Question

Price	Age (months)	Mileage (km)	Weight (kg)
13500	23	46986	1165
13750	23	72937	1165
13950	24	41711	1165
14950	26	48000	1165
13750	30	38500	1170
12950	32	61000	1170
16900	27	94612	1245
18600	30	75889	1245
21500	27	19700	1185
12950	23	71138	1105
20950	25	31461	1185
19950	22	43610	1185
19600	25	32189	1185
21500	31	23000	1185
22500	32	34131	1185
22000	28	18739	1185
22750	30	34000	1185
17950	24	21716	1105
16750	24	25563	1065
16950	30	64359	1105
15950	30	67660	1105
16950	29	43905	1170
15950	28	56349	1120
16950	28	32220	1120
16250	29	25813	1120
15950	25	28450	1120
17495	27	34545	1120
15750	29	41415	1120
16950	28	44142	1120
17950	30	11090	1120
12950	29	9750	1100
15750	22	35199	1100
15950	27	29510	1100
14950	26	32692	1100
15500	22	41000	1100
15750	26	43000	1100
15950	25	25000	1100
14950	23	10000	1100
15750	32	25329	1100
14750	27	27500	1100
13950	22	49059	1100
16750	27	44068	1100
13950	22	46961	1100
16950	27	110404	1255
16950	22	100250	1255
19000	23	84000	1270
17950	27	79375	1255
15800	22	75048	1110
17950	22	72215	1255
21950	31	64982	1195
17950	22	62636	1255
15750	30	57086	1110
20500	26	56000	1180
21950	27	49866	1195
15500	25	49163	1165
13250	32	45725	1075
15250	28	43210	1110
15250	26	43000	1110
18950	23	39704	1180
15999	30	38950	1130
14950	22	37400	1110
16500	27	37177	1130
18750	31	36544	1130
17950	30	33511	1130
17950	27	32809	1110
16950	26	32181	1075
18950	28	30993	1130
14950	22	30400	1110
22250	22	30000	1275
15950	25	29719	1110
15950	28	29206	1110
12995	32	29198	1060
18950	28	28817	1130
15750	23	28227	1110
19950	28	28000	1130
16950	23	28000	1115
18750	31	25266	1130
18450	27	23489	1115
16895	29	22575	1115
14900	30	22000	1110
18950	25	20019	1180
17250	29	20000	1115
15450	25	17003	1110
17950	31	16238	1180
16650	25	15414	1110
17450	28	8537	1130
14900	30	7000	1100
17950	20	66966	1245
15950	19	51884	1100
21950	19	50005	1265
16450	20	48110	1100
22250	20	37500	1260
19950	16	34472	1260
15950	20	33329	1100
18900	20	31850	1120
19950	17	30351	1260
15950	19	29435	1100
15950	19	25948	1100
18750	11	24500	1120

Data Set Preparation

1. Using the “Toyota Corolla” data set on D2L (Content à “JMP” à “JMP Data Sets” folder), we will be interested in analyzing the “Price” of a car as the dependent variable (Y). Please select one independent variable (X) you think may help explain Price, from the following three: “Age”, “Mileage”, or “Weight” of a car. In the space below, state your choice and explain why you chose it.

2. Randomly select a subset (sample) of 100 observations from the data file using the commands: Tables → Subset → Random - sample size: → (select 100 observations). After doing so, please use the newly created data window (your randomly selected subset), and move on to the Data Exploration section below.

Data Exploration

3. Explore the dependent variable (Price) and independent variable visually, by creating histograms for each one. Paste them below. Under each histogram, note the mean, say whether the data is skewed, and note if there are any outliers.

4. Write down the 95% confidence interval for the mean, for both the dependent and independent variable.

5. For the Price variable, test the hypothesis of µ being different than 11,500 at the 5% level of significance:

(a) State your null and alternative hypotheses.

(b) Find the relevant p-value and write it down.

(c) What do you conclude, based on your findings?

6. Using Analyze à Fit Y by X, create a scatterplot between Price (Y) and the independent variable (X) you chose. Paste it below, and comment on the following 4 concepts: direction, shape, strength of relationship, and whether there are any outliers.

7. Using Analyze à Fit Y by X, find what the correlation is between the two variables and write it down. Then, comment on the strength of the relationship.

Simple Linear Regression Modeling

8. Fit the regression model:

(a) Run the regression of X (independent variable) on Y (Price), and plot the regression line over the data. Paste your output below.

(b) Identify the results of the hypothesis test (p-value) on the regression slope coefficient. What do you conclude?

(c) Write down the regression equation, by hand.

(d) Make a prediction for Y (Price), by using one of the relevant bullets below:

If your X variable for the project is Age, use 15 for X

If your X variable for the project is Mileage, use 25,000 for X

If your X variable for the project is Weight, use 1,200 for X

Answer 1

3) Answer: ---- Date: ----11/5/2019