# Linear Regressions

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### Question Description

1. Choose any Excel Discussion dataset. Include the name of the dataset. From that dataset, select any two quantitative variables that you suspect will be related (such as age and height for example). What is the name of the dataset you have chosen? Which two variables did you choose?
2. Next, using Excel, calculate the relationship (r value) between the two variables. Recall that the Excel “formula” for correlation is “=CORREL.” What is the r value for the two variables that you have chosen? Is it positive or negative? Is it strong, medium, or weak? Note that it is best to have an r value that is medium or strong. It is recommended that you try a few different variables until you find two variables with an r value between .5 and 1 (or between -.5 and -1).
3. Next, use Excel to create a scatterplot for the two variables. You decide which variable will be dependent (y) and which will be independent (x). On the scatterplot, include the “trendline” and the “equation for the line” using Excel options. Attach your scatterplot to your post.
4. Finally, using the equation of the line that you generated above, plug in any reasonable value for x (your chosen independent variable) and solve the equation for y (your chosen dependent variable). It is up to you to determine which of your two variables is x and which is y. What prediction do you get? Show all your work. In other words, type out the equation, plug in a value for x, and show your solution for y.

### Unformatted Attachment Preview

Color Red Yellow Red Green Green Red Red Yellow Green Brown Yellow Red Brown Brown Green Red Yellow Red Red Red Yellow Green Green Brown Brown Yellow Brown Brown Red Green Yellow Red Green Yellow Weight 0.751 0.841 0.856 0.799 0.966 0.859 0.857 0.942 0.873 0.809 0.89 0.878 0.905 0.873 0.88 0.882 0.931 0.735 0.895 0.865 0.864 0.852 0.866 0.859 0.838 0.863 0.888 0.925 0.793 0.977 0.85 0.83 0.856 0.842 Preference Ranking 1 2 1 3 3 1 1 2 3 4 2 1 4 4 3 1 2 1 1 1 2 3 3 4 4 2 4 4 1 3 2 1 3 2 Day of Production Monday Wednesday Monday Friday Friday Monday Monday Wednesday Friday Thursday Wednesday Monday Thursday Thursday Friday Monday Wednesday Monday Monday Monday Wednesday Friday Friday Thursday Thursday Wednesday Thursday Thursday Monday Friday Wednesday Monday Friday Wednesday ...
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jesusale932
School: University of Virginia

Linear Regressions: Female Health Data

Linear Regressions: Female Health Data
1. Choose any Excel Discussion dataset. Include the name of the dataset. From that
dataset, select any two quantitative variables that you suspect will be related (such
as age and height for example). What is the name of the dataset you have chosen?
Which two variables did you choose?
The name of the dataset that was chosen is “femalehealthdata_1” and the two variables
which were chosen are “Weight” and “Waist”.

2. Next, using Excel, calculate the relationship (r value) between the two variables.
Recall that the Excel “formula” for correlation is “=CORREL.” What is the r value
for the two variables that you have chosen? Is it positive or negative? Is it strong,
medium, or weak? Note that it is best to have an r value that is medium or strong. It
is recommended that you try a few different variables until you find two variables
with an r value between .5 and 1 (or between -.5 and -1).
Using Excel to calculate the relationship of r value between the two variables “Weight” and
“Waist” was obtained that r is equal to 0.921. We can see that r = 0.921 is a positive value
and it is strong uphill linear relationship because it is between 0.7 and 1.0.

Page 1 to 3

Linear Regressions: Female Health Data

3. Next, use Excel to create a scatterplot for the two variables. You decide which
variable will be dependent (y) and which will be independent (x). On the scatterplot,
include the “trendline...

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Anonymous
Tutor went the extra mile to help me with this essay. Citations were a bit shaky but I appreciated how well he handled APA styles and how ok he was to change them even though I didnt specify. Got a B+ which is believable and acceptable.

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