How to find a job with Stata skills

1. The STATA OMNIBUS: Regression and Modelling with STATA

4.6

(676)

Make sure to check out my twitter feed for monthly promo codes and other updates (@easystats3).4 COURSES IN ONE! Learn everything you need to know about linear regression, non-linear regression, regression modelling and STATA in one package. Linear and Non-Linear Regression. Learning and applying new statistical techniques can often be a daunting experience. Easy Statistics is designed to provide you with a compact, and easy to understand, course that focuses on the basic principles of statistical methodology. This course will focus on the concept of linear regression and non-linear regression. Specifically Ordinary Least Squares, Logit and Probit Regression. This course will explain what regression is and how linear and non-liner regression works. It will examine how Ordinary Least Squares (OLS) works and how Logit and Probit models work. It will do this without any complicated equations or mathematics. The focus of this course is on application and interpretation of regression. The learning on this course is underpinned by animated graphics that demonstrate particular statistical concepts. No prior knowledge is necessary and this course is for anyone who needs to engage with quantitative analysis. The main learning outcomes are: To learn and understand the basic statistical intuition behind Ordinary Least SquaresTo be at ease with general regression terminology and the assumptions behind Ordinary Least SquaresTo be able to comfortably interpret and analyze complicated linear regression output from Ordinary Least SquaresTo learn tips and tricks around linear regression analysisTo learn and understand the basic statistical intuition behind non-linear regressionTo learn and understand how Logit and Probit models workTo be able to comfortably interpret and analyze complicated regression output from Logit and Probit regressionTo learn tips and tricks around non-linear Regression analysisSpecific topics that will be covered are: What kinds of regression analysis existCorrelation versus causationParametric and non-parametric lines of best fitThe least squares methodR-squaredBeta's, standard errorsT-statistics, p-values and confidence intervalsBest Linear Unbiased EstimatorThe Gauss-Markov assumptionsBias versus efficiencyHomoskedasticityCollinearityFunctional form Zero conditional mean Regression in logsPractical model buildingUnderstanding regression outputPresenting regression outputWhat kinds of non-linear regression analysis existHow does non-linear regression work?Why is non-linear regression useful?What is Maximum Likelihood?The Linear Probability ModelLogit and Probit regressionLatent variablesMarginal effectsDummy variables in Logit and Probit regressionGoodness-of-fit statisticsOdd-ratios for Logit modelsPractical Logit and Probit model building in StataThe computer software Stata will be used to demonstrate practical examples. Regression ModellingUnderstanding how regression analysis works is only half the battle. There are many pitfalls to avoid and tricks to learn when modelling data in a regression setting. Often, it takes years of experience to accumulate these. In these sessions, we will examine some of the most common modelling issues. What is the theory behind them, what do they do and how can we deal with them? Each topic has a practical demonstration in Stata. Themes include: Fundamental of Regression Modelling - What is the Philosophy?Functional Form - How to Model Non-Linear Relationships in a Linear RegressionInteraction Effects - How to Use and Interpret Interaction EffectsUsing Time - Exploring Dynamics Relationships with Time InformationCategorical Explanatory Variables - How to Code, Use and Interpret themDealing with Multicollinearity - Excluding and Transforming Collinear VariablesDealing with Missing Data - How to See the UnseenThe Essential 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 this course 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. Like for other professional statistical packages the course focuses on the proper application - and interpretation - of code. The course is aimed at anyone interested in data analytics using Stata. 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)...

2. 125 Quick Stata Tips

4.7

(61)

Make sure to check out my twitter feed for monthly promo codes and other updates (@easystats3)If you want to learn more about Stata but don't have a lot of time, this is the course for you! In this course I provide 125 fast and to-the-point tips for Stata. These tips are professional grade and aimed at helping you become a Stata master! They cover a wide range of issues in data management, graphing, statistics and programming. Each video is designed to be stand-alone and will take no more than 2 minutes. Learn years worth of hard Stata knowledge in 3 hours! You should have basic knowledge of Stata and do-files. If you do not have this check out my Essential Guide to Stata. The following themes are covered: Data ManagementHow to create a code bookHow to create a label bookHow to list only variable namesHow to describe unopened dataHow to search in variablesHow to drop/keep variables sequentiallyHow to check a digital data signatureHow to verify dataHow to compare two datasetsHow to compare variablesHow to use tabulate to generate dummy variablesHow to avoid many logical OR operatorsHow to number labelsHow to use labels in expressionsHow to attach one value label to many variablesHow to store single valuesHow to use Stata's hand-calculatorHow to use text with Stata's hand-calculatorHow to select column of data in a do-fileHow to rectangularize dataHow to check if variables uniquely identify observationsHow to drop duplicate observationsHow to draw a sampleHow to transpose a datasetHow to quickly expand and interact many variablesHow to create publication quality tables in wordHow to create publication quality tables in excelHow to export regression resultsHow to delete files from within StataHow to display directory contentHow to clone a variableHow to re-order variablesHow to add notes to dataStatisticsHow to create many one-way tables quicklyHow to create many two-way tables quicklyHow to sort and plot one-way tablesHow to expand data instead of using weightsHow to contract data to frequencies and percentagesHow to compute immediate statistics without loading dataHow to compute elasticitiesHow to set the default confidence levelHow to show base levels of factor variablesHow to estimate a constrained linear regressionHow to bootstrap any regressionHow to interpolate missing valuesHow to compute row statisticsHow to compute standardized coefficients after linear regressionHow to compute faster marginal effectsHow to reduce collinearity in polynomial variablesHow to use contrasting marginsHow to use pairwise comparison with marginsHow to define the constant in a regressionHow to visualise complex polynomial modelsHow to identify outliers from a regressionHow to inspectProgrammingHow to hide unwanted outputHow to force show wanted outputHow to hide a graphHow to suppress error messagesHow to force do-files to run to the endHow to execute programmes outside StataHow to check memory usageHow to reduce files sizesHow timestamp commandsHow to set a stopwatchHow to pause StataHow to debug error messagesHow to pause for large outputHow to add custom ado foldersHow to create a custom user profileHow to add comments to do-filesHow to loop over non-integer valuesHow to monitor a loopHow to show more in the results windowHow to display coefficient legendsHow to squish a tableHow to use and modify the Function keysHow to view command sourcecodeHow to create custom correlationsHow to insert current time & date into log filesHow to save interactive commandsHow to launch the interactive dialog boxHow to view undocumented commandsGraphingHow to recover data from a graphHow to generate a combined graph with one legendHow to display RGB colors in graphsHow to make colors opaqueWhy are SVG graphs useful?How to apply log scaling to a graphHow to reverse and switch off axesHow to have multiple axes on a graphHow to display ASCII characters in graphsHow to graph the variance-covariance matrixHow to quickly plot estimated resultsHow to randomly displace markersHow to range plotHow to download word frequencies from a webpageHow to create a violin plotHow to show the Stata color paletteHow to create custom titlesHow to customize the look of graphsHow to show a correlation matrix as graphical tableHow to plot a histogram with a boxplotHow to draw histograms with custom binsHow to graph a one/two/three-way tableHow to recover graph codeHow to do polar smoothingHow to separate scatterHow to range a graphHow to foreground/background plotHow to plotstyleHow to show multiple axesHow to quickly increase graph label ticksHow to view undocumented commandsHow to add notes to dataHow to add custom graph label ticks...

3. Logistic Regression using Stata

4.8

(142)

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind logistic regression. The theory is explained in an intuitive way. The math is kept to a minimum. The course starts with an introduction to contingency tables, in which students learn how to calculate and interpret the odds and the odds ratios. From there, the course moves on to the topic of logistic regression, where students will learn when and how to use this regression technique. Topics such as model building, prediction, and assessment of model fit are covered. In addition, the course also covers diagnostics by covering the topics of residuals and influential observations. In the second part of the course, students learn how to apply what they learned using Stata. In this part, students will walk through a large project in order to understand the type of questions that are raised throughout the process, and which commands to use in order to address these questions...

4. An Introduction to Stata

4.3

(560)

This is an introductory course to Stata. The course assumed to previous knowledge of the software nor any statistical knowledge. The course does not teach statistics. The goal of the course is to teach students about the basic functionality of Stata and how it can be used to analyze large data sets. The course contains two projects for students to work on. It also provides a step-by-step approach in covering all of the material where I go through the commands one by one. In addition to the video lectures, I have included the scripts of the lectures so that students can also study and revise the material without having to watch videos. Although Stata comes with many data sets, this course utilizes my own data sets in order to explain to students the thought process involved in collecting data...

5. Visualizing data using Stata

4.8

(55)

This course introduces the student to the graphical capabilities of Stata. The course assumes only basic knowledge of data management in Stata. The student should be familiar with the graphical user interface, as well as with loading data sets into memory. The goal of this course is to teach the student the logic of extracting meaning from data sets using visualization tools. This is accomplished by using a single data set from the start of the course up until the very end. Students will learn how to use histograms, quantile plots, and symmetry plots. In addition, students will also learn how to use these tools in order to investigate whether group differences exist. The course then introduces students to bar graphs, box plots, and dot plots, and how these graphs can be used to study differences in groups that are divided along more than one dimension. Finally, the course shows students how to produce graphs that describe the relationship between two variables. Students are taught how to decide which type of plot is best suited for their needs. Throughout the course, students will also learn how to customize the colors and shapes used in the graphs...

6. Linear Regression using Stata

4.2

(165)

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind linear regression. The theory is explained in an intuitive way. No math is involved other than a few equations in which addition and subtraction are used. The purpose of this part of the course is for students to understand what linear regression is and when it is used. Students will learn the differences between simple linear regression and multiple linear regression. They will be able to understand the output of linear regression, test model accuracy and assumptions. Students will also learn how to include different types of variables in the model, such as categorical variables and quadratic variables. All this theory is explained in the slides, which are made available to the students, as well as in the e-book that is freely available for students who enroll in the course. In the second part of the course, students will learn how to apply what they learned using Stata. In this part, students will use Stata to fit multiple regression models, produce graphs that describe model fit and assumptions, and to use variable specific commands that will make the output more readable. This part assumed very basic knowledge of Stata...

7. The Complete Guide to Stata

4.6

(68)

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

8. Complete STATA Workflow + Tips

4

(1,062)

Most students enrolled in a Stata course: +3,800Most ratings in a Stata course: +900 averaging +4,3═════════════════════════════════════════════════════Some Student Reviews are: STATA system display is matching my expectation. (December 2020)Have used Stata for advanced analysis in the past but within the first two sections of the course I got exposed to very useful tips. Kudos to the instructor for putting this together. (May 2019). The instructor has done a good job of introducing Stata and provides some very good examples. (March 2018). For someone who has never had exposure to STATA before I really enjoy the way this is taught. (December 2017).═════════════════════════════════════════════════════Hi! My name is Mauricio and I want you to be a PRO in STATA. Over the years, I've learned that STATA is a powerful data analysis software (data management, graphs and statistics):>>> If you are an undergraduate or graduate student, you may know what quantitative analysis you need, but you may experience difficulties using STATA to get those results, making your research harder.>>> If you are a professional and you already have some STATA knowledge, you can also benefit from this course by jumping straight into those sections you need the most. The plan of this course is to give you the BEST WORKFLOW ever. Each video provides the best practices coupled with tips and hints that will boost your STATA work. So, less time learning STATA, and more time getting results out of it! With more than +100 detailed lectures and +9.5 hours of video, you'll get the best way to handle STATA and you will have LIFETIME access too! Be sure to enroll now and use all resources to get the most of it: lectures, exercises, messages and more. See you inside,-M. A. Mauricio M...

9. Modeling Count Data using Stata

4.7

(56)

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind count models. The theory is explained in an intuitive way while keeping the math at a minimum. The course starts with an introduction to count tables, where students learn how to calculate the incidence-rate ratio. From there, the course moves on to Poisson regression where students learn how to include continuous, binary, and categorical variables. Students are then introduced to the concept of overdispersion and the use of negative binomial models to address this issue. Other count models such as truncated models and zero-inflated models are discussed. In the second part of the course, students learn how to apply what they have learned using Stata. In this part, students will walk through a large project in order to fit Poisson, negative binomial, and zero-inflated models. The tools used to compare these models are also introduced...

10. The Essential Guide to Stata

4.5

(607)

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)...