The Complete Guide To Stata

Free Download The Complete Guide To Stata
Published 3/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.23 GB | Duration: 25h 52m
Learn how to master Stata like a professional

Free Download What you’ll learn
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
Requirements
There are no requirements
Description
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
Overview
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 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: 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 marginal effects
Lecture 155 How to reduce collinearity in polynomial variables
Lecture 156 How to use contrasting margins
Lecture 157 How to use pairwise comparison with margins
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 timestamp 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 time & 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 download 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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