Ashford University Chapter 10 Stepwise Regression Case Study below are the instructions………………………………………………. this needs t

Ashford University Chapter 10 Stepwise Regression Case Study below are the instructions………………………………………………. this needs to be done in excel Page < 195 > of 292
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What can be the causes for the number of pushups these soldiers score? There are certainly many.
For example, age can be one as a rule is that older soldiers will score fewer pushups than younger
soldiers. But, a 25 year old soldier could do more pushups than a 22 year old. There are other
possible causes. One could be the number of hours of physical training a soldier personally does
each week. Another could be a soldier’s body type. Another could be the amount of sleep the
soldier had the night before the test was conducted. Another could be the test conditions, as scores
could be better in warm, dry weather that in cold, wet weather. Here is a list of possible
contributing factors to the number of pushups scored by any given soldier. You could probably
add many more possible factors.
Table 10-1
Age
Number of hours of preparatory training
Body type
Hours of sleep before the test
Weather conditions at the test site
To forecast the number of pushups one of these soldiers would score, consider collecting data on all
these factors. But this could be very time consuming and expensive. However, if to try to obtain
“good enough” forecasts from just age, then make such forecasts cheaply and quickly when those
forecasts are needed. So, to find out to what degree age is a cause of pushups scored and if that is
good enough for our purposes.
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To do this, build a model of the performance of all soldiers by performing a regression analysis of a
sample, and then borrow a notion from philosophy.
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Step 2. Collect the Data
Suppose the below data constitute this sample (again, remember that these data points are for
teaching purposes only!). Arbitrarily sort this data by soldier age.
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CHAPTER 10R LINEAR REGRESSION
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10-1 Theory
This book takes the approach that at least one reason to use linear regression is to help determine
the degree of cause and effect between two variables. For example, the mileage obtained from a car
is caused, in part, by the number of cylinders in the engine. The scores soldiers achieve from range
firing exercises may be caused, in part, by the number of hours of training they receive. The value
of a used car may be caused, in part, by the mileage on the car. The number of pushups a male
soldier can score within two minutes is caused, in part, by the age of the male. The sales of a fast
food restaurant may be caused, in part, by the size of its surrounding population. Both statistics and
philosophy in this discussion are necessary. This approach will require the entire chapter to
complete.
Consider developing the regression methodology by looking at a (purely hypothetical) military
physical fitness test taken outside in dry, warm weather by 10 randomly sampled male soldiers.
Examine the notion that age is at least partly a contributing cause of the number of pushups each
soldier scores during this test. This example is designed to illustrate certain points about regression.
It is simplistic and “manufactured” to help make the teaching points. Let’s test our conclusions at
alpha level of .05, or, p-value
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