Biased Survey Samples - The Risk To Findings, GreatBrook.Sampling Bias and Twitter, Sally Raskoff, Everyday Sociology.The Subtle Sources of Sampling Bias hiding in your data, Sam Ransbotham, MIT Sloan Management Review.
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(While all these readings were accessible at the time that I put together this list, a few came with restrictions (Scientific American allows you a maximum of five free articles, and I have four on my list).
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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. I have also heard good things about and having seen output from it, it seems to do be very similar to Crystal Ball. As an add-on to excel, the learning curve is not steep, and it comes with an impressive array of choices for distributions. This is my go-to program for simulations. 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. 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. There are many stand alone pure statistics packages, and many predate personal computers. It is often my first stop, before I go on to StatPlus and SPSS to do statistical analysis. I find this data analysis program delightfully intuitive, to create pivot tables and to make sense of very large datasets. If you are working with a limited budget, it should do the trick for you.
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.
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. Microsoft Excel is my workhorse for collecting data, and its data analysis toolpack is surprisingly versatile. Look at the use of Monte Carlo simulation in the valuation of a company.Īs an Apple loyalist, even I have to admit the world runs on Microsoft Office, and I am no exception. Provide examples of decision trees, simulation & probability estimation in finance. Lay out the rules on estimating probabilities, and describe probabilistic techniques that can be used in analysis (decision trees, probit/logit, scenario analysis) Look at both micro and macro examples of regression uses in finance and investing.ĭescribe the process of building a multiple regresssion, checking for significance and non-linearities with finance examples.
Use stock price and return data to test for normality in distributions.ĭescribe how to measure relationships between two or more variables, and how those measurements can be used in prediction/analysis. Stock indices as samples, albeit non-random, and use of sampling to test investment strategies and effects of events on stock prices.ĭescribe the metrics that we use to describe data, as well as how and why we use them.Ĭompute 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.Įxamine how we pick distributions to describe data, and the advantages of doing so. Lay the groundwork for what statistics covers, and why the need for understanding it is greater than ever before.Įxamine why we use samples instead of looking at populations, and the perils of sampling bias and error. Provides an introduction to (my) version of a statistics class, including a lead in as to why I think it matters in finance.
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