Difference between revisions of "Least squares modelling"
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Kevin Dunn (talk | contribs) (Created page with "<div class="noautonum">__TOC__</div> == Learning outcomes == * Understand the difference between correlation and covariance. * What the objective function of least squares do...") |
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* Identify outlier points and classify them | * Identify outlier points and classify them | ||
* Use the linear model when there are multiple predictor variables (this is what we are building up towards; we will use this extensively in the next topic) | * Use the linear model when there are multiple predictor variables (this is what we are building up towards; we will use this extensively in the next topic) | ||
== Extended readings/practice == | |||
* Run the code below to see how to build and use a linear model in R, but see [[Software_tutorial | step 16 and onwards]] in the R tutorial as well. | |||
* Try some [[Practice_questions|practice problems]]. | |||
* [http://www.nejm.org/doi/full/10.1056/NEJMon1211064 Does eating chocolate lead to winning a Nobel prize]? | |||
Revision as of 15:26, 3 January 2016
Learning outcomes
- Understand the difference between correlation and covariance.
- What the objective function of least squares does
- Understand and use an analysis of variance table
- Calculate and interpret the confidence intervals from a least squares model
- Know about the assumptions required to interpret least squares model coefficients
- Use the prediction error range from the model
- Identify outlier points and classify them
- Use the linear model when there are multiple predictor variables (this is what we are building up towards; we will use this extensively in the next topic)
Extended readings/practice
- Run the code below to see how to build and use a linear model in R, but see step 16 and onwards in the R tutorial as well.
- Try some practice problems.
- Does eating chocolate lead to winning a Nobel prize?