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CASE V & VI Linear regression In Excel
- 1. How to Build Regression Models in Excel Ashutosh Nandeshwar @n_ashutosh Advancement Services track sponsored by Blackbaud Follow the Better Together Conference on Twitter #CASEV+VI
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- 3. What is it? @n_ashutosh
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- 6. When not to? @n_ashutosh
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- 13. What else? @n_ashutosh
- 14. Today’s session evaluations will be e-mailed to you. Session handouts are available at www.casefive.org/conference/presentations/ Username: CASEV-VI | Password: Chicago 2012 @n_ashutosh Follow the Better Together Conference on Twitter by using the #CASEV+VI hashtag when you tweet!
Editor's Notes
- #3 . For segmentation, quick scoring, modeling, giving, easier options, forecasting, predicting, any analyst worth his money should ask why and so what?
- #4 We won’t go in all details here, but we can talk about a few things.
- #5 At its essence, linear regression is about minimizing the distance between the predicted value of an observation and the actual value of an observation, the technique is called least-squares
- #6 If you want to use Excel, then all the variables should be numeric or coded as numeric. You have enough data, but not a whole lot of data. You are just getting started in modeling.
- #7 If your data is quite skewed or the parameters have non-linearity , or you suspect that variables are quite similar.
- #10 This is the exciting or not-so exciting part, where you actually learn how to do this.
- #11 Missing data, lots of variables, low accuracy, R2 < 0.5, unable to take advantage of various newer methods, variable selection is manual.
- #14 Weka, decision tree, attribute selection, R