NAU EPS 625 - BLOCK ENTRY MULTIPLE REGRESSION (6 pages)

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BLOCK ENTRY MULTIPLE REGRESSION



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EPS 625 INTERMEDIATE STATISTICS BLOCK ENTRY MULTIPLE REGRESSION EXAMPLE A researcher conducted a study to evaluate a strength injury hypothesis She collects data on a group of 100 elderly women Specifically the study was designed with five predictor variables divided into lower body strength measures QUADRICEPS GLUTS and ABDOMENS and upper body strength measures ARMS and GRIP The researcher sets out to determine how accurately can a physical INJURY index injury which is the number and severity of accidents be predicted from a linear combination of five different strength measures for elderly women Specifically she wants to know 1 How well can a physical injury index be predicted from a linear combination of strength measures in elderly women 2 How well do the lower body strength measures predict the physical injury index for elderly women 3 How well do the upper body strength measures predict the physical injury index for elderly women 4 For elderly women how well do the upper body strength measures predict the physical injury index over and above the lower body strength measures The dataset for this example is on the web site labeled Injury Index MLR Dataset The dataset contains a total of 6 variables for a sample of 100 elderly women For this example you will use an a priori alpha level of 05 05 for all analyses Your dependent measure will be the participants physical injury index INJURY which is based on the records kept by the participant s physician and the independent variables are as follows quadriceps A measure of strength primarily associated with the quadriceps gluts A measure of strength of the muscles in the upper part of the back of the leg and the buttocks abdomens A measure of strength of the muscles of the abdomen and the lower back arms A measure of strength of the muscles of the arms and the shoulders grip An assessment of the hand grip strength injury Overall injury index based on the records kept by the participant s physician Once you have



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