MM207M3_Part2.docx Variable Relationships Purdue University Global MM207M3-02: Variab

MM207M3_Part2.docx    Variable Relationships  Purdue University Global  MM207M3-02: Variable Relationships  Variable Relationships     Bear Data  For my data I choose the Bear data I used in the last module. I choose to see if there was any correlation between the two variables Age in Months and Body Length (in Inches). By taking the data from both columns and using the =CORREL function my R value came out being strong at .71877413. Below is the scatterplot for my data including the trendline and the equation for the line which is y = .2281x + 48.69.  My independent variable is the Age in Months and the dependent is Body Length. To test this we will use a bear with the age of 65 months and plug it into the equation. So y = .2281(65) + 48.69, then y = 14.8265 + 48.69, lastly y = 63.52, our bear at 65 months of age would have the body length of 63.52 inches.  I choose these two variables because as the bear ages they grow during that time so the 2 should have a strong relation. With correlation between the variables it does not mean that the one variable will be the cause of change, however in this case our R value is greater than .7 which indicates they do have a strong correlation, leading us to see there that increase in age also causes the growth in body length as well.  We can also compare the equation we solved to data already present, our bear at 68 months showed to have a 73 inch body length, where as our 65 month old bear is estimated to be around 63.52 inches. Now even though thes