ASSESSMENT 3 (Q1, Q5, Q6, Q7) Q1. For each of the data sets discussed in this section, construct a scatterplot

ASSESSMENT 3 (Q1, Q5, Q6, Q7)

Q1. For each of the data sets discussed in this section, construct a scatterplot of y versus x, and produce the regression output relating y to x.

Estimating Residential Real Estate Values:

VALUE	SIZE
23974	1442
24087	1426
16781	1632
29061	910
37982	972
29433	912
33624	1400
27032	1087
28653	1139
33075	1386
17474	756
33852	1044
29046	1032
20715	720
19461	734
21377	720
52881	1635
43889	1381
45134	1372
47655	1349
53088	1599
38923	1171
57870	1966
30489	1504
29207	1296
44919	1356
48090	1553
40521	1142
43403	1268
38112	1008
27710	1120
27621	960
22258	920
29064	1259
12001	783
37650	1874
27930	1242
16066	772
20411	908
23672	1155
24215	1004
22020	958
52863	1828
41822	1146
45104	1368
28154	1392
20943	1058
17851	1375
16616	648
38752	1313
44377	1780
43566	1148
38950	1363
44633	1262
12372	840
12148	840
19852	839
20012	852
20314	852
22814	974
24696	1135
23443	1170
35904	960
21799	1052
28212	1296
27553	1282
15826	916
18660	864
21536	1404
24147	1676
17867	1131
21583	1397
15482	888
24857	1448
17716	1022
224182	2251
182012	1126
201597	2617
49683	966
60647	1469
49024	1322
52092	1509
55645	1724
51919	1559
55174	2133
48760	1233
45906	1323
52013	1733
56612	1357
69197	1234
84416	1434
60962	1384
47359	995
56302	1372
88285	1774
91862	1903
242690	3581
296251	4343
107132	1861
77797	1542

Pricing Communications Nodes:

COST	NUMPORTS
52388	68
51761	52
50221	44
36095	32
27500	16
57088	56
54475	56
33969	28
31309	24
23444	24
24269	12
53479	52
33543	20
33056	24

Forecasting Housing Starts:

YEAR	STARTS	RATES
1963	1,603.2	5.89
1964	1,528.8	5.83
1965	1,472.8	5.81
1966	1,164.9	6.25
1967	1,291.6	6.46
1968	1,507.6	6.97
1969	1,466.8	7.81
1970	1,433.6	8.45
1971	2,052.2	7.74
1972	2,356.6	7.60
1973	2,045.3	7.96
1974	1,337.7	8.92
1975	1,160.4	9.00
1976	1,537.5	9.00
1977	1,987.1	9.02
1978	2,020.3	9.56
1979	1,745.1	10.78
1980	1,292.2	12.66
1981	1,084.2	14.70
1982	1,062.2	15.14
1983	1,703.0	12.57
1984	1,749.5	12.38
1985	1,741.8	11.55
1986	1,805.4	10.17
1987	1,620.5	9.31
1988	1,488.1	9.19
1989	1,376.1	10.13
1990	1,192.7	10.05
1991	1,013.9	9.32
1992	1,199.7	8.24
1993	1,287.6	7.20
1994	1,457.0	7.49
1995	1,354.1	7.87
1996	1,476.8	7.80
1997	1,474.0	7.71
1998	1,616.9	7.07
1999	1,640.9	7.04
2000	1,568.7	7.52
2001	1,602.7	7.00
2002	1,704.9	6.43

Q2. Make a scatterplot of consumption versus income

Q3. Create a regression model for tensile strength versus outside diam and molybdenum C.

Q4. Create a regression model profit versus employees, dividends and inventory

Q5. Major League Baseball Wins. What factor is most important in building a winning baseball team? Some might argue for a high batting average. Or it might be a team that hits for power as measured by the number of home runs. On the other hand, many believe that it is quality pitching as measured by the earned run average of the team’s pitchers.

WINS = number of games won

HR = number of home runs hit

BA = average batting average

ERA = earned run average

Using WINS as the dependent variable, use scatterplots and regression to investigate the relationship of the other three variables to WINS. Which of the three possible explanatory variables exhibits the strongest relationship to WINS? What might this suggest to managers of major league baseball teams?

Team	WINS	HR	BA	ERA
Anaheim Angels	99	152	0.282	3.69
Baltimore Orioles	67	165	0.246	4.46
Boston Red Sox	93	177	0.277	3.75
Chicago White Sox	81	217	0.268	4.53
Cleveland Indians	74	192	0.249	4.91
Detroit Tigers	55	124	0.248	4.93
Kansas City Royals	62	140	0.256	5.21
Minnesota Twins	94	167	0.272	4.12
New York Yankees	103	223	0.275	3.87
Oakland Athletics	103	205	0.261	3.68
Seattle Mariners	93	152	0.275	4.07
Tampa Bay Devil Rays	55	133	0.253	5.29
Texas Rangers	72	230	0.269	5.15
Toronto Blue Jays	78	187	0.261	4.8
Arizona Diamondbacks	98	165	0.267	3.92
Atlanta Braves	101	164	0.26	3.13
Chicago Cubs	67	200	0.246	4.29
Cincinnati Reds	78	169	0.253	4.27
Colorado Rockies	73	152	0.274	5.2
Florida Marlins	79	146	0.261	4.36
Houston Astros	84	167	0.262	4
Los Angeles Dodgers	92	155	0.264	3.69
Milwaukee Brewers	56	139	0.253	4.73
Montreal Expos	83	162	0.261	3.97
New York Mets	75	160	0.256	3.89
Philadelphia Phillies	80	165	0.259	4.17
Pittsburgh Pirates	72	142	0.244	4.23
St. Louis Cardinales	97	175	0.268	3.7
San Diego Padres	66	136	0.253	4.62
San Francisco Giants	95	198	0.267	3.54

Q6. Major League Baseball Salaries. The owners of MLB teams are concerned with rising salaries. The table below provides average salary (AVESAL) of the 30 MLB teams for the 2002 season. Also provided is the number of wins (WINS) for each team during the 2002 season. Is there evidence that teams with higher total payrolls tend to be more successful? Justify your answer.

Team	WINS	AVESAL
Anaheim Angels	99	2160054
Baltimore Orioles	67	1855318
Boston Red Sox	93	3633457
Chicago White Sox	81	1791286
Cleveland Indians	74	2106591
Detroit Tigers	55	1562847
Kansas City Royals	62	1832594
Minnesota Twins	94	1430068
New York Yankees	103	4902777
Oakland Athletics	103	1746264
Seattle Mariners	93	3337435
Tampa Bay Devil Rays	55	1131474
Texas Rangers	72	3123803
Toronto Blue Jays	78	1868356
Arizona Diamondbacks	98	3199608
Atlanta Braves	101	3166233
Chicago Cubs	67	2528398
Cincinnati Reds	78	1658363
Colorado Rockies	73	1848858
Florida Marlins	79	1506567
Houston Astros	84	2449680
Los Angeles Dodgers	92	3396961
Milwaukee Brewers	56	1338991
Montreal Expos	83	1497309
New York Mets	75	3192482
Philadelphia Phillies	80	2086812
Pittsburgh Pirates	72	1370088
St. Louis Cardinales	97	2998072
San Diego Padres	66	1292744
San Francisco Giants	95	3030571

Q7. Fanfare. Fanfare International, Inc., designs, distributes, and markets ceiling fans and lighting fixtures. The company’s product line includes 120 basic models of ceiling fans and 138 compatible fan light kits and table lamps. These products are marketed to over 1000 lighting showrooms and electrical wholesalers that supply the remodeling and new construction markets. The product line is distributed by a sales organization of 58 independent sales representatives.

In the summer of 1994, Fanfare decided it needed to develop forecasts of future sales to help determine future saleforce needs, capital expenditures, and so on. The table below contains monthly sales data and data on three additional variables for the period July 1990 through May 1994. The variables are defined as follows

SALES = total monthly sales in thousands of dollars

ADDEX = advertising expense in thousands of dollars

MTGRATE = mortgage rate for 30-year loans (%)

HSSTARTS = housing starts in thousands of units

The table contains the four variables as shown, plus columns for year and month. The sales data have been transformed to provide confidentiality.

As a consultant to Fanfare, your job is to find the best single variable to forecast future sales. Try each of the three variables in a simple regression and decide which is the best to create a forecasting model for Fanfare. Justify your choice. What problems do you see with using each of the three possible variables to help forecast sales?

YEAR	MONTH	SALES	AD EX	MTG RATE	HS STARTS
90	7	1538	14	10.04	111.2
90	8	1360	14	10.1	102.8
90	9	1202	68	10.18	93.1
90	10	1243	35	10.18	94.2
90	11	1076	35	10.01	81.4
90	12	691	32	9.67	57.4
91	1	1036	94	9.64	52.5
91	2	891	75	9.37	59.1
91	3	1120	76	9.5	73.8
91	4	1490	73	9.49	99.7
91	5	1672	71	9.47	97.7
91	6	1517	209	9.62	103.4
91	7	1866	95	9.58	103.5
91	8	1588	59	9.24	94.7
91	9	1430	77	9.01	86.6
91	10	1636	94	8.86	101.8
91	11	1225	168	8.71	75.6
91	12	1027	149	8.5	65.6
92	1	1323	100	8.43	71.6
92	2	1257	128	8.76	78.8
92	3	1915	88	8.94	111.6
92	4	1422	124	8.85	107.6
92	5	1697	103	8.67	115.2
92	6	2053	89	8.51	117.8
92	7	1965	156	8.13	106.2
92	8	1855	158	7.98	109.9
92	9	1911	168	7.92	106
92	10	1754	174	8.09	111.8
92	11	1559	321	8.31	84.5
92	12	1333	206	8.22	78.6
93	1	1675	224	8.02	70.5
93	2	1360	183	7.68	74.6
93	3	1667	281	7.5	95.5
93	4	1889	255	7.47	117.8
93	5	1906	318	7.47	120.9
93	6	2246	235	7.42	128.5
93	7	2232	169	7.21	115.3
93	8	2300	250	7.11	121.8
93	9	2097	231	6.92	118.5
93	10	2008	222	6.83	123.2
93	11	1931	184	7.16	102.3
93	12	1720	177	7.17	98.7
94	1	1936	363	7.06	76.2
94	2	1847	268	7.15	83.5
94	3	2399	229	7.68	134.3
94	4	2176	287	8.32	137.6