Explains the steps of doing something.
Excel file with regression formulas in matrix form Latest news: If you are at least a part-time user of Excel, you should check out the new release of RegressIt, a free add-in developed by the author of this site.
See it at http: The linear regression version runs on both PC's and Macs and has a richer and easier-to-use interface and much better designed output than other add-ins for statistical analysis.
It may make a good complement if not a substitute for whatever regression software you are currently using, Excel-based or otherwise. If you have been using Excel's analysis toolpak for regression, this is the time to stop. RegressIt now includes a two-way interface with R that allows you to run linear and logistic regression models in R without writing any code whatsoever.
It also includes extensive built-in documentation and pop-up teaching notes. There is a separate logistic regression version with interactive tables and charts that runs on PC's. Regression example, part 1: Where did it come from, how was it measured, is it clean or dirty, how many observations are available, what are the units, what are typical magnitudes and ranges of the values, and very importantly, what do the variables look like?
Much of your brain is devoted to the processing of visual information, and failure to engage that part of your brain is like shooting in the dark.
Visual analysis helps you to identify systematic patterns as well as unusual events and data errors. The objective of this analysis will be to explain and predict how the quantity of weekly sales of a popular brand of beer depends on its price at a small chain of supermarkets.
The data file contains 52 weeks of average-price and total-sales records for three different carton sizes: This is real data, apart from some very minor adjustments for the packs.
One of the first things to consider in assembling a data set for regression analysis is the choice of units i. At the end of the day you will be looking at error measures that are expressed in the units of the dependent variable, and the model coefficients will be measured in units of predicted change in the dependent variable per unit of change in the independent variable.
Ideally these numbers should scaled in a way that makes them easy to read and easy to interpret and compare. In this analysis, the price and sales variables have already been converted to a per-case i.
The first few rows of the data set in an Excel file look like this: The column headings were chosen to be suitable as descriptive variable names for the analysis.
The value of However, there was considerable variation in prices of each carton size, as shown by the minimum and maximum values. Because these are time series variables, it is vitally important to look at their time series plots, as shown below.
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For non-time series data, you would not want to draw connecting lines between the dots, however. What stands out clearly in these plots is that as beer buyers will attest the prices of different carton sizes are systematically manipulated from week to week over a wide range, and there are spikes in sales in weeks where there are price cuts.
For example, there was deep cut in the price of packs in weeks 13 and 14, and a corresponding large increase in sales in those two weeks. In fact, if you look at all the cases-sold plots, you can see that sales volume for every carton size is rather low unless its price is cut in a given week.
High-volume beer drinkers are very price-sensitive. Another thing that stands out is the pattern of price manipulation was not the same for all the carton sizes: So, at this point we have a pretty good idea of what the qualitative patterns are in weekly prices and sales.
If our goal is to measure the price-demand relationships by fitting regression models, we are also very interested in the correlations among the variables and in the appearance of their scatterplots. Here is the correlation matrix, i. What stands out clearly here is that as we already knew from looking at the time series plots there are very strong, negative correlations between price and sales for all three carton sizes greater than 0.
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