Regression

# Regression - Cannot force a zero intercept Only fits linear...

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Regression

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A common engineering task is to fit experimental data to a mathematical model. Often the model is simply a fit to a straight line. Other times the model may be quite complex. Excel has several tools to fit models to data.
Consider the following data. What is the best linear fit? The fit that minimizes error (best fit) can be obtained using statistical arguments.

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There are several methods for modeling trends to data in Excel. Each has its own strengths and weaknesses. Builtin functions for linear fit: Slope(y,x) Intercept(y,x) rsq(y,x) Advantages: Updates when data is changed Results are easily used in further calculations Disadvantages:

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Unformatted text preview: Cannot force a zero intercept Only fits linear data Trendline Fit with Chart Tool Fits several linear regression models besides y=ax+b (see Larsen pp. 160-162) Advantages: Updates when data is changed Multiple model types. Can force model through defined intercept Disadvantages: Equation is not directly transferable to calculations Data Analysis/Regression Fits models of the type: y(x) = A f 1 (x) + B f 2 (x) + … Advantages: Very flexible model types. Can force model through origin Results are useable in further calculations Disadvantages: Fit does not update when original data is changed Must re-run analysis...
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Regression - Cannot force a zero intercept Only fits linear...

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