Abstract
In previous chapters, you have learned how to calculate regression coefficients and related terms using a single predictor. Yet most phenomena of interest to scientists involve many variables, not just one. Heart disease, for example, is associated with diet, stress, genetics, and exercise, and school performance is associated with aptitude, motivation, family environment, and a host of sociocultural factors. To better capture the complexity of the phenomena they study, scientists use multiple regression to examine the association among several predictors and a criterion. In this chapter, you will learn how to perform multiple regression analysis using tools you mastered in previous chapters.
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- 1.
As discussed in Chap. 1, these determinants are known as “minors.”
- 2.
The notation r y(i. k) indicates that we are calculating the correlation between y and x i with the variance of x k removed from x i .
- 3.
The spreadsheet function (= DEVSQ) also produces the deviation sum of squares.
- 4.
Because the first row in the adjugate matrix represents the intercept, we use the second row for the b 1 coefficients and the third row for the b 2 coefficients.
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Brown, J.D. (2014). Multiple Regression. In: Linear Models in Matrix Form. Springer, Cham. https://doi.org/10.1007/978-3-319-11734-8_4
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DOI: https://doi.org/10.1007/978-3-319-11734-8_4
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-11734-8
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