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:Frequency and Histogram |
Using Quantitative Data to create a Frequency Distribution, and graph a Histogram using Excel 2016 pivot table. |
More on Histograms: Accurate Limits
Add Normal Curve | Two additional videos on making Histograms:In the first we talk about how to force Excel to label the endpoints of our classes properly. In the second, we talk about how to add a Normal distribution curve to an existing histogram. Both are pains to do, but here it is in case you need it. |
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 cross-tabulation 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.
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Here we calculate variance, standard deviation, coefficient of variation, range, and interquartile range.
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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
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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
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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
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The Poisson probability distribution and how it works. |
Binomial
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The binomial probability distribution and how to use it. |
Hypergeometric
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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 Scratch-Off 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
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An overview of the idea behind hypothesis testing. |
Review of Hyp.Testing3
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Standard errors, and a z test of proportions for coin flipping data. |
Review of Hypothesis Testing4 z
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Review of several kinds of z-based hypothesis tests, z test for one proportion, two proportions, 1 mean, and two means. |
Review of Hypothesis Testing5 t
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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
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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
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Using a chi-square 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 One-Way 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 NON-Linear 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 R-squared 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(Gauss-Markov 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 p-value. | 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 Kelejian-Prucha Models. |
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