish a table

Lecture 184 How to use and modify the Function keys

Lecture 185 How to view sourcecode

Lecture 186 How to create custom correlations

Lecture 187 How to insert current & date into log files

Lecture 188 How to save interactive commands

Lecture 189 How to create custom number lists

Lecture 190 How to change between lower and upper cases variable names and data

Lecture 191 How to change between lower and upper case text in do-files

Lecture 192 How to explicit subscript

Lecture 193 How to launch the interactive dialog box

Lecture 194 How to view undocumented commands

Section 21: Tips and Tricks - Graphing

Lecture 195 How to recover data from a graph

Lecture 196 How to generate a combined graph with one legend

Lecture 197 How to display RGB colors in graphs

Lecture 198 How to make colors opaque

Lecture 199 Why are SVG graphs useful?

Lecture 200 How to apply log scaling to a graph

Lecture 201 How to reverse and switch off axes

Lecture 202 How to have multiple axes on a graph

Lecture 203 How to display ASCII characters in graphs

Lecture 204 How to graph the variance-covariance matrix

Lecture 205 How to quickly plot estimated results

Lecture 206 How to randomly displace markers

Lecture 207 How to word frequencies from a webpage

Lecture 208 How to range plot

Lecture 209 How to create a violin plot

Lecture 210 How to show the Stata color palette

Lecture 211 How to create custom titles

Lecture 212 How to customize the look of graphs

Lecture 213 How to show a correlation matrix as graphical table

Lecture 214 How to plot a histogram with a boxplot

Lecture 215 How to draw histograms with custom bins

Lecture 216 How to graph a one/two/three-way table

Lecture 217 How to recover graph code

Lecture 218 How to do polar smoothing

Lecture 219 How to visualise ladders of power

Lecture 220 How to combine combined graphs

Lecture 221 How to separate scatter

Lecture 222 How to range a graph

Lecture 223 How to foreground/background plot

Lecture 224 How to plotstyle

Lecture 225 How to show multiple axes

Lecture 226 How to quickly increase graph label ticks

Lecture 227 How to add custom graph label ticks

Section 22: Data visualisation - single continuous variables

Lecture 228 What is a histogram?

Lecture 229 What is an unequal bin histogram?

Lecture 230 Learn Stata - Histograms

Lecture 231 What is a density plot?

Lecture 232 How to visualise multiple densities

Lecture 233 Learn Stata - Density plots

Lecture 234 What is a ridgeline plot?

Lecture 235 Learn Stata - Ridgeline plots

Lecture 236 What are cumulative density plots?

Lecture 237 Learn Stata - Cumulative density plots

Lecture 238 What is a spike plot?

Lecture 239 Learn Stata - Spike plots

Lecture 240 What is a rootogram plot?

Lecture 241 Learn Stata - Rootogram plots

Lecture 242 What is a box plot?

Lecture 243 Learn Stata - Box plots

Lecture 244 What is a violin plot?

Lecture 245 Learn Stata - Violin plots

Lecture 246 What is a stem-and-leaf plot?

Lecture 247 Learn Stata - Stem-and-leaf plots

Lecture 248 What is a dot plot?

Lecture 249 Learn Stata - Dot plots

Lecture 250 What is a symmetry plot?

Lecture 251 What is a quantile-uniform plot?

Lecture 252 What is a quantile-normal plot?

Lecture 253 What is a quantile-chi-squared plot?

Lecture 254 What is a quantile-quantile plot?

Lecture 255 Learn Stata - Quantile plots

Section 23: Data visualisation - single discrete variables

Lecture 256 What is a bar graph?

Lecture 257 Learn Stata - Bar graphs

Lecture 258 What is a pie chart?

Lecture 259 Learn Stata - Pie charts

Lecture 260 What is a dot chart?

Lecture 261 Learn Stata - Dot charts

Lecture 262 What is a radar plot?

Lecture 263 Learn Stata - Radar plots

Section 24: Data visualisation - two continuous variables

Lecture 264 What is a scatter plot?

Lecture 265 Learn Stata - Scatter plots

Lecture 266 What is a heat plot?

Lecture 267 What is a hex plot?

Lecture 268 Learn Stata - Heat and hex plots

Lecture 269 What is a sunflower plot?

Lecture 270 Learn Stata - Sunflower plots

Lecture 271 What is a polar smoother plot?

Lecture 272 Learn Stata - Polar smoother plots

Lecture 273 What is a line of best fit?

Lecture 274 Learn Stata - Line of best fit plots

Lecture 275 What is a line plot?

Lecture 276 Learn Stata - Line plots

Lecture 277 What is an area plot?

Lecture 278 Learn Stata - Area plots

Lecture 279 What is a range plot?

Lecture 280 Learn Stata - Range plots

Lecture 281 What is a dropline plot?

Lecture 282 Learn Stata - Dropline plots

Lecture 283 What is a rainbow plot?

Lecture 284 Learn Stata - Rainbow plots

Lecture 285 What is a sparkline plot?

Lecture 286 Learn Stata - Sparkline plots

Section 25: Data visualisation - two discrete variables

Lecture 287 What is a jitter plot?

Lecture 288 Learn Stata - Jitter plots

Lecture 289 What is a table plot?

Lecture 290 Learn Stata - Table plots

Lecture 291 What is a balloon plot?

Lecture 292 Learn Stata - Balloon plots

Lecture 293 What is a stacked bar chart?

Lecture 294 Learn Stata - Stacked bar graphs

Lecture 295 What is a mosaic plot?

Lecture 296 Learn Stata - Mosaic plots

Section 26: Data visualisation - three or more variables

Lecture 297 What is a contour plot?

Lecture 298 Learn Stata - Contour plots

Lecture 299 What is a bubble plot?

Lecture 300 Learn Stata - Bubble plots

Lecture 301 What is a Chernoff Face?

Lecture 302 Learn Stata - Chernoff Faces

Lecture 303 What is a Triplot?

Lecture 304 Learn Stata - Triplots

Anyone wanting to work with Stata,Data analysts,Data scientists,Quantitative degree students,Quantitative business users,Economists, Social Scientists, Political Scientists, Biostatisticians, and other disciplines,Those wanting to skill-up in Stata

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