proj-lsrl - Final Project: Regression Note #1: There is an...

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Final Project: Regression Note #1: There is an open-ended final project option available to those who'd like a greater challenge . Note #2: You may work with one partner from your section for this project if you wish. If you wish to discuss the project with someone else, work with a partner. Otherwise, work alone. There is to be NO collaboration between individuals/groups who are not working as partners. Theoretical Overview Suppose we have a set of data consisting of ordered pairs and we suspect the x and y coordinates are related. It is natural to try to find the best line that fits the data points. If we can find this line, then we can use it to make all sorts of other predictions. In this project, we're going to use several functions to find this line using a technique called least squares regression . The result will be what we call the least squares regression line (or LSRL for short). In order to do this, you'll be able to reuse some code you've already written (improve it if necessary, of course), as the LSRL is more or less based on statistical calculations we've already automated. You'll need to program one new statistical computation called the correlation coefficient , denoted by r in statistical symbols: Once you have the correlation coefficient, you use it along with the sample means and sample standard deviations of the x and y -coordinates to compute the slope and y -intercept of your regression line via these
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proj-lsrl - Final Project: Regression Note #1: There is an...

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