Introduction: Briefly describe your sample and your two quantitative variables, including their units of measurement. State that the purpose of your analysis is to use linear regression to predict price based on mileage. - 1 points

Insert a scatter plot with mileage on the x axis and price on the y-axis. Conduct simple linear regression, with mileage as the x (independent) variable and price as the y (dependent) variable. Copy/paste your scatterplot and regression results into the report. - 2 points

Can someone help with this portion, please.

Here is what I have down.

Here is the information I created.

1 The population of interest in this project is Chevrolet Camaros. The sample has been collected on 2018 Chevrolet Camaros within 2000 miles 33709 with variables of interest are Price of cars with level of measurement as ration scale of measurement. Mileage of car as ration scale of measurement. Color of car as nominal scale of measurement. Convertible or not as nominal scale of measurement.

2 The sampling method used is simple random sampling data of 30 cars have been taken randomly from a car website.

3 Descriptive statistics for the quantitative variables Price and Mileage is given.

Price Mileage

Mean 36008.967 Mean 34801.533

Variance 1.8030605e8 Variance 6.649222e8

Stand. Dev. 13427.809 Stand. Dev. 25786.085

Stand. Error 2451.5712 Stand. Error 4707.8736

Median 30194 Median 26176.5

Range 45676 Range 108228

Min 18283 Min 9532

Max 63959 Max 117760

Q1 27385 Q1 15614

Q3 41990 Q3 42099

Sum 1080269 Sum 1044046

Total 30 Total 30

4 Histogram for both the Price and Mileage are shown below:

5 The boxplot for both the Price and the Mileage are shown below.

The Sale Price boxplot is

The Mileage boxplot shows that it is

The frequent distribution for Exterior Color variable is shown below:

Exterior Color	Frequency	Relative Frequency
Black	9	0.3
Blue	3	0.1
Burgundy	1	0.033333333
Gray	4	0.13333333
Gray/Black	1	0.033333333
Red	8	0.26666667
White	2	0.066666667
White/Black	1	0.033333333
Yellow	1	0.033333333

Based on the pie chart, we can say that most of the people prefer the red color car with black following behind.

Sale Price Mileage Exterior Color Convertable or Not 26301 38439 Black No 30888 38654 Black No 28861 42099 Black No 21500 91844 Red Yes 22399 58458 Gray No 28957 37323 Black Yes 59943 17962 Red No 35250 20163 Black No 25345 60647 Black No 27385 22468 Gray Yes 61988 14188 Gray No 18283 117760 Black Yes 27700 14918 Blue No 28998 25440 Gray Yes 40840 12889 Black Yes 59943 17962 Red No 26617 32851 Red No 35825 33112 Gray/Black No 32897 44017 Yellow Yes 42387 19136 Red No 29500 26913 Burgundy No 28861 42099 Black Yes 44199 23966 Blue No 63959 10910 White Yes 39800 9532 Red No 35666 40389 Red No 29064 15614 Red Yes 63494 15138 Blue Yes 21429 84989 White Yes 41990 14166 White/Black Yes

Can you please be more specific on what it is you are needing? If not, is there another tutor that can look at this as well? Please.

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Adyl98

3 hours ago

Provide information for the regression analysis report or provide the entire data set.

Adyl98

3 hours ago

provide the data of the 30 cars, their price and mileage. upload the file in google drive and paste the link here.

Here is the data for the 30 cars.

Sale Price Mileage Exterior Color Convertable or Not

26301 38439 Black No

30888 38654 Black No

28861 42099 Black No

21500 91844 Red Yes

22399 58458 Gray No

28957 37323 Black Yes

59943 17962 Red No

35250 20163 Black No

25345 60647 Black No

27385 22468 Gray Yes

61988 14188 Gray No

18283 117760 Black Yes

27700 14918 Blue No

28998 25440 Gray Yes

40840 12889 Black Yes

59943 17962 Red No

26617 32851 Red No

35825 33112 Gray/Black No

32897 44017 Yellow No

Here is the second half of that question and the findings.

Question:

Find & practically interpret the correlation coefficient (r) - 4 points Find & practically interpret the coefficient of determination (R2) - 4 points Find the equation of the least-squares line. Specifically, fill in the numbers for "a" and "b" in the least-squares equation: y^=a+bx. When fitting this model, have y be price and x be mileage. - 4 points Find & practically interpret the slope (b) - 4 points Find & interpret the y-intercept (a) if an interpretation is possible. If it's not possible, just report the y-intercept and explain why it can not be interpreted practically - 2 points Use your model to estimate the price of a car that has 100,000 miles (it's required to include this prediction in the report, whether or not it's good practice to make this prediction). Is this prediction reasonable? Why or why not? - 3 point Provide a brief conclusion or discussion of the results that includes one of the following: - 1 point potential problems or sources of bias are the results what you expected? are there other variables that may also be useful to predict the price of a car

Findings:

Explanation:

potential problems or sources of bias are the results what you expected? are there other variables that may also be useful to predict the price of a car

I used Excel to do the regression analysis.

With that, our correlation coefficient is -0.632. This means that there is a moderately negative relationship between mileage and sales price. Since the value is not near -1, it is neither a perfect nor a strong relationship. The coefficient of determination is 0.3996. This means that only 40% fit our regression model, and by others standard, this isn't enough. A higher coefficient of determination is general better for a data set. Using the regression analysis data, our least-squares equation is y=47,464.41-0.32916x. Our slope is -0.32916. This supports our negative relationship stated by our correlation coefficient. As price increases, our mileage decreases. Our y-intercept is 47.464.41. Y-intercept has 0 as an x-value. Therefore, this means that our y-intercept is the price of the car with 0 mileage. Substituting x as 100000, it leaves us with a price of $ 14,548.41. This means that, using the equation we established earlier, a car with 100,000 miles will have a sale price of $ 14,548.41.

Although we have an equation, the prediction isn't what we have in reality. First, we need to remember the fact that only 40% of our data fit the regression. This means that we need more data to have a more cohesive equation. Second, in reality, buying a car with 100,000 mileage is risky. Even if it is well-maintained, a car with that mileage will have a fair share of problems in the near future, and therefore isn't worth buying at all.

Potential problems with the data are the number of observations. Generally, the more observation we have, the better and generalizable our results can be. The results are somewhat what we can predict. In real life, the more mileage a car has, the lower is its value, and this is what we see with the regression. For other variables include its condition, the location on which we ought to buy, or the color of the car. Many of these are qualitative in nature.

Sale Price (y)	Mileage (x)
26301	38439			SUMMARY OUTPUT			LEAST-SQUARES EQUATION
30888	38654						y=47464.41-0.32916x
28861	42099			Regression Statistics
21500	91844			Multiple R	0.632111508		Correlation Coefficient
22399	58458			R Square	0.399564959		-0.632111508
28957	37323			Adjusted R Square	0.37812085
59943	17962			Standard Error	10589.07787
35250	20163			Observations	30
25345	60647
27385	22468			ANOVA
61988	14188				df	SS	MS	F	Significance F
18283	117760			Regression	1	2.09E+09	2089275349	18.63285	0.000179
27700	14918			Residual	28	3.14E+09	112128570.1
28998	25440			Total	29	5.23E+09
40840	12889
59943	17962				Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
26617	32851			Intercept	47464.4074	3283.352	14.45608246	0	40738.77	54190.05	40738.77	54190.05
35825	33112			X Variable 1	-0.32916483	0.076256	-4.316579044	0.000179	-0.48537	-0.17296	-0.48537	-0.17296
32897	44017
42387	19136
29500	26913
28861	42099
44199	23966
63959	10910
39800	9532
35666	40389
29064	15614
63494	15138
21429	84989
41990	14166

Sale Price (y)	Mileage (x)
26301	38439
30888	38654
28861	42099
21500	91844
22399	58458
28957	37323
59943	17962
35250	20163
25345	60647
27385	22468
61988	14188
18283	117760
27700	14918
28998	25440
40840	12889
59943	17962
26617	32851
35825	33112
32897	44017
42387	19136
29500	26913
28861	42099
44199	23966
63959	10910
39800	9532
35666	40389
29064	15614
63494	15138
21429	84989
41990	14166

LEAST-SQUARES EQUATION

y=47464.41-0.32916x

Correlation Coefficient

-0.632111508

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.632111508
R Square	0.399564959
Adjusted R Square	0.37812085
Standard Error	10589.07787
Observations	30

ANOVA
	df	SS	MS	F	Significance F
Regression	1	2.09E+09	2089275349	18.63285	0.000179
Residual	28	3.14E+09	112128570.1
Total	29	5.23E+09
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	47464.4074	3283.352	14.45608246	0	40738.77	54190.05	40738.77	54190.05
X Variable 1	-0.32916483	0.076256	-4.316579044	0.000179	-0.48537	-0.17296	-0.48537	-0.17296