See Figure 2.1.įigure 2.1 You can analyze multiple variables in one pass.įigure 2.1 shows 11 values of two different variables in the range A1:B12. The good news is that using the tool on an existing data set gets you up to 16 different descriptive statistics, and you don’t have to enter a single function on the worksheet. The Descriptive Statistics tool is good news and bad news. COUNT( ) counts the numeric values, AVERAGE( ) returns the mean, and either STDEV.S( ) or STDEV.P( ) gets you the standard deviation.Įxcel comes equipped with a Descriptive Statistics tool in the Data Analysis add-in (which was at one time termed the Analysis ToolPak or ATP). MIN( ) gets you the minimum in a set of values, MAX( ) gets you the maximum, and MAX( ) − MIN( ) gets you the range. If you’re using Excel to analyze the data, either as a preliminary check or as your principal numeric application, one way to carry out this sort of work is to point Excel’s various worksheet functions at the data set. If a mean value, the range of the observed values, or their standard deviation looks unusual, you probably should verify and validate the way the data is collected, entered and stored before too much time passes. You can save yourself a lot of subsequent grief if you just look over some preliminary descriptive statistics based on your data set. In that case, the results of your sophisticated procedure might turn out cockeyed, but you would have no special reason to suspect a missing decimal point as the cause of your findings. The point is that sophisticated multivariate analyses such as factor analysis with Varimax rotation or Cox Proportional Hazards Regression do not alert you when someone entered a patient’s body temperature on Wednesday morning as 986 degrees instead of 98.6 degrees. Then you can take steps to correct data entered in error, or to adjust your decision rules if necessary, or even to replicate the experiment if it looks like something might have gone wrong with the methodology.
No matter what the cause, if your data set contains any unexpected values you want to know about it. The reasons vary from the mundane (someone entered an impossible value for a variable) to the technical (different sample sizes accompanying different variances).Īny of those events could happen, whether the source of the data is a sales ledger, a beautifully designed medical experiment or a study of political preferences. Regardless of the sort of analysis you have in mind for a particular data set, you want to understand the distribution of the variables in that set. Analyzing One Factor by Another: The Contingency Table
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