This is a course of webcasts (about 15-30 minutes apiece), designed to introduce you to the basics of statitiscs, primarily as practiced in finance and investing. As with my accounting class, I will start with the open disclosure that my knowledge in statistics is limited to what I use on a regular basis, and that I have no interest (or expertise) in delving into the depths of statistical theory. The class webcasts are right below, followed by links to the statistical tools that I find useful, and readings on each topic. With each session, I also have a post-class test and solution, some more involved than others, testing the grasp of the material in the session.

## Class Webcasts

 Session Webcast Short Description Supplementary Material Preview Class Preview Provides an introduction to (my) version of a statistics class, including a lead in as to why I think it matters in finance. 1 Statistics 101: What and Why? Lay the groundwork for what statistics covers, and why the need for understanding it is greater than ever before. 2 Sampling: Lead In Examine why we use samples instead of looking at populations, and the perils of sampling bias and error. 2A Sampling: Applications Stock indices as samples, albeit non-random, and use of sampling to test investment strategies and effects of events on stock prices. 3 Data Descriptives: Lead In Describe the metrics that we use to describe data, as well as how and why we use them. 3A Data Descriptives: Applications Compute data descriptives for a variety of data in finance, from asset returns over time to PE ratios, costs of capital and dividend yields across companies. 4 Data Distributions: Lead In Examine how we pick distributions to describe data, and the advantages of doing so. 4A Data Distributions: Applications Use stock price and return data to test for normality in distributions. 5 Data Relationships: Lead In Describe how to measure relationships between two or more variables, and how those measurements can be used in prediction/analysis. 5A Data Relationships: Applications Look at both micro and macro examples of regression uses in finance and investing. 5B Data Relationships: More Applications Describe the process of building a multiple regresssion, checking for significance and non-linearities with finance examples. 6 Probabilities: Lead In Lay out the rules on estimating probabilities, and describe probabilistic techniques that can be used in analysis (decision trees, probit/logit, scenario analysis) 6A Probability Tools: Applications Provide examples of decision trees, simulation & probability estimation in finance. 6B Simulations: Applications Look at the use of Monte Carlo simulation in the valuation of a company.

## Statistical Tools

 Tool My comments How to get Microsoft Excel As an Apple loyalist, even I have to admit the world runs on Microsoft Office, and I am no exception. Microsoft Excel is my workhorse for collecting data, and its data analysis toolpack is surprisingly versatile. I can do most of what I want with its pre-set functions, and If I were truly an Excel ninja (which I not) almost everything. Subscribe to Office 365 StatPlus There are many statistical add ons, to Excel, but my favorite (perhaps because it is one of the few that has expended resources to build and maintain a Mac version) is StatPlus. If you are working with a limited budget, it should do the trick for you. StatPlus Home Wizard Pro I find this data analysis program delightfully intuitive, to create pivot tables and to make sense of very large datasets. It is often my first stop, before I go on to StatPlus and SPSS to do statistical analysis. Wizard SPSS There are many stand alone pure statistics packages, and many predate personal computers. I used SPSS on mainframe computers when I first started looking at data, and it is that loyalty, and the fact that SPSS has a Mac version that keep me in its corner. That said, it is overkill for almost everything I do, containing powers that I not only have never used, but would not know how to use. IBM SPSS site Crystal Ball This is my go-to program for simulations. As an add-on to excel, the learning curve is not steep, and it comes with an impressive array of choices for distributions. I have also heard good things about @Risk, and having seen output from it, it seems to do be very similar to Crystal Ball. A significant caveat is that neither program works on a Mac, and I have to do unpleasant (for me) end runs to get around that limitations, including turning my Mac into a PC (a skin-crawling exercise) using Parallels Desktop. Oracle Crystal Ball