unit 7 business DC
hootannie
unit_7_discussion_example_post_1.pdf
Unit 7 Discussion Example - Initial Post Download the MM305_DataSets zip file to your computer. Select a data set with at least two numerical variables. You will NOT be able to copy and paste a graph into the Discussion Board, so you should copy your graph to a Word Document and attach it to your post. Determine the following information on your selected data set. Be sure to answer all questions using complete sentences.
1. What are the two variables? Do you think there might be a correlation between the two variables (before you analyze the data)?
2. Create a scatterplot with a simple linear regression (see video in Live Binder). 3. What is the linear regression (prediction line) equation? What is the coefficient of determination r
2 ?
4. Do you think that there is a strong positive or strong negative correlation? Why or why not? Is this result what you expected?
5. Attach the scatterplot to your post. ****************************************************************************************** 1. I choose to download and analyze the UsedCar.xlsx
Two numeric variables are: Age and Price ($)
Yes! I am thinking there might be a correlation between how old a used car is and what it’s selling price is.
2. The scatter plot for age vs price is:
3. The regression analysis output is below. The linear regression equation (prediction line) is :
$0
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
0 2 4 6 8 10 12 14 16
Age vs. Price($)
y = 26,712.80889 - 1512.885x or Price = 26,712.80889 - 1512.885(age)
The coefficient of determination, R Square = 0.331341532.
4. This gives a weak correlation.
5. See above scatter plot.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.575622734
R Square 0.331341532
Adjusted R Square 0.329848991
Standard Error 9306.165871
Observations 450
ANOVA
df SS MS F Significance F
Regression 1 19226096595 19226096595 221.9982 4.57E-41
Residual 448 38798916004 86604723.22
Total 449 58025012599
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0%
Intercept 26712.80889 923.2011973 28.93498077 1.4E-104 24898.47 28527.15 24898.47 28527.15
Age -1512.885 101.5385941 -14.89960554 4.57E-41 -1712.44 -1313.33 -1712.44 -1313.33
