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[DOC File]www.csus.edu
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The computer output of a regression analysis always contains a table containing the sums of squares SSR, SSE, and SST. The table is called an analysis of variance, or ANOVA, table for reasons we will see later in the course. The table has the form displayed below.
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[DOC File]CHAPTER 11—REGRESSION/CORRELATION
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Fact: Sum of Squares Total = Sum of Squares Regression + Sum of Squares Errors (SST) = (SSR) + (SSE) Proof: NOTE ( SST = SSR + SSE are “definitional” formulas. Computational and alternative forms for these SSQ’s, such as SSR = (B1)2SXX exist and are used in practice. The degrees of freedom of (SSQ’s) also partition.
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[DOC File]FORECASTING
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= SS due to Regression (SSR)}. Also called explained SS. Explained SS + unexplained SS = Total SS ... 8 9 110 119.2 10816 84.64 9 18 240 213.97 676 677.5609 10 30 320 340.33 11236 413.3089 ∑x = 180 ∑y = 2140 SST = 43640 SSE = 1933.6274 ... Sir Ronald Fischer, this is a statistic which essentially compares SSR to SSE. Further, it enables a ...
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[DOC File]AGEC 317
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b) Calculate SST, SSE, and SSR for the estimated equation. In words, why or why not does SST = SSR? c) Calculate the R2 for the equation. Intuitively, why does R2 equal the value you calculated (do not give a mathematical answer)? d) Intuitively, why can’t the for this equation be calculated?
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[DOC File]A fitted value is simply another name for a predicted ...
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SSE. is the total sum of squared deviations about the regression line . . SSR. is the total sum of squared deviations due to regression, i.e. SSR, however, is most easily found by computing the difference SSR = SST – SSE. Interpreting r2: Blank- percent of the variation in y variable is explained by the regression …
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[DOC File]California State University, Sacramento
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One-Factor ANOVA with k levels Multiple Regression with k - 1 independent variables Sums of Squares df Sums of Squares df SSR "Between" k-1 SSR k-1 SSE "Within" n-k SSE n-k SST "Total" n-1 SST n-1 Testing for a Differential Treatment Effect on the Response Variable
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[DOCX File]AMS572 - Stony Brook
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SST = SSR + SSE (2) Please derive the method of moment estimators of the regression model parameters. Solution: (1) SST= ∑ j=1 N Y j - ̅ Y 2 = ∑ j=1 N Y j - Y j + Y j - ̅ Y 2 = ∑ j=1 N [ Y j - Y j 2 +( Y j - ̅ Y ) 2 +2 Y j - Y j Y j - ̅ Y ]
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Sst sse ssr regression calculator