Applied regression analysisWiley, 1966 - 407 pages |
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Page 36
... shown in Figure 1.9 and that the sum of squares due to replication is 42.0 . Answer the questions 3a , 3b , and 3c on the basis of this information . 5. Suppose that the actual data plot is as shown in Figure 1.10 , and the sum of ...
... shown in Figure 1.9 and that the sum of squares due to replication is 42.0 . Answer the questions 3a , 3b , and 3c on the basis of this information . 5. Suppose that the actual data plot is as shown in Figure 1.10 , and the sum of ...
Page 108
... shown in Figure 1.4 in Chapter 1. Note the downward trend ; this is reasonable , since as the temperature rises , the need for steam should diminish . 2. Regress Y on X. This straight line regression was performed in Chapter 1 , and the ...
... shown in Figure 1.4 in Chapter 1. Note the downward trend ; this is reasonable , since as the temperature rises , the need for steam should diminish . 2. Regress Y on X. This straight line regression was performed in Chapter 1 , and the ...
Page 170
... shown on page 373. The overall F - test shows that the regression equation is significant . 3. Calculate the partial correlation coefficients of all variables not in regression with the response . Choose as the next variable to enter ...
... shown on page 373. The overall F - test shows that the regression equation is significant . 3. Calculate the partial correlation coefficients of all variables not in regression with the response . Choose as the next variable to enter ...
Contents
CHAPTER | 1 |
THE EXAMINATION OF RESIDUALS | 88 |
TWO INDEPENDENT VARIABLES | 104 |
Copyright | |
12 other sections not shown
Common terms and phrases
actually additional analysis of variance appear applied b₁ b₂ calculations Chapter coefficients column confidence Confidence Limits constant contours Control correct correlation coefficient defined degrees of freedom dependent Determinant discussed distribution effect elements entering equation error estimate examined example explained F Total F-test Figure fitted follows function given independent variables indicates interval involved lack of fit least squares linear matrix mean mean square method multiple nonlinear normal equations Note observations obtained occur operating original orthogonal Overall parameters partial plot possible prediction prediction equation problem procedure regression regression equation Requirements residuals residuals Mean response runs selected shown significant situation solution stage Standard deviation Statistical Step sum of squares Suppose temperature term transformations true usually variation vector write X₁ Y₁ zero