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Mass Prediction
Construct a model with mass as the response variable, using stepwise selection. Corn Yield
Choose a model that “best” fits the corn data. |
Mass Prediction: Problem |
A statistics project was carried out in Australia in which body measurements were collected from 22 male subjects. The aim of the project was to construct a model which would predict the mass of a person based on other characteristics.
Use the stepwise selection method to fit such a model using a 0.15 significance level for entry. |
Lee Creighton
SAS Institute
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Mass Prediction: Sample Data | |
The Body_measurements data set is from a project that involved the recording of 11 body measurements (such as mass, arm measurements, chest measurement, height, etc.) for 22 male subjects. These are the variables in the data set: Name | Type | Description | | Mass | num | body mass | | Fore | num | forearm measurement | | Bicep | num | bicep measurement | | Chest | num | chest measurement | | Neck | num | neck measurement | | Shoulder | num | numeric | | Waist | num | waist measurement | | Height | num | height | | Calf | num | calf measurement | | Thigh | num | circumference around one thigh | | Head | num | circumference around head | |
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Source of Data
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Sall, J., Creighton, L., & Lehman, A. (2006). JMP Start Statistics, Third Edition. Cary, NC: SAS Institute Inc. |
Mass Prediction: Solution |
The resulting model for this data fit by the stepwise selection method (using the REG procedure in SAS) is the following:
predicted Mass = -80.4533 +2.1232*Fore + 0.6656*Waist + 0.2770*Height + 0.5232*Thigh – 0.6371*Head
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Corn Yield: Problem |
Data was collected to study the effect of weather related events on corn yield. The variables involved in the study were the total precipitation (in inches) for the year before the growing season, the average daily temperature (Fahrenheit) for the months May through August, the total rain for the months June through August, and corn yield (in bushels per acre). Apply the stepwise selection method to the linear regression of corn yield on the other variables to find the model that “best” fits the data, according to this criterion. |
SAS Institute Inc.
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Corn Yield: Sample Data | |
The Corn data set contains information about weather related events and corn yield. These are the variables in the data set: Name | Type | Description | | CornYield | num | corn yield (in bushels per acre) | | Year | num | year | | Pre_seasonPrecip | num | total precipitation (in inches) for the year prior to the start of the growing season | | May_Temp | num | average daily temperature (in degrees Fahrenheit) for May | | June_Rain | num | total rain (in inches) for June | | June_Temp | num | average daily temperature (in degrees Fahrenheit) for June | | July_Rain | num | total rain (in inches) for July | | July_Temp | num | average daily temperature (in degrees Fahrenheit) for July | | Aug_Rain | num | total rain (in inches) for August | | Aug_Temp | num | average daily temperature (in degrees Fahrenheit) for August | |
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Source of Data
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This data is sample data from SAS Institute Inc. |
Corn Yield: Solution |
Using a significance level for entry and remaining in the model if 0.15, the final results of the stepwise selection show the inclusion of only the July_Temp and July_Rain variables in the “best” fit model. So, the resulting linear regression function based on this selection method is predicted CornYield = 163.47 + 3.49(July_Rain)- 1.68(July_Temp) |
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