C532Nov 30 2011

C532Nov 30 2011 - C532: ANOVA & Regression Modeling , 201...

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C532: ANOVA & Regression Modeling 1RY , 201 Y. Cao 1 Topics: Learn more about multiple linear regressions and how to choose the “best” model Datasets : bodyfa.sav To select the possible best regression model, first and very important thing to do is to use all of available resources to assess which variables should be included if they are clinical important. Secondly, we need to resort to statistical tools and theories. This is our main topic today. That is, we use the block method in the procedure of Linear Regression with SPSS; we add one explanatory variable at a time to the model, and compare the R square (R 2 ) change to learn how much (percentage) each new variable contributes to the model! Of course, for each block, we can include more than one variable, but today we always stick to this rule: include only one variable for each block thus we can simplify our solutions. Realistically speaking, we try to make a model the best model as possible as we can; or we may say that there is no such thing – the best model at all! Once we have decided which variables are “the best candidates” to predict the outcome variable, and then we want to use the regular method to do modeling. That is, put all variables we choose to enter the model simultaneously (the block no longer is good for us or more precisely, we only use one block now!). Finally, we can assess our models with the help of residual plots and influence analysis as we have learned from previous course C531. In the following examples, we will not investigate higher order terms or interactions, for instances, terms x 1 x 2 , x 1 x 2 x 3 , X 1 x 2 x 3 x 4 , and so on but no terms like x 1 2 , x 1 2 * x 2 ,… Suppose now, after taking some clinical considerations, we decide to include four important explanatory variables : Age (=x 1 , years), Body Mass Index (or BMI=x 2 , weight (pound) / [height (inch] 2 ), Abdominal circumference (=x 3 , cm) and Hip circumference (=x 4 , cm), to predict the outcome variable , % body fat (y).
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C532: ANOVA & Regression Modeling Dec 1st, 2010 Y. Cao 2 Our first model will be y= Age (continuous, block 1) + BMI (block 2) + Abdominal circumference (cm, block 3) + Hip circumference (cm, block 4), . Go to Analyze > Regression > Linear … click to continue. Select the outcome variable (Percent fat method 1) and x1 (age) . Click on Next to add one more block.
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C532: ANOVA & Regression Modeling Dec 1st, 2010 Y. Cao 3 We add another variable BMI (x2) to the block and hit Next again.
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C532: ANOVA & Regression Modeling Dec 1st, 2010 Y. Cao 4 A third variable x 3 = Abdominal circumference is chosen. Click Next to include the final variable…
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C532: ANOVA & Regression Modeling Dec 1st, 2010 Y. Cao 5 It’s Hip circumference (cm), Hit Statistics
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C532: ANOVA & Regression Modeling Dec 1st, 2010 Y. Cao 6 Now, highlight R squared change , Confidence interval and click on Continue to return.
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C532: ANOVA & Regression Modeling Dec 1st, 2010 Y. Cao 7 Hit OK to see our first model.
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This note was uploaded on 01/03/2012 for the course C 532 taught by Professor Long during the Fall '11 term at Palmer Chiropractic.

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C532Nov 30 2011 - C532: ANOVA & Regression Modeling , 201...

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