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Statistics/Econometrics

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*FILE* Means that there is an associated file located under "Files" in the left sidebar.  If there isn't a name of the file listed, it will be the same as the video name (or obvious).  If you see me use an Excel file, notes (bullet points or definitions), a data set or Maple file, or anything else that you would find useful that is not available, please let me know.
Statistics Videos:
 
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 ScatterplotI 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 KurtosisHow 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 Scratch-Off Style Tickets
Pick Three and Pick FourThe Binomial Distribution, to analyze Pick 3 and Pick 4 tickets (each balled pulled from separate urns)
LottoThe Hypergeometric Distribution to Analyze multiple balls pulled from one urn.
Lotto with Powerball 



Stats: Continuous Probability Distributions (Uniform, Normal, Exponential) 
Continuous Uniform Distribution Calculations Introduction to continuous distributions; how to solve problems with the continuous Uniform distribution. Includes variance, standard deviation, expected value, and probability calculations.
Uniform distribution variance: Why the 12? Everyone who studies the uniform distribution wonders: Where does the 12 come from in (b-a)^2/12? Here I show you!
Normal Distribution Intro Here we take an introductory look at the Normal distribution and the Empirical Rule.
Normal Dist. Calculations 1: Finding Probailities  How to use a z score table and solve basic problems like: Given an x, what is the probability of being more than x? Or less than x? Or between 2 x's?
Normal Calcs 2: Finding X's  Here we do what I call a "backward problem": given a probability, find the x value that would result in that probability.
Normal Dist. Calcs 3: Finding Two Symmetric X's with Alpha  Here we solve a common problem: Given that say, 95% is in the middle of the normal distribution, how do I find two x's the same distance on either side?  I introduce the concept of Alpha here, the probability that "ain't in the middle" 
   

Introduction to Sampling Distributions

Introduction to Sampling Distributions- Central Limit Theorem and LLN Here we introduce the idea of a sampling distribution and what they are about.  I also discuss the meaning of the central limit theorem and the law of large numbers-- two commonly confused ideas.
Sampling Distribtion: Means and Standard Errors Here we calculate some problems with sample means and sampling distributions.
Sampling Distribution: Proportions Here we calculate some problems with sample proportion sampling distributions.


How to Make Confidence Intervals

Confidence Intervals: An Introduction Here is an overview of the ides of a confidence interval, and some of the terminology used.
Confidence Intervals for Means Here we focus on doing confidence intervals for means, with an introduction to the t distribution.
Confidence intervals for means: Practice Problem Here we do a practice problem making confidence intervals for means with both the t and z, and discuss why you use each.
Confidence Intervals for Proportions Here we look at how to make confidence intervals for proportions.


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 z-based 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 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.

Learn R Free within R! Intro to the "swirl"package.

Econometrics One: Getting Started with Modeling Linear Relationships:
Econometrics PreliminariesAn introduction to the course, and what you should know before you start.
Econometrics basic intuitionIntroducing econometric modeling at a basic level (Lecture 1).
Econometrics  intuition b
Part 2 of lecture 1
Econometrics  intuition cPart 3 of lecture 1.
Econometrics  intuition D1
An example of modeling height and weight with a dummy variable for gender.
Econometrics  intuition D2Example modeling HT/WT with dummies, part 2. 
3d visualization
Visualizing a multiple regression in 3D using Maple.
R Intro 1The basics of using R for statistics. Link to R website (FREE!)
Mailbag: dummy interactionIn a regression, what does interacting TWO dummy variables mean?
Here is the answer!
Mailbag: Notation  

An answer to a viewer question: Why do we see alphas, betas, beta-hat, B's for slopes, and e's, epsilon's and u's for error terms?? Why can't the notation be easier? (Contains some basic+advanced content)

Econometrics Two:Modeling NON-Linear Relationships (Curves):
Econometrics Curves intuition a1 Motivation: How residual plots can tell you about a nonlinear relationship, and intro to logarithms: Important!
Econometrics Curves a2Short continuation of above (1.5 minutes)
Econometrics Curves intuition bUsing logarithms to model curves. Review of logs again, and log-linear models, and start of lin-log
Econometrics Curves clin-log models and log-log models
Econometrics Curves d1Modeling with polynomials
Econometrics Curves intuition d2Second part of modeling with polynomials (short, 1.5 minutes)
R intro 2
Using R to estimate non-linear models

Econometrics Three: OLS Formulas
*File* 3 OLS Formulas.xls
OLS Form aOrdinary least squares regression. How do you calculate OLS slopes and y intercepts?
OLS Form bUsing numbers to calculate a slope and y intercept "by hand"
OLS Form cInterpretation and calculation of R squared and TSS RSS ESS, etc. 
OLS Form dInterpretation of and calculating R-squared and adjusted R squared
R Intro 3aThe third lecture on how to use R, focusing on OLS Formulas.
R Intro 3bContinuation of above.
OLS: A graphical ViewAn intuitive, graphical view of the RSS, ESS, and TSS formulas that is easy to understand and remember.
Derivation of OLS FormulasA derivation of the OLS Slope and Intercept Formulas using calculus. 
Special Cases: DerivationsHere I derive two special case formulas, where we know the slope OR intercept should be = 0.
Centered DataIn 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 aHere we look at how to evaluate a regression and some steps in conducting a regression project.
Using 'metrics bThe steps in doing a regression project.
Using 'metrics cHere we look at a real research study, and evaluate it IN DETAIL (4 videos total)
Using 'metrics dContinuation of the above.
Using 'metrics eYet another continuation...
Using 'metrics fYet ANOTHER continuation of above.
R Intro 4aHere 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 tipsCommon 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
Assumptions of CLRM aThe assumptions of the Classical Linear Regression Model. Part a discusses some preliminary ideas.
Assumptions of CLRM bPart b shows a graphical discussion of "Minimum variance (efficiency)" versus "unbiased" estimators.
Assumptions of CLRM cOverview of the Gauss-Markov Theorem 
Assumptions of CLRM dAssumption 1: Linear in the coefficients, correctly specified, and additive error term.
Assumptions of CLRM eAssumptions 3 and 4: Error term has zero mean and errors are unrelated to the explanatory variables.
Assumptions of CLRM fAssumptions 4 and 5: No serial correlation and no heteroskedasticity.
Assumptions of CLRM gAssumptions 6 and 7: No perfect multicollinearity and normally distributed error term.

Econometrics 6: Statistical Inference
(Also see Crash Course on Hypothesis Testing, listed under Statistics)
Inference A1A review of basic information about the central limit theorem and the normal distribution, the foundation of most statistical inference calculations.
Inference A2Continuation of above
Inference BA review of the Law of Large Numbers and Standard Errors, important for understanding inference. 
Inference CThe epistemology of inference:  That is, what can we learn about the world from collecting data and how do we know it?
Inference DStatistical Inference: Actually Doing it, Pt. 1
Inference EStatistical Inference: Actually Doing it, Pt. 2:  An introduction to the p-value.
Inference FNow that we have DONE some hypothesis tests... we must sit back and think... so what? What does it all mean?
Inference GA 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 IWhy 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 2Using 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. 2bContinuation of above. We focus on Omitted variables bias this time. 
Econometrics Specification 3Specification: 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 AMulticollinearity 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 BContinuation/Discussion of above, Variance Inflation Factors, and what they REALLY mean.

Econometrics 9 Heteroskedasticity
Heteroskedasticity AWhat heteroskeasticity is, why we care, and intro to detection. 
Heteroskedasticity B.aviWhite 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 TSLS in R Part 1A brief overview of endogeneity, and how to do Two Stage Least Squares "by hand".
Econometrics TSLS in R part 2Using R's library for two stage least squares "automatically".
Econometrics TSLS in R part 3How to calculate the TSLS correction factor to fix standard errors, and how to correct heteroskedasticity using White's correction with TSLS.

Econometrics ∞: Advanced Topics
Panel Data ModelsAn introduction to Panel Data and Fixed Effects Models: We use R, too!
*File: Panel Data
Fixed Effects v Random EffectsWhat 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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