Question

In: Statistics and Probability

An expert who works for a car magazine obtained random data (rounded to the nearest thousand)...

An expert who works for a car magazine obtained random data (rounded to the nearest thousand) among two categories of used or new cars:

Domestic
Foreign

The expert would like to understand sales based on list price (rounded to the nearest thousand dollars), sale price (rounded to the nearest thousand dollars), and number of days it takes to sell each car. The complete data set is in the file named Cars.

Managerial Report

Prepare a report (see below) that summarizes your assessment of the nature of the car market. Be sure to include the following seven items in your report.

1 Descriptive statistics (mean, median, range, standard deviation, and coefficient of variation) to summarize each of the three variables for the all Domestic cars. Use z-scores to determine if there any outliers in the data set for any of the three variables. If there are any outliers in any category, please list them and state for which category they are an outlier. If a result is an outlier, state whether it is below or above the mean.

2 Descriptive statistics (mean, median, range, standard deviation, and coefficient of variation) to summarize each of the three variables for the all Foreign cars. Use z-scores to determine if there any outliers in the data set for any of the three variables. If there are any outliers in any category, please list them and state for which category they are an outlier. If a result is an outlier, state whether it is below or above the mean.

3 Compare your summary results from #1 and #2. Discuss any specific statistical results that would help the car expert understand the car market.

4 Develop a 98% confidence interval estimate of the population mean sales price and population mean number of days to sell for Domestic cars. What is the Margin of error? What are the lower (or left) and upper (or right) endpoints of the confidence interval? Interpret your results.

5 Develop a 98% confidence interval estimate of the population mean sales price and population mean number of days to sell for Foreign cars. What is the Margin of error? What are the lower (or left) and upper (or right) endpoints of the confidence interval? Interpret your results.

6 Assume the car expert requested estimates of the mean number of days to sell for the Domestic cars with a margin of error of seven days and the mean number of days of Foreign cars with a margin of error of eight days. Using 98% confidence, how large should the sample sizes be for each?

7 Suppose a Domestic car has a list price of $30,000 and a Foreign car has a list price of $30,000. What is your estimate of the final selling price (based on the percent difference for the sale and list price) and number of days required to sell each of these cars?

	Domestic Cars			Foreign Cars
Car	List Price in K	Sale Price in K	Days to Sell	List Price in K	Sale Price in K	Days to Sell
1	6.1	4.4	36	83.1	78.5	101
2	67.6	66.8	51	59.6	57.1	62
3	14	13.4	87	37	32.5	63
4	83.4	80.5	80	43.4	43.2	47
5	40	35.4	38	16.3	15	93
6	56	52	24	8.3	5.9	21
7	71.2	68.7	27	29.2	27.5	107
8	50.7	49.7	52	32.7	32.7	15
9	4.9	4.3	74	23.9	23.5	12
10	70.5	66.5	84	85.5	85.1	58
11	58	56	30	19	16.3	96
12	75	73.7	31	19	18.9	106
13	2.8	1.1	88	80.8	77.4	50
14	7.7	4	11	17.7	15.6	42
15	20	16.2	20	71.4	70.4	75
16	23	21.5	71	16.1	13.8	62
17	6.2	6	88	49.2	45.2	91
18	18	14	43	40	38	6
19	97	93.2	95	32	29.2	52
20	69.2	66.8	37	27.2	22.5	71
21	63.5	60.7	13	85.5	82.8	106
22	65	61.9	60	87.4	85.7	91
23	9.9	7	24	58.9	56.9	83
24	90.2	88.3	42	56.2	52.8	29
25	56	55	91	13.5	10	76
26	80	75.2	39	55.7	54.2	40
27	58.7	58.3	58	75.7	74.6	47
28	33.1	32.9	47	89.1	85.7	67
29	31.5	28.7	86	3.5	3.2	106
30	54.5	54.1	88	67.5	65.3	100
31	22.4	17.8	30	41.4	40.1	10
32	40.3	38.4	29	45.3	43.3	36
33	27.2	22.6	34	87.2	85.1	61
34	14.4	14.1	12	16.4	14.3	65
35	5.6	1.5	86	32.6	29.1	25
36	42.2	40.8	72	14.2	14.2	39
37	9.5	6.2	69	60.5	59.1	79
38	93.1	90.5	32	73.1	68.3	83
39	10.7	7.3	15	48.7	44.7	65
40	93.3	91.7	15	38.3	38.2	35
41	50	49.1	19	6.6	4.3	6
42	33.2	29.6	27	86.2	81.4	53
43	67	60	18	5	4.6	108
44	56	53.1	54	37	32.7	85
45	49.5	48.2	37	23.5	21.2	32
46	52.7	48.8	66	90.7	87.4	97
47	41.1	39.7	78	84.1	83.8	16
48	72.1	70.9	12	45.1	42.7	38
49	50	46.9	84	51.7	49.9	53
50	88	84.2	20	36	34.6	97
51	11.8	11.3	20	66.8	65	45
52	69.5	69	90	54.5	50.2	31
53	63	62.6	35	33	33	44
54	60	58.1	46	11.4	10.8	92
55	66.7	66.1	47	59.7	57.9	49
56	58	55.5	51	84.2	80.1	52
57	67.2	62.7	96	48.2	43.4	101
58	74.9	70.4	59	68.9	64.9	14
59	71.4	70.2	14	60.4	59.9	98
60	71.1	67.8	75	87.1	85.1	90
61	54	54	52	68.3	66.4	61
62	19.9	17.2	60	8.9	7.5	32
63	49.5	48.6	56	39.5	39.3	48
64	56.8	54.8	11	57.8	53.1	29
65	74	71.3	71	59	57.5	98
66	20.4	17	31	22.4	19.4	6
67	34.5	32.5	40	78.5	77.3	6
68	17.2	14.4	29	11.2	9.4	83
69	45	42.5	72	86.2	82	79
70	82	81	32	4	3.5	70
71	40	39.3	21	22.6	21.1	38
72	16	12.4	62	36	36	18
73	25	20.1	72	88	87.1	26
74	64.1	61.5	10	88.1	85.2	41
75	39.5	35.4	71	66.5	61.8	100
76	72	67.3	97	4.1	3.1	7
77	13	10.4	80	22	20.6	11
78	37.1	36.1	26	71.1	70.5	33
79	67.7	62.7	29	36.7	33.5	42
80	40	36.6	28	69.2	66.3	9
81	55.9	52.8	71	14.9	12.6	94
82	51	47.4	81	81	78.8	104
83	50	45.4	41	46.9	44.3	25
84	44.2	41	15	11.2	6.6	69
85	12	9.8	31	7	5.3	66
86	72	70	78	36	31.9	52
87	51.6	46.6	84	49.6	46.8	39
88	54.4	52.6	26	42.4	38.3	92
89	44.5	42.5	92	4.5	4	46
90	61.6	60.9	19	31.6	31	64
91	34.1	33.1	91	57.1	53.3	38
92	80	79	38	42	39	64
93	61.9	61.1	85	71.9	71	11
94	74.6	73.4	84	86.6	86.5	11
95	17	14.5	90	90	87.2	6
96	10.8	7.9	70	47.8	42.9	73
97	44.7	43.8	61	52.7	52.6	66
98	11.7	10.3	73	35.7	32	31
99	97.9	95.9	92	44.9	43.3	24
100	67.5	63	10	21.5	19.4	89
101	64	61.9	43	84.3	83.2	31
102	97.7	93.7	73	69.7	65.7	100
103	8.9	6.5	87	37.9	37.9	43
104	51.3	47.7	18	49.3	45.5	18
105	56	53.1	47	82.7	82.5	67
106	12.6	8.4	95	3.6	3.2	7
107	42.6	38.7	73	53.6	48.9	87
108	50	47.7	84	74.2	69.2	11
109	25.7	24.8	38	56.7	53.2	50
110	72.4	67.6	98	54.4	51	69
111	20.7	18.3	16	38.7	34	28
112	62.3	57.9	13	4.3	2.9	85
113	41.4	37.4	61	19.4	15.9	7
114	50	49	97	16.5	12.8	95
115	77.9	76.7	84	41.9	41	52
116	10	9.2	25	73	69.4	98
117	80	76.1	44	56	55.9	68
118	61.5	60	48	69.5	68.9	43
119	61.1	58.3	73	36.1	32.4	85
120	38.6	34.2	54	32.6	28	7
121	73.9	68.9	39	85.9	80.9	63
122	30	30	94	68.9	68.8	26
123	56	51.3	17	81.3	78.6	53
124	39	36.1	72	52	50.4	104
125	7.4	3.5	81	60.4	58.2	67
126	74.9	74.5	16	23.9	19.4	43
127	40	37.9	43	59.6	55.8	28
128	43	39	23	44.5	44.3	54
129	28.8	24.6	67	90.8	88.8	67
130	14.5	10.5	14	66.5	63.5	11
131	10.8	9.9	59	60.8	56	19
132	6.6	1.9	12	83.6	82.5	8
133	12.6	10.7	51	35.6	33.7	41
134	3.4	0.2	86	81.4	78.5	45
135	40.6	40.6	30	8.6	3.7	12
136	67	63.5	70	7.2	6.3	35
137	33.6	32	97	80.6	75.7	44
138	67	66	59	3.5	3.2	84
139	14	9.5	94	52	49.4	24
140	22.7	19.7	34	6.7	2.7	18
141	69.6	65.6	26	54.6	49.6	65
142	68.7	65.2	38	11.7	8.5	13
143	70	68	95	84.7	82.7	83
144	57	54	89	57.9	57.1	103
145	56.6	54.6	89	22.6	18.3	97
146	69.4	69.2	80	85.4	80.9	77
147	81.7	77.3	86	72.7	70.8	22
148	70	65.8	72	30.3	28.5	57
149	39.3	37.2	52	10.3	6.2	108
150	16.8	13.3	49	80.8	80.1	11
151	65.3	62.5	19	64.3	59.6	100
152	15.9	14	91	31.9	29	84
153	57.5	54.8	72	70.5	66.5	45
154	55.7	55	61	7.7	5.7	71
155	67.3	63.5	39	20.3	16.5	54
156	10	9.6	72	56	52.3	77
157	96	94.1	81	6	2.1	46
158	57.7	55.7	22	24.7	23.7	75
159	15.7	15.2	77	59.7	55.4	35
160	12.2	7.8	94	53.2	48.8	13
161	56.1	52.5	58	48.1	47.8	24
162	10.3	9.6	36	19.3	16.3	64
163	58	54.7	98	6.3	3.5	61
164	7.5	6.5	59	26.5	22	84
165	14.4	12.8	62	44.4	43.7	59
166	57.5	57.4	46	89.5	89.2	36
167	40.1	39.7	68	69.1	67.5	49
168	23.3	21	33	48.3	44.7	103
169	59	57.9	53	89	84.4	108
170	24.2	19.3	33	56.2	54.6	20
171	62	59.2	22	30	25.1	67
172	45	40.7	37	57.8	56.3	42
173	42	42	33	70.4	68.5	16
174	60	59.5	54	50.1	46.5	43
175	77.8	75	32	18.8	15.9	63
176	73	70	76	24	22.2	103
177	13.4	12	53	38.4	35.5	16
178	29	27.9	57	73.6	71.5	93
179	97	92.7	31	25	24.7	60
180	10	7.7	62	19	18.5	70
181	21.7	19.5	13	49.7	49.6	47
182	42.9	41.1	69	67.9	63.2	107
183	23	20	42	28.6	28.3	50
184	35	34.7	15	26.7	24.2	40
185	3.4	3.3	34	51.4	47	25
186	45	40.6	72	53.5	53.1	99
187	7.9	4.6	50	6.9	2.2	23
188	44	41.2	63	9.1	6.2	97
189	8	3.1	36	70	68.3	83
190	33	31.9	27	17	13	106
191	2.6	2.4	63	57.6	55.6	87
192	24	23	63	19.2	17.8	52
193	23.6	19.7	30	34.6	32.2	46
194	49.1	45.3	67	57.1	54.2	37
195	1.5	1.3	69	80.5	80.4	98
196	57.4	52.8	89	81.4	80.4	108
197	60.9	59.8	56	69.9	69	88
198	59.7	56.1	65	16.7	14.9	106
199	1.8	1.7	20	78.8	75.4	7
200	63.1	61.7	40	54.1	52.2	57

Expert Solution

Solution1:

Domestic Cars_List Price in K		Domestic Cars_Sale Price in K		Domestic Cars_Days to Sell

Mean	44.854	Mean	42.3965	Mean
Median	49.3	Median	45.35	Median
Standard Deviation	25.33192087	Standard Deviation	25.21323011	Standard Deviation
Range	96.4	Range	95.7	Range
Coefficientofvariation	56.47639201	Coefficientofvariation	59.47007444	Coefficientofvariation

z score is x-mean/sd

|Z|>3 are outliers

Subtract each bvalue form mean

divide by stddev You will get Z score

Take the moduus of Z score

Domestic Cars_List Price in K(X)	Mean	Stddev	X-mean	Z =x-mean/sd	\|Z\|=abs(Z)
6.1	44.854	25.33192	-38.754	-1.529848455	1.529848
67.6	44.854	25.33192	22.746	0.897918485	0.897918
14	44.854	25.33192	-30.854	-1.217988962	1.217989
83.4	44.854	25.33192	38.546	1.521637471	1.521637
40	44.854	25.33192	-4.854	-0.191615947	0.191616
56	44.854	25.33192	11.146	0.439998216	0.439998
71.2	44.854	25.33192	26.346	1.040031671	1.040032
50.7	44.854	25.33192	5.846	0.230776025	0.230776
4.9	44.854	25.33192	-39.954	-1.577219517	1.57722
70.5	44.854	25.33192	25.646	1.012398552	1.012399
58	44.854	25.33192	13.146	0.518949987	0.51895
75	44.854	25.33192	30.146	1.190040035	1.19004
2.8	44.854	25.33192	-42.054	-1.660118876	1.660119
7.7	44.854	25.33192	-37.154	-1.466687038	1.466687
20	44.854	25.33192	-24.854	-0.981133651	0.981134
23	44.854	25.33192	-21.854	-0.862705995	0.862706
6.2	44.854	25.33192	-38.654	-1.525900866	1.525901
18	44.854	25.33192	-26.854	-1.060085421	1.060085
97	44.854	25.33192	52.146	2.058509509	2.05851
69.2	44.854	25.33192	24.346	0.961079901	0.96108
63.5	44.854	25.33192	18.646	0.736067355	0.736067
65	44.854	25.33192	20.146	0.795281183	0.795281
9.9	44.854	25.33192	-34.954	-1.379840091	1.37984
90.2	44.854	25.33192	45.346	1.79007349	1.790073
56	44.854	25.33192	11.146	0.439998216	0.439998
80	44.854	25.33192	35.146	1.387419461	1.387419
58.7	44.854	25.33192	13.846	0.546583106	0.546583
33.1	44.854	25.33192	-11.754	-0.463999555	0.464
31.5	44.854	25.33192	-13.354	-0.527160971	0.527161
54.5	44.854	25.33192	9.646	0.380784389	0.380784
22.4	44.854	25.33192	-22.454	-0.886391526	0.886392
40.3	44.854	25.33192	-4.554	-0.179773181	0.179773
27.2	44.854	25.33192	-17.654	-0.696907277	0.696907
14.4	44.854	25.33192	-30.454	-1.202198608	1.202199
5.6	44.854	25.33192	-39.254	-1.549586397	1.549586
42.2	44.854	25.33192	-2.654	-0.104768999	0.104769
9.5	44.854	25.33192	-35.354	-1.395630445	1.39563
93.1	44.854	25.33192	48.246	1.904553557	1.904554
10.7	44.854	25.33192	-34.154	-1.348259383	1.348259
93.3	44.854	25.33192	48.446	1.912448734	1.912449
50	44.854	25.33192	5.146	0.203142905	0.203143
33.2	44.854	25.33192	-11.654	-0.460051966	0.460052
67	44.854	25.33192	22.146	0.874232953	0.874233
56	44.854	25.33192	11.146	0.439998216	0.439998
49.5	44.854	25.33192	4.646	0.183404963	0.183405
52.7	44.854	25.33192	7.846	0.309727795	0.309728
41.1	44.854	25.33192	-3.754	-0.148192473	0.148192
72.1	44.854	25.33192	27.246	1.075559968	1.07556
50	44.854	25.33192	5.146	0.203142905	0.203143
88	44.854	25.33192	43.146	1.703226542	1.703227
11.8	44.854	25.33192	-33.054	-1.304835909	1.304836
69.5	44.854	25.33192	24.646	0.972922666	0.972923
63	44.854	25.33192	18.146	0.716329413	0.716329
60	44.854	25.33192	15.146	0.597901757	0.597902
66.7	44.854	25.33192	21.846	0.862390188	0.86239
58	44.854	25.33192	13.146	0.518949987	0.51895
67.2	44.854	25.33192	22.346	0.88212813	0.882128
74.9	44.854	25.33192	30.046	1.186092446	1.186092
71.4	44.854	25.33192	26.546	1.047926848	1.047927
71.1	44.854	25.33192	26.246	1.036084083	1.036084
54	44.854	25.33192	9.146	0.361046446	0.361046
19.9	44.854	25.33192	-24.954	-0.985081239	0.985081
49.5	44.854	25.33192	4.646	0.183404963	0.183405
56.8	44.854	25.33192	11.946	0.471578924	0.471579
74	44.854	25.33192	29.146	1.15056415	1.150564
20.4	44.854	25.33192	-24.454	-0.965343296	0.965343
34.5	44.854	25.33192	-10.354	-0.408733315	0.408733
17.2	44.854	25.33192	-27.654	-1.091666129	1.091666
45	44.854	25.33192	0.146	0.005763479	0.005763
82	44.854	25.33192	37.146	1.466371231	1.466371
40	44.854	25.33192	-4.854	-0.191615947	0.191616
16	44.854	25.33192	-28.854	-1.139037191	1.139037
25	44.854	25.33192	-19.854	-0.783754225	0.783754
64.1	44.854	25.33192	19.246	0.759752886	0.759753
39.5	44.854	25.33192	-5.354	-0.211353889	0.211354
72	44.854	25.33192	27.146	1.071612379	1.071612
13	44.854	25.33192	-31.854	-1.257464847	1.257465
37.1	44.854	25.33192	-7.754	-0.306096014	0.306096
67.7	44.854	25.33192	22.846	0.901866073	0.901866
40	44.854	25.33192	-4.854	-0.191615947	0.191616
55.9	44.854	25.33192	11.046	0.436050628	0.436051
51	44.854	25.33192	6.146	0.24261879	0.242619
50	44.854	25.33192	5.146	0.203142905	0.203143
44.2	44.854	25.33192	-0.654	-0.025817229	0.025817
12	44.854	25.33192	-32.854	-1.296940732	1.296941
72	44.854	25.33192	27.146	1.071612379	1.071612
51.6	44.854	25.33192	6.746	0.266304321	0.266304
54.4	44.854	25.33192	9.546	0.3768368	0.376837
44.5	44.854	25.33192	-0.354	-0.013974463	0.013974
61.6	44.854	25.33192	16.746	0.661063173	0.661063
34.1	44.854	25.33192	-10.754	-0.424523669	0.424524
80	44.854	25.33192	35.146	1.387419461	1.387419
61.9	44.854	25.33192	17.046	0.672905939	0.672906
74.6	44.854	25.33192	29.746	1.174249681	1.17425
17	44.854	25.33192	-27.854	-1.099561306	1.099561
10.8	44.854	25.33192	-34.054	-1.344311794	1.344312
44.7	44.854	25.33192	-0.154	-0.006079286	0.006079
11.7	44.854	25.33192	-33.154	-1.308783498	1.308783
97.9	44.854	25.33192	53.046	2.094037806	2.094038
67.5	44.854	25.33192	22.646	0.893970896	0.893971
64	44.854	25.33192	19.146	0.755805298	0.755805
97.7	44.854	25.33192	52.846	2.086142629	2.086143
8.9	44.854	25.33192	-35.954	-1.419315976	1.419316
51.3	44.854	25.33192	6.446	0.254461556	0.254462
56	44.854	25.33192	11.146	0.439998216	0.439998
12.6	44.854	25.33192	-32.254	-1.273255201	1.273255
42.6	44.854	25.33192	-2.254	-0.088978645	0.088979
50	44.854	25.33192	5.146	0.203142905	0.203143
25.7	44.854	25.33192	-19.154	-0.756121105	0.756121
72.4	44.854	25.33192	27.546	1.087402733	1.087403
20.7	44.854	25.33192	-24.154	-0.953500531	0.953501
62.3	44.854	25.33192	17.446	0.688696293	0.688696
41.4	44.854	25.33192	-3.454	-0.136349707	0.13635
50	44.854	25.33192	5.146	0.203142905	0.203143
77.9	44.854	25.33192	33.046	1.304520102	1.30452
10	44.854	25.33192	-34.854	-1.375892502	1.375893
80	44.854	25.33192	35.146	1.387419461	1.387419
61.5	44.854	25.33192	16.646	0.657115585	0.657116
61.1	44.854	25.33192	16.246	0.641325231	0.641325
38.6	44.854	25.33192	-6.254	-0.246882186	0.246882
73.9	44.854	25.33192	29.046	1.146616561	1.146617
30	44.854	25.33192	-14.854	-0.586374799	0.586375
56	44.854	25.33192	11.146	0.439998216	0.439998
39	44.854	25.33192	-5.854	-0.231091832	0.231092
7.4	44.854	25.33192	-37.454	-1.478529804	1.47853
74.9	44.854	25.33192	30.046	1.186092446	1.186092
40	44.854	25.33192	-4.854	-0.191615947	0.191616
43	44.854	25.33192	-1.854	-0.073188291	0.073188
28.8	44.854	25.33192	-16.054	-0.633745861	0.633746
14.5	44.854	25.33192	-30.354	-1.198251019	1.198251
10.8	44.854	25.33192	-34.054	-1.344311794	1.344312
6.6	44.854	25.33192	-38.254	-1.510110512	1.510111
12.6	44.854	25.33192	-32.254	-1.273255201	1.273255
3.4	44.854	25.33192	-41.454	-1.636433345	1.636433
40.6	44.854	25.33192	-4.254	-0.167930416	0.16793
67	44.854	25.33192	22.146	0.874232953	0.874233
33.6	44.854	25.33192	-11.254	-0.444261612	0.444262
67	44.854	25.33192	22.146	0.874232953	0.874233
14	44.854	25.33192	-30.854	-1.217988962	1.217989
22.7	44.854	25.33192	-22.154	-0.874548761	0.874549
69.6	44.854	25.33192	24.746	0.976870255	0.97687
68.7	44.854	25.33192	23.846	0.941341958	0.941342
70	44.854	25.33192	25.146	0.992660609	0.992661
57	44.854	25.33192	12.146	0.479474102	0.479474
56.6	44.854	25.33192	11.746	0.463683747	0.463684
69.4	44.854	25.33192	24.546	0.968975078	0.968975
81.7	44.854	25.33192	36.846	1.454528466	1.454528
70	44.854	25.33192	25.146	0.992660609	0.992661
39.3	44.854	25.33192	-5.554	-0.219249066	0.219249
16.8	44.854	25.33192	-28.054	-1.107456483	1.107456
65.3	44.854	25.33192	20.446	0.807123949	0.807124
15.9	44.854	25.33192	-28.954	-1.14298478	1.142985
57.5	44.854	25.33192	12.646	0.499212044	0.499212
55.7	44.854	25.33192	10.846	0.428155451	0.428155
67.3	44.854	25.33192	22.446	0.886075719	0.886076
10	44.854	25.33192	-34.854	-1.375892502	1.375893
96	44.854	25.33192	51.146	2.019033624	2.019034
57.7	44.854	25.33192	12.846	0.507107221	0.507107
15.7	44.854	25.33192	-29.154	-1.150879957	1.15088
12.2	44.854	25.33192	-32.654	-1.289045555	1.289046
56.1	44.854	25.33192	11.246	0.443945805	0.443946
10.3	44.854	25.33192	-34.554	-1.364049737	1.36405
58	44.854	25.33192	13.146	0.518949987	0.51895
7.5	44.854	25.33192	-37.354	-1.474582215	1.474582
14.4	44.854	25.33192	-30.454	-1.202198608	1.202199
57.5	44.854	25.33192	12.646	0.499212044	0.499212
40.1	44.854	25.33192	-4.754	-0.187668358	0.187668
23.3	44.854	25.33192	-21.554	-0.850863229	0.850863
59	44.854	25.33192	14.146	0.558425872	0.558426
24.2	44.854	25.33192	-20.654	-0.815334933	0.815335
62	44.854	25.33192	17.146	0.676853527	0.676854
45	44.854	25.33192	0.146	0.005763479	0.005763
42	44.854	25.33192	-2.854	-0.112664176	0.112664
60	44.854	25.33192	15.146	0.597901757	0.597902
77.8	44.854	25.33192	32.946	1.300572513	1.300573
73	44.854	25.33192	28.146	1.111088265	1.111088
13.4	44.854	25.33192	-31.454	-1.241674493	1.241674
29	44.854	25.33192	-15.854	-0.625850684	0.625851
97	44.854	25.33192	52.146	2.058509509	2.05851
10	44.854	25.33192	-34.854	-1.375892502	1.375893
21.7	44.854	25.33192	-23.154	-0.914024646	0.914025
42.9	44.854	25.33192	-1.954	-0.07713588	0.077136
23	44.854	25.33192	-21.854	-0.862705995	0.862706
35	44.854	25.33192	-9.854	-0.388995373	0.388995
3.4	44.854	25.33192	-41.454	-1.636433345	1.636433
45	44.854	25.33192	0.146	0.005763479	0.005763
7.9	44.854	25.33192	-36.954	-1.458791861	1.458792
44	44.854	25.33192	-0.854	-0.033712406	0.033712
8	44.854	25.33192	-36.854	-1.454844273	1.454844
33	44.854	25.33192	-11.854	-0.467947143	0.467947
2.6	44.854	25.33192	-42.254	-1.668014053	1.668014
24	44.854	25.33192	-20.854	-0.82323011	0.82323
23.6	44.854	25.33192	-21.254	-0.839020464	0.83902
49.1	44.854	25.33192	4.246	0.167614609	0.167615
1.5	44.854	25.33192	-43.354	-1.711437527	1.711438
57.4	44.854	25.33192	12.546	0.495264456	0.495264
60.9	44.854	25.33192	16.046	0.633430054	0.63343
59.7	44.854	25.33192	14.846	0.586058992	0.586059
1.8	44.854	25.33192	-43.054	-1.699594761	1.699595
63.1	44.854	25.33192	18.246	0.720277001	0.720277

No outliers seen for

Domestic Cars_List Price in K(X).

No value is above 3

orchestra answered 1 year ago

16,184,885 rounded to the nearest ten thousand

The following data give the odometer mileage (rounded to the nearest thousand miles) for all 20...

The following data give the odometer mileage (rounded to the nearest thousand miles) for all 20 cars that are for sale at a dealership. 61 88 58 83 71 40 27 38 52 43 27 40 90 43 95 35 28 47 88 76 a. Calculate the values of the three quartiles and the interquartile range. Q1 = Q2 = Q3 = IQR = b. Find the approximate value of the 19th percentile. c. Calculate the percentile rank of 71....

The table below gives information on GPAs and starting salaries (rounded to the nearest thousand dollars)...

The table below gives information on GPAs and starting salaries (rounded to the nearest thousand dollars) of 7 recent college graduates. GPA | 2.90 3.81 3.20 2.42 3.94 2.05 2.25 Starting salary | 38 48 38 35 50 31 37 List the independent variable ___________ and the dependent variable_________. Build a table, with column totals containing x, y, x2, y2, and xy. Calculate average of y & average of x. Show formula & work for a, b, SSxx,...

The following information gives information on GPAs and starting salaries (rounded to the nearest thousand dollars)...

The following information gives information on GPAs and starting salaries (rounded to the nearest thousand dollars) of seven recent college graduates. GPA 2.90 3.81 3.20 2.42 3.94 2.25 2.05 Salary 23 28 23 21 32 22 19 a. Find the regression line of the data above. b. Use the equation of the regression line to predict the value of the salary for a GPA of 3.00. The following information gives information on GPAs and starting salaries (rounded to the nearest...

Given the following data, what is the weighted-average cost of ending inventory rounded to the nearest...

Given the following data, what is the weighted-average cost of ending inventory rounded to the nearest whole dollar? (Do not round in the process of your calculations, only round your final answer.) Sales revenue 100 units at $15 per unit Beginning inventory 40 units at $9 per unit Purchases 80 units at $10 per unit

Data are gathered on each car in the motor pool, regarding number of miles (in thousand...

Data are gathered on each car in the motor pool, regarding number of miles (in thousand miles) driven in a given year, and maintenance costs (in thousand dollars) for that year: Part of the linear regression analysis output are shown in below: Car Number 1 2 3 4 5 Miles Driven (x) 80 29 53 13 15 Repair Costs (y) 3.2 2.15 2.65 2.2 2.325 Construct a 95% confidence interval for the conditional mean of y given x0=50. (A) [2.476,...

Insurance contracts are usually obtained through an agent, who works as an independent contractor. True/False? To...

Insurance contracts are usually obtained through an agent, who works as an independent contractor. True/False? To deem an agreement a per se violation of antitrust law, a court must determine whether the agreement actually injures competition. True/False?

FOR PYTHON: Write a python program for a car salesperson who works a five day week....

FOR PYTHON: Write a python program for a car salesperson who works a five day week. The program should prompt for how many cars were sold on each day and then prompt for the selling price of each car (if any) on that day. After the data for all five days have been entered, the program should report the total number of cars sold and the total sales for the period. See example output. NOTE: duplicate the currency format shown...

A simple random sample of front-seat occupants involved in car crashes is obtained. Among 2771occupants not...

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The age for race car drivers were selected at random. The following ages were obtained: 32...

The age for race car drivers were selected at random. The following ages were obtained: 32 32 33 33 41 29 38 32 33 23 27 45 52 29 and 25. (Give three decimals for all number answers.) 1. What is the average for this sample? 2. What is the standard deviation for this sample? Use a .05 significance level to test the claim that the mean age of all race drivers is greater than 30 years. 3. What is the significance level? ...