10 Stata courses

Learning and applying new techniques can often be a daunting experience. This course helps you out with 100+ tips and tricks on Stata with each video designed to be stand-alone and taking no more than 2 minutes. If you want to learn more about Stata but don't have a lot of time, this is the course for you!

Throughout this course, you will learn everything you need to know about linear and non-linear regression, regression modeling, and Stata. By the end of this course, you will be able to understand and be confident in interpreting complex types of data using Stata.

Learning and applying new visual techniques can often be a daunting experience. This is especially true if you need to generate and code data visualizations yourself. This course focuses specifically on how to create many different types of graphs and all their possible options and sub-options.

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.

Duration 2 Days 12 CPD hours This course is intended for IBM SPSS Statistics users who want to familiarize themselves with the statistical capabilities of IBM SPSS StatisticsBase. Anyone who wants to refresh their knowledge and statistical experience. Overview Introduction to statistical analysis Describing individual variables Testing hypotheses Testing hypotheses on individual variables Testing on the relationship between categorical variables Testing on the difference between two group means Testing on differences between more than two group means Testing on the relationship between scale variables Predicting a scale variable: Regression Introduction to Bayesian statistics Overview of multivariate procedures This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques and how to apply them with confidence, interpret their output, and graphically display the results. INTRODUCTION TO STATISTICAL ANALYSIS * Identify the steps in the research process * Identify measurement levels Describing individual variables * Chart individual variables SUMMARIZE INDIVIDUAL VARIABLES * Identify the normal distributionIdentify standardized scores Testing hypotheses * Principles of statistical testing * One-sided versus two-sided testingType I, type II errors and power Testing hypotheses on individual variables * Identify population parameters and sample statistics EXAMINE THE DISTRIBUTION OF THE SAMPLE MEAN * Test a hypothesis on the population mean * Construct confidence intervals TESTS ON A SINGLE VARIABLE TESTING ON THE RELATIONSHIP BETWEEN CATEGORICAL VARIABLES * Chart the relationship * Describe the relationship TEST THE HYPOTHESIS OF INDEPENDENCE * Assumptions * Identify differences between the groups * Measure the strength of the association Testing on the difference between two group meansChart the relationship * Describe the relationship TEST THE HYPOTHESIS OF TWO EQUAL GROUP MEANS * Assumptions Testing on differences between more than two group means * Chart the relationship * Describe the relationship TEST THE HYPOTHESIS OF ALL GROUP MEANS BEING EQUAL * Assumptions * Identify differences between the group means Testing on the relationship between scale variables * Chart the relationship * Describe the relationship TEST THE HYPOTHESIS OF INDEPENDENCE * Assumptions * Treatment of missing values Predicting a scale variable: Regression * Explain linear regression * Identify unstandardized and standardized coefficients * Assess the fit * Examine residuals * Include 0-1 independent variables INCLUDE CATEGORICAL INDEPENDENT VARIABLES INTRODUCTION TO BAYESIAN STATISTICS * Bayesian statistics and classical test theory * The Bayesian approach * Evaluate a null hypothesis OVERVIEW OF BAYESIAN PROCEDURES IN IBM SPSS STATISTICS OVERVIEW OF MULTIVARIATE PROCEDURES * Overview of supervised models * Overview of models to create natural groupings