Most of the videos below are targeted toward an introductory statistics course, from the basics up through a crash course in statistical inference.
Stats: Basics
Stats Intro Vocabulary 1 
Boring but necessary stats vocabulary (First half): e.g. Qualitative, categorical, parameter, statistic, ratio vs. interval data, etc. 
Stats Intro Vocabulary 2 
Continuation of above 
(OLD) Stats Tables and Graphs 1 
Frequency Distribution, Graphing Categorical data, Bar Graph, Excel pivot table 
(OLD) Stats Tables and Graphs 2 
Frequency Distribution, Graphing Quantitative data, Excel pivot table, Histogram. 
Categorical Data in Excel 2016: Update 
Here we make frequency distributions two ways: First using the COUNTIF function, and then using a Pivot Table. 
Quantitative Data in Excel 2016: Update 
Using Quantitative Data to create a Frequency Distribution, and graph a Histogram using Excel 2016 pivot table. 
Quantitative Data and CUMULATIVE distributions in Excel 2016 
In this video we make a Cumulative Frequency Distribution, and make a graph of an Ogive. 
Crosstabulations in Excel 2016 
Here we use Excel's PivotTable feature to make a crosstabulation in Excel. 
Making Scatterplots and Trendlines 
How to make and understand scatterplots and trendlines in Excel 2016. 
How to ID a point in a Scatterplot  I show a couple of quick ways to find a scatter plot point in your data set. 
Stats: Numerical Descriptive Statistics
Mean, Median, Mode 
Some details about how to find and interpret the mean, median, and mode. 
Percentiles, Quartiles, and Simple Box Plots 
How to calculate and interpret percentiles, quartiles, range, interquartile range, and make a simple boxplot. 
Dispersion: Variance, Std. Dev. Etc.

Here we calculate variance, standard deviation, coefficient of variation, range, and interquartile range.

Skewness and Kurtosis  How to think about skewness and Kurtosis Measures 
Stats: Introduction to Probability
Intro Probability A 
Preliminary rules and symbols used in probability 
Intro Probability B 
Complement and addition rules for probability 
Intro Probability C 
Conditional probability rule, multiplication rule, and statistical independence. 
Intro Probability D 
Multiplication rule for independent events, and what I call Burkey's rule of common sense for probability. 
Intro Probability E 
Using probability rules with a joint probability table: Calculating unions, finding intersections and marginal probabilities in a table. 
Intro Probability F 
Using a joint probability table to calculate conditional probabilities, and check for statistical independence. 
Probability G Bayes Rule

A derivation of Bayes' rule, a numerical example so that you can see WHY it works, and an example calculation using drug tests. 
Stats: Discrete Probability Distributions
(Tables, Poisson, Binomial, and Hypergeometric)
Discrete Numeric Probability Distributions Overview

An overview of what a discrete numeric probability distribution is, and three examples: the Poisson, binomial, and hypergeometric. I just give an overview here, no formulas. Further videos focus on each distribution individually. 
Basic Formulas for Discrete Distributions 
Mean, Variance, and Standard Deviation for discrete probability distributions, working with tables. 
Poisson

The Poisson probability distribution and how it works. 
Binomial

The binomial probability distribution and how to use it. 
Hypergeometric

The hypergeometric probability distribution and how to use it. An example using cards is given. 
Discrete Distributions: Applications with Lottery Tickets (coming soon) Scratchers  Basic Formulas applied to ScratchOff Style Tickets  Pick Three and Pick Four  The Binomial Distribution, to analyze Pick 3 and Pick 4 tickets (each balled pulled from separate urns)  Lotto  The Hypergeometric Distribution to Analyze multiple balls pulled from one urn.  Lotto with Powerball  
Stats: Continuous Probability Distributions (Uniform, Normal, Exponential)
Introduction to Sampling Distributions
How to Make Confidence Intervals
Crash Course (or Review) of Hypothesis Testing
Review of Hypothesis Testing1 
Review of basic statistical ideas needed for hypothesis testing: deviation, standard deviation, empirical rule, and z scores. 
Review of Hyp.Testing2

An overview of the idea behind hypothesis testing. 
Review of Hyp.Testing3

Standard errors, and a z test of proportions for coin flipping data. 
Review of Hypothesis Testing4 z

Review of several kinds of zbased hypothesis tests, z test for one proportion, two proportions, 1 mean, and two means. 
Review of Hypothesis Testing5 t

A comparison of many different types of t tests that you might see, and how they are similar. Hypothesis testing using a t with one mean, paired t tests, and two independent samples. 
Review of Hypothesis Testing6 x2

A review of what the chi square distribution is, and 3 common chi square tests. Testing a sample variance, test of independence (contingency table), and a goodness of fit test. 
Review of Hyp.Testing6 x2 ci

Using a chisquare distribution to make a confidence interval for a population variance based on a sample variance. 
Review of Hypothesis Testing7 F 
Introduction to the F distribution and F tests: Comparing two sample variances and a OneWay Anova.*FILE* HotDog.xls 
 Introductory Econometrics Course Each section includes lectures on the ideas and theories, as well as a "How To" section using the R Statistics Package (FREE!) In my course, you learn the WHY and WHAT everything means.
Econometrics One: Getting Started with Modeling Linear Relationships: Econometrics Two:Modeling NONLinear Relationships (Curves): Econometrics Three: OLS Formulas *File* 3 OLS Formulas.xls OLS Form a  Ordinary least squares regression. How do you calculate OLS slopes and y intercepts?  OLS Form b  Using numbers to calculate a slope and y intercept "by hand"  OLS Form c  Interpretation and calculation of R squared and TSS RSS ESS, etc.  OLS Form d  Interpretation of and calculating Rsquared and adjusted R squared  R Intro 3a  The third lecture on how to use R, focusing on OLS Formulas.  R Intro 3b  Continuation of above.  OLS: A graphical View  An intuitive, graphical view of the RSS, ESS, and TSS formulas that is easy to understand and remember.  Derivation of OLS Formulas  A derivation of the OLS Slope and Intercept Formulas using calculus.  Special Cases: Derivations  Here I derive two special case formulas, where we know the slope OR intercept should be = 0.  Centered Data  In this brief visualization, I illustrate why when you subtract the means from your data in a regression, it does not change the slopes, but sets the y intercept to zero. 
Econometrics Four: USING 'Metrics Using 'metrics a  Here we look at how to evaluate a regression and some steps in conducting a regression project.  Using 'metrics b  The steps in doing a regression project.  Using 'metrics c  Here we look at a real research study, and evaluate it IN DETAIL (4 videos total)  Using 'metrics d  Continuation of the above.  Using 'metrics e  Yet another continuation...  Using 'metrics f  Yet ANOTHER continuation of above.  R Intro 4a  Here we talk about modeling using some data in R.  R Intro 4b *FILE* R4sumstats  Copying, pasting, and formatting regression results for use in a paper or report.  Burkey's writing tips  Common writing problems that economists complain about. Some common mistakes everyone should avoid, even me! 
Econometrics 5: Assumptions of the CLRM(GaussMarkov Theorem) *FILE* 5 CLRM.docx
Econometrics 6: Statistical Inference (Also see Crash Course on Hypothesis Testing, listed under Statistics) Inference A1  A review of basic information about the central limit theorem and the normal distribution, the foundation of most statistical inference calculations.  Inference A2  Continuation of above  Inference B  A review of the Law of Large Numbers and Standard Errors, important for understanding inference.  Inference C  The epistemology of inference: That is, what can we learn about the world from collecting data and how do we know it?  Inference D  Statistical Inference: Actually Doing it, Pt. 1  Inference E  Statistical Inference: Actually Doing it, Pt. 2: An introduction to the pvalue.  Inference F  Now that we have DONE some hypothesis tests... we must sit back and think... so what? What does it all mean?  Inference G  A trip to the courtroom for a trial: A metaphor for understanding type 1 and type 2 errors, power, confidence levels, and the relationship between alpha and beta in hypothesis testing.  Inference H
 Discover Your Inner Alpha: Why do people talk about alpha=.05 in hypothesis testing? Why do some people use .10 or .01? Discover your inner alpha in this experiment, your willingness to commit a type 1 error.  Inference I  Why use a z, t, F, or chi square distribution? In this part, you can see where these distributions come from.  Inference J  Why use a z, t, F, or chi square distribution? In Part J we look at some common statistical tests, and you get to see why they have a particular distribution. 
Econometrics Seven: Specification and Variable Selection For information about functional form selection see the first two sections: modeling linear/nonlinear relationships Econometrics Specification 1  An introduction to specification. How do you select the right set of explanatory variables? Introduction.  Econometrics Specification 2  Using Venn Diagrams, we look at where the INFORMATION used to calculate slopes/standard errors comes from, and look at multicollinearity. This is important when you are concerned about R squared, statistical significance, etc.  Econometrics Spec. 2b  Continuation of above. We focus on Omitted variables bias this time.  Econometrics Specification 3  Specification: Selecting the Variables for an Econometric Model. There is no one right way, but many wrong ways... I discuss some common methods such as I throw in some quotes about econometrics for fun. 
Econometrics Eight: Multicollinearity Econometrics Multicollinearity A  Multicollinearity increases standards errors. So, this video focuses on the calculation of standard errors in a regression. We calculate some by hand (using R and Excel).  Multicollinearity B  Continuation/Discussion of above, Variance Inflation Factors, and what they REALLY mean. 
Econometrics 9 Heteroskedasticity Heteroskedasticity A  What heteroskeasticity is, why we care, and intro to detection.  Heteroskedasticity B.avi  White Test, Brief info on Park Test and Goldfield Quandt, and White/Huber/Eicker Sandwich Estimators  Heteroskedasticity C *File white function*  Applied Heteroskedasticity in R:Check for heteroskedasticity "by hand" with White/Breusch Pagan tests, define a function to apply White/Huber/Eicker correction using the lmtest and car libraries. 
Econometrics 10 Two Stage Least Squares Econometrics ∞: Advanced Topics Panel Data Models  An introduction to Panel Data and Fixed Effects Models: We use R, too! *File: Panel Data  Fixed Effects v Random Effects  What is the difference, and why should I care? Hausman test. We use R!    Spatial Econometrics 0  An overview of the new project on Spatial Econometrics. No content, but just announcing what I plan to do.   A 30 minute lecture on what spatial econometrics is, and the major types of models used: Spatial Lag, Error, Durbin, Manski, and KelejianPrucha Models. 


