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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 Squares
To be at ease with general regression terminology and the assumptions behind Ordinary Least Squares
To be able to comfortably interpret and analyze complicated linear regression output from Ordinary Least Squares
To learn tips and tricks around linear regression analysis
To learn and understand the basic statistical intuition behind non-linear regression
To learn and understand how Logit and Probit models work
To be able to comfortably interpret and analyze complicated regression output from Logit and Probit regression
To learn tips and tricks around non-linear Regression analysis
Specific topics that will be covered are:
What kinds of regression analysis exist
Correlation versus causation
Parametric and non-parametric lines of best fit
The least squares method
Beta's, standard errors
T-statistics, p-values and confidence intervals
Best Linear Unbiased Estimator
The Gauss-Markov assumptions
Bias versus efficiency
Zero conditional mean
Regression in logs
Practical model building
Understanding regression output
Presenting regression output
What kinds of non-linear regression analysis exist
How does non-linear regression work?
Why is non-linear regression useful?
What is Maximum Likelihood?
The Linear Probability Model
Logit and Probit regression
Dummy variables in Logit and Probit regression
Odd-ratios for Logit models
Practical Logit and Probit model building in Stata
The computer software Stata will be used to demonstrate practical examples.
Understanding 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 Regression
Interaction Effects - How to Use and Interpret Interaction Effects
Using Time - Exploring Dynamics Relationships with Time Information
Categorical Explanatory Variables - How to Code, Use and Interpret them
Dealing with Multicollinearity - Excluding and Transforming Collinear Variables
Dealing with Missing Data - How to See the Unseen
The Essential Guide to Stata
Learning 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 Stata
Viewing and exploring data
Correlation and ANOVA
Regression including diagnostics (Ordinary Least Squares)
Regression model building
Binary 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)