By Dan Hebert, PE, Senior Technical Editor
Many journalists dont dig for data and dont know how to analyze numbers using basic statistics. Instead, they present sensationalist headlines that generate visceral fears and inaccurately influence opinions.
For example, a common headline over the past few decades suggests that higher oil prices drive stocks down. You know that a declining stock market is not good for sales of your machines, for your career or for your investment portfolio, so the recent rise in oil prices might be causing you great concern. Should it?
There are two ways to back-check a headline. One is to find a trusted source and see what he or she has to say. The other way is to do some basic statistical analysis yourself.
Trusted sources that have been consistently correct in their predictions can explain their opinions in a way that makes sense to you.
My trusted source for money matters is Ken Fisher and his MarketMinder website. The site uses basic fact checking and elementary statistics to disprove often-scary headlines. MarketMinder recently reported: What media types fail to note when howling over higher oil prices is, historically, theres no meaningful correlation between oil and stock prices.
You might want to check the accuracy of this and other headlines, and you also might want to check the opinions of other self-proclaimed experts. Heres how.
Using the Internet and Excel, you can run a correlation analysis between virtually any published data sets from oil prices and stock prices to machine tool sales.
I didnt remember much of college stats, so I turned to an expert for a refresher course. Dr. Terry Woodfield holds a Ph.D. in statistics from Texas A&M and is a statistical services specialist for SAS Institute, probably the worlds most renowned stats firm. He showed me how to use Excel to perform a statistical correlation and interpret the results.
The Excel function =CORREL checks correlation between two sets of data. Correlation values are always between -1 and +1. A correlation of 1 means the two data sets move in perfect lock step, a correlation of -1 means the data sets move in a perfectly opposite fashion. A correlation of 0 means the data sets are random and have no relationship to each other.
Put another way, correlation numbers near 1 or -1 mean that changes in one variable cause the second variable to change positively or negatively, respectively. Correlation numbers near 0 mean that changes in one variable dont cause changes in the second variable.
A good way to get a feel for correlation is to compare two columns of numbers in Excel using the CORREL function. Change the numbers in one column and observe how the correlation value changes.
To check correlation of oil prices and stock prices, I found stock prices at yahoo.com and oil prices at inflationdata.com. I imported monthly data from January 1974 to November 2007 into an Excel spreadsheet.
I chose that time period because it had wide fluctuations in stock and oil prices, it is relatively recent and it contains enough data points to generate a valid correlation. The S&P 500 stock index is a widely used index representing almost half of worldwide stock market capitalization.
When comparing virtually any economic data, one must first strip out inflation effects or else the correlation will always be strongly positive because prices tend to increase over time. This is done by comparing the percentage change from month to month instead of the raw price data.
Comparing percentage change in monthly stock and oil prices over the specified time period yields a negative correlation of -.15. Per Dr. Woodfield, this is a very weak tending toward insignificant negative correlation.
That means that stock prices go down a bit when oil prices go up, but not enough to matter or to worry about.
You can perform similar correlation analysis on any data sets of interest to you and your company and reveal the truth behind the headlines.