The data set provided in StatCrunch contains a random sample of 47 Craigslist posts for various used Honda Civics. A Mason student is interested in purchasing a used Honda Civic and wants to research what variables influence the price of the used cars: the age of the car or the mileage of the car. They recorded the "Price" in US dollars, the "Age" in years (counting from 2021, thus a 10-year-old car was a 2011 since 2021 - 2011 = 10), and the "Mileage" in thousands of miles. Use the "Mileage" in thousands of miles and "Age" in years to predict the "Price" in US dollars. The student wants to find which explanatory variable, either "Mileage" or "Age", will be the best predictor of the response variable, "Price". Investigate the relationship between the explanatory variables and response variable to help the student find the better predictor. The dataset is called "Craigslist Honda Civic Data."

a) Make two separate scatterplots where each scatterplot will present one of the explanatory variables graphed with the response variable "Price." Copy and paste the two scatterplots in your solutions document

b) Interpret the scatterplot of "Age" and "Price" using trend, strength, and shape (form) in one complete sentence.

c) Interpret the scatterplot of "Mileage" and "Price" using trend, strength, and shape (form) in one complete sentence.

d) Calculate both correlation coefficients. Each correlation will be calculated using one of the explanatory variables vs. the response variable "Price". Provide both of these correlation coefficient values in your solutions document.

e) Which of the two explanatory variables would be the better predictor of "Price"? Base your response on the scatterplots and the correlation coefficient. State your answer in one or two complete sentences including an explanation for your variable choice.

f) Using the "Age" variable as the explanatory variable and "Price" as the response variable, run a Simple Linear Regression analysis

g) Copy and paste the fitted line plot for "Age" and "Price" into your solutions document.

h) Type the regression equation for "Age" and "Price" in context into your solutions document. You may copy and paste it from your output in part (f). 6

i) Interpret the slope of the regression line (in context of this data set) for "Age" and "Price".

j) Is it meaningful to interpret the y-intercept for "Age" and "Price"? Why or why not?

k) State r² (i.e., the coefficient of determination) for "Age" and "Price" and explain what this value means in context of the data set.

l) The student finds a used Honda Civic from 2014 for $11,500 and wants to know whether they are getting a good deal on the price. Use the regression equation from part (h) to predict the price for a random Honda Civic from 2014. You can show the typed calculation. State your predicted values in one sentence in context and explain whether or not the student is getting a good deal. Hint: a 2014 car is 7-years-old.

m) Was the prediction you made for the student in part (l) an example of extrapolation? Why or why not? Write your response in one to two complete sentences with an explanation.

use the data set below please

Year	Age	Mileage	Price
2010	11	133	7800
2013	8	105.394	13491
2020	1	39.237	26795
2009	12	213.45	3950
2021	0	1.5	27750
2012	9	101.774	11995
2011	10	68	9500
2015	6	86	13500
2003	18	68	4700
2006	15	140	4500
2008	13	82.583	8500
2011	10	108.165	9995
2015	6	60.016	14977
2008	13	107	9900
2012	9	127.772	9977
2010	11	118.432	4500
2004	17	204	2600
2006	15	244.019	3950
2008	13	184.369	5490
2015	6	60.016	14977
2010	11	96.349	12995
2013	8	123.102	12995
2013	8	118.606	14995
2015	6	150.005	14995
2015	6	34.807	24590
2017	4	39.011	21560
2017	4	74.5	22995
2008	13	184.835	3900
2012	9	222	5800
2017	4	60.104	23457
2017	4	84.003	23495
2009	12	78.5	9000
2012	9	127.772	9977
2018	3	6.157	15450
2010	11	135.987	5900
2016	5	24.755	19990
2015	6	155.9	9425
2013	8	82.141	12995
2003	18	160.75	3500
2010	11	125.33	6500
2016	5	142.128	14995
2012	9	90.139	11995
2008	13	198.386	3700
2017	4	30.219	26990
2009	12	220	3000
2013	8	57.9	13500
2012	9	159.082	5900