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| visits | member for | 1 year, 1 month |
| seen | Jan 22 at 18:07 | |
| stats | profile views | 21 |
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Jan 22 |
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Sum of Squares reference resources Good books on regression that I have read are listed below. A good introduction: Miles, J., & Shevlin, M. (2001). Applying Regression and Correlation: A guide for students and researchers. LA: Sage. More advanced (lucidly written nevetheless): Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence and Erlbaum. |
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Jan 22 |
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Sum of Squares reference resources Few textbooks that I've come across introduce regression by using the term sum of squares. It's more common to introduce the term when referring to analysis of variance (ANOVA). If you're taking a course in statistics I would recommend working on the practicals rather than theory. There will always be more time for theory later :) Try browsing a few introductory text books in statistics at your library. I could provide you with several recommendations on general books, but none of them introduces sum of squares in the context of regression. |
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Jan 22 |
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Differences between groups I hope this helps: statisticshell.com/docs/contrasts.pdf |
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Jan 22 |
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Sum of Squares reference resources New section on SST and SSR |
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Jan 22 |
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Sum of Squares reference resources In a graph with data points along the axes x and y, the total sum of squares SST represents how much the position of each observed data point deviates from the mean. In the same plot, we could draw a line that fits between all the data points. This line approximates the original observations. If we only had access to the line, but not the original observations, we would find that all the estimations of the original data points are - by definition - present on this line. The deviation of our newly estimated points from the mean of the observed values would give us an error term: the SSR. |
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Jan 21 |
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Differences between groups You need to calculate one sum for each treatment. You then apply the contrast on these sums. |
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Jan 21 |
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Differences between groups Will you do calculations by hand or are you using any particular software (SPSS, Excel, R)? It would make things easier to explain. |
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Jan 21 |
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Sum of Squares reference resources Added Sxx, Syy, Sxy |
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Jan 21 |
awarded | Commentator |
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Jan 21 |
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Sum of Squares reference resources Sxx is the distance from the sample (x) to the mean (x bar). Because Sxx is written with Sigma we say "For every x, sum the results of...". Thus, Sxx takes one sample at a time, subtracts the mean, and squares the result. Data set x = 3, 4.5, 6 The mean of x (x bar) = 4.5 y = 1, 4, 12 The mean of y (y bar) = 5.67 Sxx = SQR(3 - 4.5) + SQR(4.5 - 4.5) + SQR(6 - 4.5) Syy = SQR(1 - 5.67) + SQR(4 - 5.67) + SQR(12 - 5.67) Sxy = (3 - 4.5)*(1 - 5.67) + (4.5 - 4.5)*(4 - 5.67) +(6 - 4.5)*(12 - 5.67) I've edited the answer. See above. It's should be easier to read. |
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Jan 21 |
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Sum of Squares reference resources Flipped two sentences. |
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Jan 21 |
answered | Sum of Squares reference resources |
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Jan 21 |
answered | Differences between groups |
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May 2 |
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Weibull distributed data for correlation analysis Great! Looks more normal. Thanks. |
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May 2 |
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Weibull distributed data for correlation analysis OK, so using the Box-Cox SPSS syntax provided in the link above, I was able to estimate the lambda with the least skew at 0.1. Have I then understood correctly that I would need to apply the transform, like so (X^0.1 - 1) / 0.1 |
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May 2 |
asked | Weibull distributed data for correlation analysis |
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May 2 |
awarded | Supporter |
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Apr 30 |
awarded | Student |
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Apr 30 |
awarded | Scholar |
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Apr 30 |
awarded | Editor |
