Discussion # II - Looking at part 2 Excel output, and the Regression Equation,interpret the slope of the Regression Equation. Use the Regression Equationto make a prediction and show the work for your predicted value based on your expression. For Example, if Year is used to predict Price, plug in a Year value into the regression equation and use it to predict the Price of a vehicle. Does this predicted Price value make sense with the data?

Part 2, table 2

Vehicle Type	Year	Make	Model	Price	MPG (city)	MPG (highway)	Seating Capacity
Midsize SUV	2022	Kia	Telluride	34,015	20	26	8
Midsize SUV	2022	Genesis	GV70	42,045	22	28	5
Midsize SUV	2022	Genesis	GV80	50,745	21	25	5
Midsize SUV	2022	Hyundai	Santa Fe	28,425	25	28	5
Midsize SUV	2022	Kia	Sorento	30,665	24	29	7
Midsize SUV	2022	Hyundai	Santa Fe Hybrid	34,975	36	31	5
Midsize SUV	2022	Kia	Sorento Hybrid	35,165	39	35	7
Midsize SUV	2022	Hyundai	Sante Fe Plug-In Hybrid	40,575	31	76	5
Midsize SUV	2022	BMW	X5	60,395	21	26	7
Midsize SUV	2022	Subaru	Outback	28,070	26	33	5
Car	2021	Lamborghini	Sian FKP 37	3,000,000	8	14	2

The correlation is 0.0712.

R^2 = 0.0712 * 0.0712 = 0.0051, which indicates a positive correlation, but a fairly weak correlation at less than 1%.

After running a regression analysis, the equation, p-value, and conclusion are as follows:

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.07117981
R Square	0.005066565
Adjusted R Square	-0.119300114
Standard Error	10935.44831
Observations	10
ANOVA
	df	SS	MS	F	Significance F
Regression	1	4871725.291	4871725	0.040739	0.845081
Residual	8	956672237.2	1.2E+08
Total	9	961543962.5
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	34881.74419	18293.43821	1.90679	0.09299	-7303	77066.49	-7303	77066.49
Seating Capacity	614.5348837	3044.680577	0.201839	0.845081	-6406.51	7635.581	-6406.51	7635.581

Price = 614.53 (seat capacity (x)) + 34,881.74

P-value = 0.8451