as atatched, each part seperately
>S&P 0 0
n $U.S. Millions
lphabetically:
01 – 0)
of from the S&P 500
0 from S&P 500
M ompany
,0 IVISION IZZA INC
6, 1
1 ILL AUTOMOTIVE INC
, 7 BORATORIES
, 1
1 , 1
, 5
2 7,4 0
2 9
1, 5
LAC INC
, 1
3 &E CORP
3 SOLUTIONS
,8 , 9
,9 4 UNIPER NETWOR S INC
, 0
ALLSTATE CORP 4 ,5 ,5 ION PHARMACEUTICALS INC
2
5 3
5 ,1 0
ALLSTATE CORP 33, 6 8
2
6 ,7 , 9
4
7 ,4 ,617
7 8 ,618
8 2
3
9 ,711
9 , 3
0
10 10 APPLIED MATERIALS INC 11 5
11 12 , 9
, 12 ,881
,948
Mean BB&T CORP 38,2 9
Median 4
,093.99
Standard Deviation .53
4
COGNIZANT TECH SOLUTIONS ,862
, ,730
, 9
,510
DOMINION ENERGY INC EMERSON ELECTRIC CO 38,417 ,085
EXPRESS SCRIPTS HOLDING CO 8
RP
, HEWLETT PACKARD ENTERPRISE PUBLIC STORAGE 2
JOHNSON CONTROLS INTL PLC 36,317 , KIMBERLY-CLARK CORP 43,513 U S BANCORP UNITED TECHNOLOGIES CORP 2
MCKESSON CORP 34,035 2
32,444 ,906
, , S&P GLOBAL INC 39,473 SOUTHERN CO CARDINAL HEALTH INC 24,405 TE CONNECTIVITY LTD ,938
VALERO ENERGY CORP 30,846 ,067
,867
CIGNA CORP 44,434 , CLOROX CO/DE 17,193 CME GROUP INC 41,665 ,522
COGNIZANT TECH SOLUTIONS 40,829 , 12,727 COSTCO WHOLESALE CORP 69,521 CUMMINS INC 28,203 DANAHER CORP 56,610 ,027
DOMINION ENERGY INC 48,545 DOVER CORP 13,082 EATON CORP PLC 35,001 EBAY INC 38,242 ELECTRONIC ARTS INC EQUINIX INC EXPRESS SCRIPTS HOLDING CO 36,178 ,179
FIDELITY NATIONAL INFO SVCS FIRSTENERGY CORP FORD MOTOR CO GENERAL MILLS INC GENERAL MOTORS CO 52,430 GOODYEAR TIRE & RUBBER CO 7,933 HALLIBURTON CO 36,990 8,346 HCA HEALTHCARE INC 29,477 HEWLETT PACKARD ENTERPRISE 28,762 ILLINOIS TOOL WORKS 48,608 INCYTE CORP 27,294 INTERCONTINENTAL EXCHANGE 39,497 INTERPUBLIC GROUP OF COS 8,499 INTUIT INC 35,156 INTUITIVE SURGICAL INC 34,857 JACOBS ENGINEERING GROUP INC 6,349 ,526
,147
JUNIPER NETWORKS INC KIMBERLY-CLARK CORP 43,513 KINDER MORGAN INC 45,625 LAM RESEARCH CORP 25,788 LILLY (ELI) & CO 91,008 16,386 LYONDELLBASELL INDUSTRIES NV 35,658 MARATHON PETROLEUM CORP 28,331 MARSH & MCLENNAN COS MCKESSON CORP 34,035 METTLER-TOLEDO INTL INC 14,710 MICRON TECHNOLOGY INC 31,328 MONSANTO CO 51,322 NORFOLK SOUTHERN CORP 32,444 NORTHROP GRUMMAN CORP 45,824 OCCIDENTAL PETROLEUM CORP 47,351 PAYPAL HOLDINGS INC 70,400 PG&E CORP 34,713 PHILIP MORRIS INTERNATIONAL PHILLIPS 66 PIONEER NATURAL RESOURCES CO 27,742 PPG INDUSTRIES INC 26,995 PPL CORP 26,186 PRAXAIR INC 37,229 PRICE (T. ROWE) GROUP 19,882 19,264 PROLOGIS INC PRUDENTIAL FINANCIAL INC 48,576 PUBLIC STORAGE 35,770 RAYTHEON CO 49,856 REGENERON PHARMACEUTICALS REPUBLIC SERVICES INC 21,627 S&P GLOBAL INC 39,473 SEALED AIR CORP 8,520 SEMPRA ENERGY SHERWIN-WILLIAMS CO 31,504 SIMON PROPERTY GROUP INC 49,483 SMUCKER (JM) CO 13,824 SOUTHERN CO 47,671 SOUTHWEST AIRLINES 33,565 STANLEY BLACK & DECKER INC 21,547 STATE STREET CORP 35,076 STERICYCLE INC TARGET CORP TJX COMPANIES INC 45,229 UNITED TECHNOLOGIES CORP 94,711 VALERO ENERGY CORP 30,846 VERISK ANALYTICS INC VERIZON COMMUNICATIONS INC VERTEX PHARMACEUTICALS INC WASTE MANAGEMENT INC 33,069 WELLTOWER INC 27,072 WILLIAMS COS INC WYNN RESORTS LTD ZIONS BANCORPORATION
2
5
0
Market Values
Standard & Poor’s
50
Market Values
I
Ordered
A
Market Value:
Second Quintile Alphabetically (
1
20
Sample
12
Sample of
6
Sample of 12 from Second Quintile
1
3
C
120
52
ACT
B
L
R
D
4
56
O’R
E
Y
1
7
9
8
ABBOTT
LA
85
34
ALLSTATE CORP
33
17
2 ABBOTT LABORATORIES 85,3
41
AETNA INC
51
18
VERIZON COMMUNICATIONS INC
19
24
AES CORP
7,
38
BB&T CORP
38,
23
3
ABBVIE INC
11
26
A
F
31
58
P
G
34,7
13
ALBEMARLE CORP
12,8
25
COGNIZANT TEC
H
40
29
4
ACCENTURE PLC
79
60
AIR PRODUCTS & CHEMICALS INC
30
49
J
K
10
68
33,1
71
DOMINION ENERGY INC
48
45
5 ACTIVISION BLIZZARD INC
46
61
ALE
X
30,
65
CARDINAL HEALTH INC
24,
405
AMERICAN INTERNATIONAL GROUP
60,
35
EXPRESS SCRIPTS HOLDING CO
36
78
6
ACUITY BRANDS INC
8,
53
171
SL GREEN REALTY CORP
10,
15
ANADARKO PETROLEUM CORP
25,
59
HEWLETT PACKARD ENTERPRISE
28
62
7
ADOBE SYSTEMS INC
72
27
AMERICAN ELECTRIC POWER CO
34,
69
APPLE INC
7
75
54
APPLIED MATERIALS INC
47
JOHNSON CONTROLS INTL PLC
36,
317
8
ADVANCE AUTO PARTS INC
8,
271
ANALOG DEVICES
28,9
98
U S BANCORP
88
AUTONATION INC
4,2
91
MCKESSON CORP
34,035
9
ADVANCED MICRO DEVICES
12,
86
ANTHEM INC
49,
82
UNITED TECHNOLOGIES CORP
94
BARD (C.R.) INC
23,2
99
PHILLIPS
66
43
22
10 AES CORP 7,3
80
AON PLC
36,
21
STANLEY BLACK & DECKER INC
21,547
BLOCK H & R INC
6,
319
PUBLIC STORAGE
35,7
70
11 AETNA INC 51,
185
47,617
SYMANTEC CORP
18,
93
C H ROBINSON WORLDWIDE INC
9,2
42
SOUTHWEST AIRLINES
33,565
12
AFFILIATED MANAGERS GRP INC
10,518
AVALONBAY COMMUNITIES INC
26,503
ALLERGAN PLC
84
74
CATERPILLAR INC
67
127
VALERO ENERGY CORP
30,846
13 AFLAC INC 31,5
81
BAXTER INTERNATIONAL INC
32
Mean
1
14
CERNER CORP
21,3
44
36,623
14
AGILENT TECHNOLOGIES INC
19,
213
39
Median
29,
55
CIGNA CORP
44,
434
35,
97
15 AIR PRODUCTS & CHEMICALS INC 30,949
BECTON DICKINSON & CO
45,7
89
Standard Deviation
215
CLOROX CO/DE
17,
193
5,
478
16
AKAMAI TECHNOLOGIES INC
8,
149
BOSTON SCIENTIFIC CORP
36,
453
COMERI
CA INC
12,727
17
ALASKA AIR GROUP INC
10,528
CAPITAL ONE FINANCIAL CORP
41,685
COSTCO WHOLESALE CORP
69,521
18 ALBEMARLE CORP 12,825
CARNIVAL CORP/PLC (USA)
35,7
83
DANAHER CORP
56,610
19
ALEXANDRIA R E EQUITIES INC
11,043
CBS CORP
26,
73
DOVER CORP
13,082
20 ALEXION PHARMACEUTICALS INC 30,652 CIGNA CORP 44,434
E TRADE FINANCIAL CORP
11,2
77
21
ALIGN TECHNOLOGY INC
13,434
CME GROUP INC
41,665
EMERSON ELECTRIC CO
38,
417
22
ALLEGION PLC
7,717
40,829
ESSEX PROPERTY TRUST
17,
167
23 ALLERGAN PLC 84,749
CONSTELLATION BRANDS -CL A
37
EXXON MOBIL CORP
339
129
24
ALLIANCE DATA SYSTEMS CORP
13,
442
CORNING INC
26,319
FIRSTENERGY CORP
14,
178
25
ALLIANT ENERGY CORP
9,
234
CROWN CASTLE INTL CORP
40,862
FORTIVE CORP
22,
471
26 ALLSTATE CORP 33,171
CSX CORP
45,0
63
GENERAL ELECTRIC CO
221
27
ALPHABET INC
605,
366
CUMMINS INC
28,
203
GOODYEAR TIRE & RUBBER CO
7,933
28
ALTRIA GROUP INC
124
64
DEERE & CO
41,032
HCA HEALTHCARE INC
29,
477
29
AMAZON.COM INC
474
DELTA AIR LINES INC
35,738
HOME DEPOT INC
178,855
30
AMEREN CORP
13,612
48,545
IDEXX LABS INC
14,669
31
AMERICAN AIRLINES GROUP INC
24,565
EATON CORP PLC
35,001
INTERPUBLIC GROUP OF COS
8,
499
32 AMERICAN ELECTRIC POWER CO 34,694
EBAY INC
38,
242
JACOBS ENGINEERING GROUP INC
6,
349
33
AMERICAN EXPRESS CO
75,
342
ECOLAB INC
38,1
90
KIMBERLY-CLARK CORP
43,513
34 AMERICAN INTERNATIONAL GROUP 60,
353
ELECTRONIC ARTS INC
36,
141
L3 TECHNOLOGIES INC
13,686
35
AMERICAN TOWER CORP
58,510
LILLY (ELI) & CO
91,008
36
AMERICAN WATER WORKS CO INC
14,
451
EQUINIX INC
35,
117
MACERICH CO
8,
137
37
AMERIPRISE FINANCIAL INC
21,775
EXELON CORP
35,507
MASTERCARD INC
134
38
AMERISOURCEBERGEN CORP
20,
486
36,178
METTLER-TOLEDO INTL INC
14,710
39
AMETEK INC
14,
170
FIDELITY NATIONAL INFO SVCS
30,
156
MOLSON COORS BREWING CO
17,559
40
AMGEN INC
127,
336
FISERV INC
27,
291
MYLAN NV
20,899
41
AMPHENOL CORP
23,
416
FORD MOTOR CO
43,
76
NORFOLK
SOUTHERN CO
32,
444
42 ANADARKO PETROLEUM CORP 25,5
92
GENERAL MILLS INC
32,
121
OMNICOM GROUP
18,170
43 ANALOG DEVICES 28,998
GENERAL MOTORS CO
52,
430
PAYPAL HOLDINGS INC
70,
400
44
ANSYS INC
11,076
HALLIBURTON CO
36,990
PHILIP MORRIS INTERNATIONAL
181
273
45 ANTHEM INC 49,823 HCA HEALTHCARE INC 29,477
PRICE (T. ROWE) GROUP
19,882
46 AON PLC
36,
210
28,762
35,
770
47
APACHE CORP
18,824
HP INC
32,
152
RANGE RESOURCES CORP
5,
227
48
APARTMENT INVST & MGMT CO
7,120
HUMANA INC
33,366
REPUBLIC SERVICES INC
21,627
49 APPLE INC 775,
454
ILLINOIS TOOL WORKS
48,608
S&P GLOBAL INC
39,
473
50 APPLIED MATERIALS INC 47,617
INCYTE CORP
27,
294
SEALED AIR CORP
8,
520
51
ARCHER-DANIELS-MIDLAND CO
23,973
INTERCONTINENTAL EXCHANGE
39,
497
SMUCKER (JM) CO
13,824
52
ARCONIC INC
10,867
INTUIT INC
35,156
STERICYCLE INC
6,
57
53
ARTHUR J GALLAGHER & CO
10,592
INTUITIVE SURGICAL INC
34,857
TE CONNECTIVITY LTD
28,
409
54
ASSURANT INC
5,768
TORCHMARK CORP
9,
238
55
AT&T INC
239
460
88,618
56
AUTODESK INC
24,417
KINDER MORGAN INC
45,625
94,711
57
AUTOMATIC DATA PROCESSING
53,
202
LAM RESEARCH CORP
25,788
VERISK ANALYTICS INC
14,
428
58 AUTONATION INC 4,291
LYONDELLBASELL INDUSTRIES NV
35,658
WESTERN UNION CO
9,
307
59
AUTOZONE INC
15,
132
MARATHON PETROLEUM CORP
28,
331
WYNN RESORTS LTD
13,
240
60 AVALONBAY COMMUNITIES INC 26,503
MARRIOTT INTL INC
39,477
ZIONS BANCORPORATION
9,
161
61
AVERY DENNISON CORP
8,
230
MARSH & MCLENNAN COS
39,
96
62
BAKER HUGHES A GE CO
15,
790
63
BALL CORP
14,713
MICRON TECHNOLOGY INC
31,
328
64
BANK OF AMERICA CORP
238,
260
MONSANTO CO
51,
322
65
BANK OF NEW YORK MELLON CORP
54,788
MONSTER BEVERAGE CORP
29,
95
66 BARD (C.R.) INC 23,
299
NORFOLK SOUTHERN CORP
67 BAXTER INTERNATIONAL INC
32,881
NORTHROP GRUMMAN CORP
45,824
68 BB&T CORP
38,239
OCCIDENTAL PETROLEUM CORP
47,
351
69 BECTON DICKINSON & CO 45,789 PG&E CORP
34,713
70
BERKSHIRE HATHAWAY
431
PHILLIPS 66
43,
223
71
BEST BUY CO INC
17,791
PIONEER NATURAL RESOURCES CO
27,742
72
BIOGEN INC
61,
229
PPG INDUSTRIES INC
26,995
73
BLACKROCK INC
69,
186
PPL CORP
26,186
74 BLOCK H & R INC 6,319
PRAXAIR INC
37,229
75
BOEING CO
143
314
PROGRESSIVE CORP-OHIO
27,
383
76
BORGWARNER INC
9,865
PROLOGIS INC
32,
343
77
BOSTON PROPERTIES INC
18,603
PRUDENTIAL FINANCIAL INC
48,576
78 BOSTON SCIENTIFIC CORP 36,453 PUBLIC STORAGE 35,770
79
BRISTOL-MYERS SQUIBB CO
93,
312
RAYTHEON CO
49,856
80
BROADCOM LTD
100
290
REGENERON PHARMACEUTICALS
51,
315
81
BROWN FORMAN CORP
19,
344
82 C H ROBINSON WORLDWIDE INC
9,242
SEMPRA ENERGY
2
8,
346
83 CA INC
13,075
SHERWIN-WILLIAMS CO
31,504
84
CABOT OIL & GAS CORP
11,502
SIMON PROPERTY GROUP INC
49,
483
85
CAMPBELL SOUP CO
16,011
47,671
86 CAPITAL ONE FINANCIAL CORP 41,685 SOUTHWEST AIRLINES 33,565
87
STATE STREET CORP
35,076
88
CARMAX INC
12,
140
SUNTRUST BANKS INC
27,593
89 CARNIVAL CORP/PLC (USA)
35,783
SYSCO CORP
28,
162
90 CATERPILLAR INC
67,127
TARGET CORP
31,
265
91
CBOE HOLDINGS INC
10,591
28,409
92
CBRE GROUP INC
12,836
TJX COMPANIES INC
45,229
93 CBS CORP
26,734
TRAVELERS COS INC
35,346
94
CELGENE CORP
105
95
CENTENE CORP
13,698
VERTEX PHARMACEUTICALS INC
38,
277
96
CENTERPOINT ENERGY INC
12,149
WASTE MANAGEMENT INC
33,069
97
CENTURYLINK INC
12,771
WELLTOWER INC
27,072
98 CERNER CORP
21,344
WILLIAMS COS INC
26,
259
99
CF INDUSTRIES HOLDINGS INC
6,844
YUM BRANDS INC
26,
284
100
CHARTER COMMUNICATIONS INC
101
ZOETIS INC
30,685
101
CHESAPEAKE ENERGY CORP
4,504
102
CHEVRON CORP
206
103
CHIPOTLE MEXICAN GRILL INC
9,802
104
CHUBB LTD
68,
386
105
CHURCH & DWIGHT INC
13,
298
106
107
CIMAREX ENERGY CO
9,
419
108
CINCINNATI FINANCIAL CORP
12,543
109
CINTAS CORP
14,213
110
CISCO SYSTEMS INC
157
252
111
CITIGROUP INC
186,526
112
CITIZENS FINANCIAL GROUP INC
17,770
113
CITRIX SYSTEMS INC
11,938
114
115
116
CMS ENERGY CORP
13,040
117
COACH INC
13,252
118
COCA-COLA CO
195
119
120
COLGATE-PALMOLIVE CO
63,597
121
COMCAST CORP
190
274
122
COMERICA INC
123
CONAGRA BRANDS INC
14,
264
124
CONCHO RESOURCES INC
19,
333
125
CONOCOPHILLIPS
55,213
126
CONSOLIDATED EDISON INC
25,
304
127 CONSTELLATION BRANDS -CL A
37,862
128
COOPER COMPANIES INC
11,915
129 CORNING INC 26,319
130
131
COTY INC
15,312
132 CROWN CASTLE INTL CORP 40,862
133
CSRA INC
5,339
134 CSX CORP
45,063
135
136
CVS HEALTH CORP
81,
432
137
D R HORTON INC
13,
358
138
139
DARDEN RESTAURANTS INC
10,520
140
DAVITA INC
12,606
141 DEERE & CO 41,032
142
DELPHI AUTOMOTIVE PLC
24,223
143 DELTA AIR LINES INC 35,738
144
DENTSPLY SIRONA INC
14,
219
145
DEVON ENERGY CORP
17,511
146
DIGITAL REALTY TRUST INC
18,706
147
DISCOVER FINANCIAL SVCS
22,853
148
DISCOVERY COMMUNICATIONS INC
9,152
149
DISH NETWORK CORP
14,565
150
DISNEY (WALT) CO
172
151
DOLLAR GENERAL CORP
20,611
152
DOLLAR TREE INC
17,061
153
154
155
DOW CHEMICAL
78,594
156
DR PEPPER SNAPPLE GROUP INC
16,566
157
DTE ENERGY CO
19,206
158
DU PONT (E I) DE NEMOURS
71,342
159
DUKE ENERGY CORP
59,574
160
DUKE REALTY CORP
10,
166
161
DXC TECHNOLOGY COMPANY
22,
243
162 E TRADE FINANCIAL CORP
11,277
163
EASTMAN CHEMICAL CO
12,050
164
165
166 ECOLAB INC
38,190
167
EDISON INTERNATIONAL
25,635
168
EDWARDS LIFESCIENCES CORP
24,
321
169
36,141
170 EMERSON ELECTRIC CO 38,417
171
ENTERGY CORP
13,769
172
ENVISION HEALTHCARE CORP
6,632
173
EOG RESOURCES INC
54,921
174
EQT CORP
11,041
175
EQUIFAX INC
17,507
176
35,117
177
EQUITY RESIDENTIAL
24,998
178 ESSEX PROPERTY TRUST
17,167
179
EVEREST RE GROUP LTD
10,775
180
EVERSOURCE ENERGY
19,264
181 EXELON CORP 35,507
182
EXPEDIA INC
21,724
183
EXPEDITORS INTL WASH INC
10,608
184
185
EXTRA SPACE STORAGE INC
10,014
186 EXXON MOBIL CORP
339,129
187
F5 NETWORKS INC
7,673
188
FACEBOOK INC
401
189
FASTENAL CO
12,
372
190
FEDERAL REALTY INVESTMENT TR
9,581
191
FEDEX CORP
55,806
192
30,156
193
FIFTH THIRD BANCORP
20,032
194
14,178
195 FISERV INC 27,291
196
FLIR SYSTEMS INC
5,120
197
FLOWSERVE CORP
5,372
198
FLUOR CORP
6,071
199
FMC CORP
10,
235
200
FOOT LOCKER INC
6,196
201
43,768
202 FORTIVE CORP 22,471
203
FORTUNE BRANDS HOME & SECUR
10,103
204
FRANKLIN RESOURCES INC
24,974
205
FREEPORT-MCMORAN INC
21,152
206
GAP INC
9,431
207
GARMIN LTD
9,
440
208
GARTNER INC
11,604
209
GENERAL DYNAMICS CORP
58,793
210 GENERAL ELECTRIC CO 221,730
211
32,121
212
213
GENUINE PARTS CO
12,
470
214
GGP INC
19,956
215
GILEAD SCIENCES INC
99,
374
216
GLOBAL PAYMENTS INC
14,
390
217
GOLDMAN SACHS GROUP INC
88,697
218
219
GRAINGER (W W) INC
9,619
220
221
HANESBRANDS INC
222
HARLEY-DAVIDSON INC
8,519
223
HARRIS CORP
13,955
224
HARTFORD FINANCIAL SERVICES
20,037
225
HASBRO INC
13,235
226
227
HCP INC
14,830
228
HELMERICH & PAYNE
5,
496
229
HERSHEY CO
15,989
230
HESS CORP
14,160
231
232
HILTON WORLDWIDE HOLDINGS
20,273
233
HOLOGIC INC
12,
379
234 HOME DEPOT INC 178,855
235
HONEYWELL INTERNATIONAL INC
103,529
236
HORMEL FOODS CORP
18,061
237
HOST HOTELS & RESORTS INC
13,809
238 HP INC
32,152
239 HUMANA INC 33,366
240
HUNT (JB) TRANSPRT SVCS INC
9,927
241
HUNTINGTON BANCSHARES
14,
443
242 IDEXX LABS INC 14,669
243
IHS MARKIT LTD
18,623
244
245
ILLUMINA INC
25,
382
246
247
INGERSOLL-RAND PLC
22,
292
248
INTEL CORP
166,674
249
250
251
INTL BUSINESS MACHINES CORP
134,824
252
INTL FLAVORS & FRAGRANCES
10,517
253
INTL PAPER CO
22,701
254
255
256
INVESCO LTD
14,148
257
IRON MOUNTAIN INC
9,632
258
259
JOHNSON & JOHNSON
357
260 JOHNSON CONTROLS INTL PLC 36,317
261
JPMORGAN CHASE & CO
326
262
10,
680
263
KANSAS CITY SOUTHERN
10,878
264
KELLOGG CO
23,819
265
KEYCORP
19,713
266
267
KIMCO REALTY CORP
8,589
268
269
KLA-TENCOR CORP
14,521
270
KOHL’S CORP
7,049
271
KRAFT HEINZ CO
106,
494
272
KROGER CO
22,003
273
L BRANDS INC
13,307
274 L3 TECHNOLOGIES INC 13,686
275
LABORATORY CP OF AMER HLDGS
16,256
276
277
LAUDER (ESTEE) COS INC -CL A
22,161
278
LEGGETT & PLATT INC
6,374
279
LENNAR CORP
12,047
280
LEUCADIA NATIONAL CORP
9,366
281
LEVEL 3 COMMUNICATIONS INC
21,206
282
283
LINCOLN NATIONAL CORP
16,386
284
LKQ CORP
10,666
285
LOCKHEED MARTIN CORP
84,131
286
LOEWS CORP
287
LOWE’S COMPANIES INC
65,343
288
289
M & T BANK CORP
24,887
290 MACERICH CO
8,137
291
MACY’S INC
7,232
292
MARATHON OIL CORP
10,
395
293
294 MARRIOTT INTL INC 39,477
295
39,962
296
MARTIN MARIETTA MATERIALS
14,182
297
MASCO CORP
12,148
298 MASTERCARD INC
134,085
299
MATTEL INC
6,858
300
MCCORMICK & CO INC
11,873
301
MCDONALD’S CORP
126,
450
302
303
MEDTRONIC PLC
114,118
304
MERCK & CO
174,722
305
METLIFE INC
59,176
306
307
MGM RESORTS INTERNATIONAL
18,935
308
MICHAEL KORS HOLDINGS LTD
5,678
309
MICROCHIP TECHNOLOGY INC
18,627
310
311
MICROSOFT CORP
560,372
312
MID-AMERICA APT CMNTYS INC
11,762
313
MOHAWK INDUSTRIES INC
18,505
314 MOLSON COORS BREWING CO 17,559
315
MONDELEZ INTERNATIONAL INC
66,791
316
317 MONSTER BEVERAGE CORP
29,952
318
MOODY’S CORP
25,141
319
MORGAN STANLEY
86,296
320
MOSAIC CO
8,474
321
MOTOROLA SOLUTIONS INC
14,823
322 MYLAN NV 20,899
323
NASDAQ INC
12,284
324
NATIONAL OILWELL VARCO INC
12,431
325
NAVIENT CORP
4,112
326
NETAPP INC
11,799
327
NETFLIX INC
78,432
328
NEWELL BRANDS INC
25,
469
329
NEWFIELD EXPLORATION CO
5,723
330
NEWMONT MINING CORP
19,822
331
NEWS CORP
8,
404
332
NEXTERA ENERGY INC
68,
550
333
NIELSEN HOLDINGS PLC
15,336
334
NIKE INC -CL B
77,
482
335
NISOURCE INC
8,
429
336
NOBLE ENERGY INC
12,
472
337
NORDSTROM INC
8,065
338
339
NORTHERN TRUST CORP
19,995
340
341
NRG ENERGY INC
7,782
342
NUCOR CORP
18,400
343
NVIDIA CORP
96,693
344 O’REILLY AUTOMOTIVE INC
17,978
345
346 OMNICOM GROUP 18,170
347
ONEOK INC
21,
493
348
ORACLE CORP
206,545
349
PACCAR INC
24,046
350
PACKAGING CORP OF AMERICA
10,314
351
PARKER-HANNIFIN CORP
22,106
352
PATTERSON COMPANIES INC
4,019
353
PAYCHEX INC
20,791
354
355
PENTAIR PLC
11,
446
356
PEOPLE’S UNITED FINL INC
6,005
357
PEPSICO INC
166,229
358
PERKINELMER INC
7,244
359
PERRIGO CO PLC
10,743
360
PFIZER INC
197,894
361
362
181,273
363
43,223
364
PINNACLE WEST CAPITAL CORP
9,676
365
366
PNC FINANCIAL SVCS GROUP INC
61,824
367
368
369
370
371
PRICELINE GROUP INC
99,685
372
PRINCIPAL FINANCIAL GRP INC
373
PROCTER & GAMBLE CO
232,283
374 PROGRESSIVE CORP-OHIO 27,383
375
32,343
376
377
PUBLIC SERVICE ENTRP GRP INC
22,
750
378
379
PULTEGROUP INC
7,368
380
PVH CORP
9,286
381
QORVO INC
8,720
382
QUALCOMM INC
78,512
383
QUANTA SERVICES INC
4,996
384
QUEST DIAGNOSTICS INC
14,774
385
RALPH LAUREN CORP
4,189
386 RANGE RESOURCES CORP
5,227
387
RAYMOND JAMES FINANCIAL CORP
11,981
388
389
REALTY INCOME CORP
15,639
390
RED HAT INC
17,545
391
REGENCY CENTERS CORP
11,263
392
51,315
393
REGIONS FINANCIAL CORP
17,505
394
395
RESMED INC
10,937
396
ROBERT HALF INTL INC
5,755
397
ROCKWELL AUTOMATION
21,262
398
ROCKWELL COLLINS INC
17,309
399
ROPER TECHNOLOGIES INC
23,718
400
ROSS STORES INC
21,530
401
ROYAL CARIBBEAN CRUISES LTD
24,319
402
403
SALESFORCE.COM INC
64,589
404
SCANA CORP
9,200
405
SCHEIN (HENRY) INC
14,
455
406
SCHLUMBERGER LTD
95,318
407
SCHWAB (CHARLES) CORP
57,363
408
SCRIPPS NETWORKS INTERACTIVE
8,383
409
SEAGATE TECHNOLOGY PLC
9,624
410
411
28,346
412
413
SIGNET JEWELERS LTD
4,184
414
415
SKYWORKS SOLUTIONS INC
19,269
416 SL GREEN REALTY CORP
10,158
417
SMITH (A O) CORP
7,866
418
419
SNAP-ON INC
8,879
420
421
422
423
STAPLES INC
6,666
424
STARBUCKS CORP
78,168
425
426
6,572
427
STRYKER CORP
55,025
428 SUNTRUST BANKS INC 27,593
429 SYMANTEC CORP 18,935
430
SYNCHRONY FINANCIAL
24,115
431
SYNOPSYS INC
11,509
432 SYSCO CORP
28,162
433
31,265
434 TE CONNECTIVITY LTD 28,409
435
TECHNIPFMC PLC
13,317
436
TEXAS INSTRUMENTS INC
81,081
437
TEXTRON INC
13,005
438
THERMO FISHER SCIENTIFIC INC
68,671
439
TIFFANY & CO
11,905
440
TIME WARNER INC
79,431
441
442 TORCHMARK CORP
9,238
443
TOTAL SYSTEM SERVICES INC
11,679
444
TRACTOR SUPPLY CO
7,215
445
TRANSDIGM GROUP INC
14,678
446 TRAVELERS COS INC 35,346
447
TRIPADVISOR INC
5,011
448
TWENTY-FIRST CENTURY FOX INC
53,533
449
TYSON FOODS INC -CL A
18,260
450 U S BANCORP 88,618
451
UDR INC
10,
459
452
ULTA BEAUTY INC
15,583
453
UNDER ARMOUR INC
7,703
454
UNION PACIFIC CORP
82,408
455
UNITED CONTINENTAL HLDGS INC
20,590
456
UNITED PARCEL SERVICE INC
75,
880
457
UNITED RENTALS INC
10,057
458
459
UNITEDHEALTH GROUP INC
184,840
460
UNIVERSAL HEALTH SVCS INC
9,912
461
UNUM GROUP
11,313
462
463
VARIAN MEDICAL SYSTEMS INC
8,927
464
VENTAS INC
23,987
465
VERISIGN INC
10,109
466
14,428
467
197,424
468
38,277
469
VF CORP
24,904
470
VIACOM INC
14,304
471
VISA INC
219,217
472
VORNADO REALTY TRUST
15,024
473
VULCAN MATERIALS CO
16,271
474
WAL-MART STORES INC
241,130
475
WALGREENS BOOTS ALLIANCE INC
86,325
476
477
WATERS CORP
13,882
478
WEC ENERGY GROUP INC
19,872
479
WELLS FARGO & CO
267,920
480
481
WESTERN DIGITAL CORP
24,791
482 WESTERN UNION CO
9,307
483
WESTROCK CO
14,418
484
WEYERHAEUSER CO
24,862
485
WHIRLPOOL CORP
12,982
486
WHOLE FOODS MARKET INC
13,372
487
26,259
488
WILLIS TOWERS WATSON PLC
20,127
489
WYNDHAM WORLDWIDE CORP
10,891
490
13,240
491
XCEL ENERGY INC
24,022
492
XEROX CORP
7,795
493
XILINX INC
15,727
494
XL GROUP LTD
11,477
495
XYLEM INC
10,195
496 YUM BRANDS INC
26,284
497
ZIMMER BIOMET HOLDINGS INC
24,463
498
9,161
499 ZOETIS INC 30,685
Big Stacks
Big Stacks
Required Sample Size
BowlingData
Mr. Consistency
Mr. Unpredictable
1 219 177
2 223 269
3 217 188
4 231 256
5 202 200
6 227 277
7 204 171
8 248 256
9 202 229
10 222 169
11 231 226
12 228 183
13 221 222
14 207 255
15 216 177
16 225 222
17 227 171
18 209 279
19 227 200
20 219 175
21 224 165
22 209 228
23 227 265
24 221 171
25 212 199
26 217 255
27 224 228
28 228 219
29 228 265
30 217 187
31 203 277
32 216 235
33 228 188
34 191 255
35 221 209
36 218 220
37 235 255
38 192 216
39 217 179
40 219 209
41 219 288
42 225 177
43 232 239
44 212 205
45 211 217
46 232 255
47 231 213
48 231 245
49 198 199
50 217 198
51 221 175
52 216 233
53 221 279
54 234 216
55 218 210
56 231 244
57 233 222
58 209 237
59 238 223
60 219 198
Coat
| Sales by Sales Person | ||||||||||||||
| Name | Region | |||||||||||||
| Ali | Chicago | 1560 | ||||||||||||
| Bailey | 1280 | |||||||||||||
| Camden | 690 | |||||||||||||
| Denton | 1840 | |||||||||||||
| Erickson | ||||||||||||||
| Foreman | 800 | |||||||||||||
| Gehrig | 1330 | |||||||||||||
| Hacker | 620 | |||||||||||||
| Ippolito | 950 | |||||||||||||
| Jackson | 1370 | |||||||||||||
| Kemp | 860 | |||||||||||||
| Louis | 660 | |||||||||||||
| McGwire | ||||||||||||||
| Norton | 1750 | |||||||||||||
| Owens | 1900 | |||||||||||||
| Pujols | ||||||||||||||
| Quisenberry | ||||||||||||||
| Robinson | 2000 | |||||||||||||
| Sosa | ||||||||||||||
| Thomas | 1400 | |||||||||||||
| Utley | 1550 | |||||||||||||
| Van Slyke | ||||||||||||||
| Wainwright | 1880 | |||||||||||||
| Xavier | ||||||||||||||
| Young | 1490 | |||||||||||||
| Zimmerman |
and ACT +)
| The Impact of ACT on GPA | ||||
| The Impact of | Absences | |||
| Average | ||||
| Allen | 3.21 | |||
| Berglund | 3.00 | |||
| Crowder | 3.05 | |||
| Dennison | 3.87 | |||
| Edmundson | 2.49 | |||
| Fabbri | 2.91 | |||
| Granderson | 3.81 | |||
| Hunter | 1.89 | |||
| Isner | 2.78 | |||
| Jones | 2.89 | |||
| Klein | 2.54 | |||
| Langston | 3.5 | |||
| Monday | 3.68 | |||
| Nolan | 3.01 | |||
| O’Reilly | 3.1 | |||
| Prendergast | 3.55 | |||
| Quick | 1.76 | |||
| Racine | 3.32 | |||
| Steen | 2.77 | |||
| Unger | 1.78 | |||
| Victor | 3.78 | |||
| Wilson | 2.61 | |||
| 3.66 | ||||
| 3.28 | ||||
| Zander | 3.19 | |||
| 26.48 | 3.02 | 5.32 | ||
| 4.91 | 0.60 | 6.81 | ||
| Mode |
s
| Louistown Masher | ||||||
| Number of | Total | Cost per | ||||
| Bats Produced | ||||||
| January | 21,000 | 236,000 | 11.24 | |||
| February | 30,000 | 330,000 | 11.00 | |||
| March | 18,000 | 203,000 | 11.28 | |||
| April | 17,500 | 202, | 600 | 11.58 | ||
| May | 17,000 | 201,000 | 11.82 | |||
| June | 16,000 | 193,000 | 12.06 | |||
| July | 15,000 | 1 | 88,000 | 12.53 | ||
| August | 14,000 | 182,000 | 13.00 | |||
| September | 10,000 | 1 | 31,000 | 13.10 | ||
| October | 200,500 | 11.79 | ||||
| November | 13,000 | 170,000 | 13.08 | |||
| December | 177,000 | 12.64 | ||||
| 202,500 | 2,414,100 | 11.92 | ||||
| Average per month | 16,875 | 201,175 | ||||
| Median per month | 16,500 | 196,750 |
s
| Self Study | In class | |||||||||
| Student | Control | Treatment | ||||||||
| Total Points (Out of 100) | |||||
| Exam One | Exam Two | Change | |||
| Top | Bottom | Top | |||
| Quartile | |||||
| -5 | |||||
| -2 | |||||
| -7 | |||||
| -9 | |||||
| -4 | |||||
| -1 | |||||
| -12 | |||||
| -10 | |||||
| -8 |
| Pharmaceutical Experiment | |||
| Pills Taken (out of 28) | |||
| With | |||
| Subject | Message | ||
| ANOVA Illustration | Cardinal | Northtown | ShowTime |
| Ross | 650 | 650.00 | |
| Tony | 690.00 | ||
| Pete | 640 | 586.67 | |
| 556.67 | 643.33 | 726.67 | 642.22 |
Months
| Months of Service | |||||
| Anderson | 50,000 | ||||
| 87,000 | |||||
| Connor | 91,000 | ||||
| Dalton | 55,000 | ||||
| Eagan | 79,000 | ||||
| Falstaff | 94,000 | ||||
| Graham | 62,000 | ||||
| Hemingway | 105,000 | ||||
| Italiano | 110,000 | ||||
| Justice | 67,000 | ||||
| Kent | 81,000 | ||||
| Lerner | |||||
| Montgomery | 53,000 | ||||
| Nelson | |||||
| O’Hara | 72,000 | ||||
| 134,000 | |||||
| Quentin | 101,000 | ||||
| Roosevelt | 75,000 | ||||
| Sanders | 48,000 | ||||
| Thompson | |||||
| Unnerstall | 123,000 | ||||
| Vancil | 100,000 | ||||
| Washington | |||||
| 112,000 | |||||
| Yetton | 107,000 | ||||
| Zoltek | 66,000 |
| Master’s Degree |
Salaries
| Aronson | 3.06 | 49,000 | |
| Boston | 2.99 | 41,000 | |
| Cantoni | 2.75 | 38,500 | |
| 3.39 | 54,000 | ||
| Evans | 3.59 | 57,000 | |
| Fenton | 2.46 | ||
| Gardner | 3.88 | 62,500 | |
| Houston | 2.07 | 35,000 | |
| Islip | 3.83 | 51,500 | |
| 58,000 | |||
| Kirkwood | 38,000 | ||
| 2.44 | 37,000 | ||
| Montana | 2.59 | 40,000 | |
| Nobles | 45,000 | ||
| 2.3 | 32,000 | ||
| Pratt | 3.95 | 63,000 | |
| Quinton | 3.25 | ||
| Russell | |||
| Sanger |
| Hours | Hours | ||||
| Score on Exam | 0 – 10 | 11 – 20 | 21 – 30 | 31 – 40 | Over 40 |
| 80 – 100 | |||||
| 60 – 79 | |||||
| 40 – 59 | |||||
| 20 – 39 | |||||
| 0 – 19 | |||||
| 32.56 | 75.59 | ||||
| 77.5 | |||||
| Sorted on Score | |||||
| Gateway City Chair Company | |||||||
| (Allocation Problem) | |||||||
| Parameters | |||||||
| Time Requirements | |||||||
| Hours Required per Unit | Time Available | ||||||
| Club Chairs (C) | Recliners (R) | Gliders (G) | (Hours) | ||||
| Fabrication | |||||||
| Assembly | 2400 | ||||||
| Shipping | 1600 | ||||||
| Demand/Output Potential | |||||||
| Profit per Unit Sold | |||||||
| Decision Variables | |||||||
| Product Mix: | |||||||
| Objective Function | |||||||
| Total Profit: | |||||||
| Constraints | |||||||
| i. Constraints on Time | |||||||
| Hours Used | |||||||
| Hours Available | |||||||
| <= | |||||||
| ii. Constraints on Demand/Outputs | |||||||
| Total Sales/Ouptut | Cap on Sales/Output | ||||||
| Club Chairs (C): | |||||||
| Recliners (R): | |||||||
| Gliders (G): |
| X&Y Industries | |||
| (Cost Minimization Problem — X&Y Under Contract) | |||
| Processing Time (per unit) | |||
| Minimum | Production Requirement | ||
| Minimum Production of Y: | |||
| Minimum Total Production: | |||
| Minimum Production of X: | |||
| Cost per Unit Produced | |||
| Total Cost: | |||
| i. Constraints on Production | |||
| Product Y | >= | ||
| Output of X Produced | |||
| Non-negative Production of X | |||
| ii. Constraint on Time |
Optimization
Linear Programming
Profit Maximization
Gateway Chair Company
Club Chairs (C)
Recliners (R)
Gliders (G)
Total Profit
→ $17 profit per unit
→ $27 profit per unit
→ $24 profit per unit
Profit Maximization
3 departments
Only variable input is time
Time is limited in each department
Upper bound on demand for each product
Profit Maximization
Hours Required per Unit Time Available
Department Chairs (C) Recliners (R) Gliders (G) (Hours)
Fabrication 4 6 5 2000
Assembly 3 7 7 2400
Shipping 2 5 2 1600
C Production R Production G Production
Demand Potential
(Production Limit) 300 280 250
Profit Maximization
Which combination (,,) profit?
optimization problem.
maximizes
Constrained
Profit Maximization
Constrained optimization: find the best set of decisions, according to some performance measure, subject to satisfying the constraints of the problem.
Structure:
Decision variables
Objective function
Constraints
Solution:
Optimal decisions
Sensitivity analysis (shadow prices)
Setup
Most of the work is in setting up the problem.
Decision variables: Controllable inputs in an optimization model.
This Example: and and
Setup
Objective function: A function of the decision variables which is to be maximized or minimized.
This Example: total profit from selling , and
Setup
Constraints: Restrictions which limit the values of the decision variables.
This Example: we have time constraints and demand (or production) constraints
Setup
Constraints: Restrictions which limit the values of the decision variables.
Time constraints:
Hours Available
Fabrication:
Assembly:
Shipping:
Setup
Constraints: Restrictions which limit the values of the decision variables.
Demand potential constraints:
Upper Bound
Club Chairs:
Recliners:
Gliders:
Solution
Use Excel Solver to get solution (see beginning of Course Packet).
In this example the objective and all constraints are linear functions of the decisions variables (,,).
→ Linear Programming Problem
Solution
Solve for (). Get the maximized profit.
But we also want to perform a sensitivity analysis: study of how changes in the parameters of the model affect the optimal solution.
→ Shadow Price (for a constraint): the change in the optimal objective function value for a one unit increase in the right-hand side of a constraint.
Shadow Price
Say we have solved for (). Profit is maximized.
Will total maximized profit ↑ ?
Upper Bound
Club Chairs:
Recliners:
Gliders:
RHS
+ 1
By how much?
Shadow Price
A non-binding constraint has a shadow price = 0.
Make a binding constraint less restrictive → makes objective no worse. Usually makes it better.
Make a binding constraint more restrictive → makes objective no better. Usually makes it worse.
Optimization Contd.
Minimization Problem
Cost Minimization
X&Y Industries
Contract
Output
Output
Produce at least 226
Produce at least 325 of either or
1150 hours available
Total Cost
→ 2 hours per unit, cost per unit
→ 4 hours per unit, cost per unit
Cost Minimization
Minimize cost of fulfilling contract and satisfying time constraint. Find ().
Non-negativity constraints: .
Can’t minimize cost by .
Structure: decision variables, objective function, constraints.
Another constrained optimization problem.
Setup
Decision Variables:
and
Setup
Objective function:
Total cost from producing and
Setup
Constraints on production levels, time and non-negativity constraints on .
Production constraints:
Production
Minimum : 0
Minimum total: Y
Setup
Constraints on production levels, time and non-negativity constraints on .
Time constraint:
Time
Time Constraint:
Setup
Constraints on production levels, time and non-negativity constraints on .
Non-negativity constraints:
Production
Minimum : 0
Minimum : Y
superfluous
Solution
Again, we use Excel Solver to get solution.
Select min this time in the solver.
Enter the correct inequalities in the constraints list.
Recall
Solve for (). Get the minimized cost.
But we also want to perform a sensitivity analysis.
→ Shadow Prices for the constraints
Shadow Prices
A non-binding constraint has a shadow price = 0.
Make a binding constraint less restrictive → makes objective no worse. Usually makes it better.
Make a binding constraint more restrictive → makes objective no better. Usually makes it worse.
Shadow Prices
Say we have solved for (). Cost is minimized.
Will total minimized cost change?
Min :
Min output:
Time:
RHS
+ 1
By how much?
Non-neg :
+ 1
The first part:
The following are the cases from this module.
You have completed each of these replications by now. Please upload your work in the EXCEL file.
There is one additional step, though, that you may need to complete. You may or may not have thought about the professional polish for the benefit of others using your data. Professional polish for effective analysis involves organizing and labeling the data so that a user of your information will quickly understand where the key data are and what messages the data are conveying.
VISUALIZATION AND MEASURES OF CENTRAL TENDENCY
1. Histograms
2. Use the cell function “=average” and “=median” to generate the mean and median score for Mr. Predictable and Mr. Unpredictable.
2. Generate a descriptive statistics table from the Data Analysis (DATA / DATA ANALYSIS). Ensure that the values from the descriptive statistics table agree with those from the cell function calculations developed in step 1.
MEASURING THE VARIABILITY IN THE DATA
1. Use the bowling data. Calculate the variance and standard deviation using the EXCEL cell functions (VAR Function and STDEV function)
2. Ensure that the variance and standard deviation calculated in item 1 agree with the variance and standard deviation provided by the from the Descriptive Statistics tool in the EXCEL DATA TOOLPAK.
DIVIDING DATA FOR ANALYSIS
1. Develop quartiles and deciles for the S&P 500 Market Values
NORMALIZING DATA FOR DECISION MAKING
See the data in the “Coat Sales” tab in the EXCEL student data file. North Trace sells winter coats. You are the Sales Manager with responsibility for two markets: The Upper Midwest headquartered in Chicago and the Southwest headquartered in Los Angeles. You are evaluating your most talented sales persons. Their sales are shown in the EXCEL spreadsheet.
Required:
1. Make the empirical case for the top five sales persons in your employ.
2. One sales person is going to be moved out of sales and into the warehouse shipping operations. You do not want this to be one of the talented sales persons. Each of your sales persons is equally able to perform the shipping operations function. Who should be transferred?
A CLOSER LOOK AT THE NORMAL DISTRIBUTION (BELL CURVE)
See the bell curve that represents the bowling scores of Mr. Consistency and Mr. Unpredictable
1. Identify the mean
2. Discuss one standard deviation away from the mean
3. Discuss two standard deviations away from the mean
4. What does it mean to be in the right tail of the distribution?
5. What does it mean to be in the left tail of the distribution?
6. What is the cumulative probability?
RELATIONSHIP BETWEEN TWO VARIABLES
See the “Two Variables (GPA and ACT +)” tab in the data file
1. Create a scatter diagram for GPAs and ACT Scores
2. Create a scatter diagram for GPAs and Absences
3. Does there appear to be a relationship between the two variables? If so, which variable is at least somewhat dependent upon the value of the other variable?
SEE THE TWO VARIABLES (GPA AND ACT +) TAB:
1. Calculate the Correlation Coefficient for two variable sample
• ACT Score and GPA
• Absences and GPA
The second part
Hypothesis Testing ASSIGNMENT (small sample size)
The CPA Review course providers conducted an experiment. They selected a sample of 24 UMSL Master of Accounting students with very close ACT scores, very close GMAT scores, and very close GPAs. Twelve randomly selected students used the self-study CPA Review course materials (the control group) and twelve randomly selected students used the same materials but also used the in-class instructor experience two nights per week (the treatment group).
At what confidence level can the CPA Review provider claim the in-class experience has incremental value?
(See scores in EXCEL file tab “CPA Exam Scores”)
Hypothesis Testing (Larger Sample Size)
The University has struggled for a number of years with its introductory statistics course. After another challenging outcome on exam 1, the Dean provided a teaching assistant that would work exclusively with the lowest performing quartile of students on exam 1.
The data for exams one and two are shown in the EXCEL file (Tab Student Test Scores). Does the data suggest that the teaching assistant helped the lesser performing students close the performance gap?
Example:
A hospital struggles to get its patients to complete the entire regimen of antibiotics when they are prescribed. Instead, many patients take the medicine until they feel better and then save the antibiotics in case they get another infection. To manage this issue, the hospital has decided to put a message on each bottle imploring patients to finish the entire bottle. The experts are confident that at worst, the message will go unheeded. It certainly will not cause them to take less medicine! They hope it causes them to take more.
If they gather data, they could use a one-tailed test in this instance to see if any observed increase in usage is significant.
One-Tailed Hypothesis Test Assignment:
See the data in Tab “Pharmacy”
1. Calculate the mean, median, and modal number of pills taken by participants in the control group and in the sample group.
2. With what level of confidence can the Pharmacy say that the message of encouragement to complete the full dosage caused a change in patient’s behavior (i.e., led to a increase in pills taken that is large enough to be statistically significant)?
The 3rd part
Hypothesis Testing ASSIGNMENT (small sample size)
The CPA Review course providers conducted an experiment. They selected a sample of 24 UMSL Master of Accounting students with very close ACT scores, very close GMAT scores, and very close GPAs. Twelve randomly selected students used the self-study CPA Review course materials (the control group) and twelve randomly selected students used the same materials but also used the in-class instructor experience two nights per week (the treatment group).
At what confidence level can the CPA Review provider claim the in-class experience has incremental value?
(See scores in EXCEL file tab “CPA Exam Scores”)
Hypothesis Testing (Larger Sample Size)
The University has struggled for a number of years with its introductory statistics course. After another challenging outcome on exam 1, the Dean provided a teaching assistant that would work exclusively with the lowest performing quartile of students on exam 1.
The data for exams one and two are shown in the EXCEL file (Tab Student Test Scores). Does the data suggest that the teaching assistant helped the lesser performing students close the performance gap?
Example:
A hospital struggles to get its patients to complete the entire regimen of antibiotics when they are prescribed. Instead, many patients take the medicine until they feel better and then save the antibiotics in case they get another infection. To manage this issue, the hospital has decided to put a message on each bottle imploring patients to finish the entire bottle. The experts are confident that at worst, the message will go unheeded. It certainly will not cause them to take less medicine! They hope it causes them to take more.
If they gather data, they could use a one-tailed test in this instance to see if any observed increase in usage is significant.
One-Tailed Hypothesis Test Assignment:
See the data in Tab “Pharmacy”
1. Calculate the mean, median, and modal number of pills taken by participants in the control group and in the sample group.
2. With what level of confidence can the Pharmacy say that the message of encouragement to complete the full dosage caused a change in patient’s behavior (i.e., led to a increase in pills taken that is large enough to be statistically significant)?
The 4th part:
SIMPLE REGRESSION ASSIGNMENT
See EXCEL data tab “Bat Production Costs”
Use regression analysis to estimate the fixed and variable components of production costs at Louistown Masher
REGRESSION ASSIGNMENT
A local high tech company employs 26 information technology professionals. Their salaries are shown in the EXCEL spreadsheet.
Required:
1. Produce a scatter plot of salaries and tenure (months of service) and comment on the linear assumption (is it at least quasi-linear to become comfortable that the linearity assumptions holds).
2. Estimate the regression equation that represents salaries as a function of tenure with the company (months of service)
3. Analyze the fit of the regression model
· Comment on the F-value for the significance of the regression model
· What is the probability that there is no linear relation between the DV and the IV whatsoever?
· Comment on the R2
· What percentage of the variance in the salaries is explained by tenure?
· Comment on the plot of the residuals against the independent variable
4. Interpret the estimated regression coefficients
· How are salaries determined according to the regression model? What is the fixed component of salary and what is the amount resulting from raises while at the company?
5. Replicate the following values per the regression output using EXCEL formulas
· Average salary for all employees
· Total Residual (observed salary – mean salary) for each employee
· Predicted salary per the regression equation for each employee (Y-HAT)
· The Model Residual (the difference between the actual salary for the employee and the predicted salary per the regression equation) for each employee
· Total unexplained variance (residual variance)
· Total explained variance
MULTIPLE REGRESSION ASSIGNMENT:
Per our analysis of the residuals in the regression of salaries at the High Tech company against tenure (months of service) it was clear that there was some other variable of significance. One of our hypotheses was that it might be that some employees had additional education (i.e., a Master’s Degree) which earned them incremental salary.
See the data in the EXCEL file.
Required:
1. Use Multiple Regression to estimate the relationship between salaries and both tenure (months with the company) and education (indicator variable for a Master’s Degree).
2. Produce a histogram of the model residuals and analyze
3. Analyze the residual plots for each for each of the IVs
4. Analyze whether the model is improved with the two variables (is adjusted R-squared greater than regression R-squared with just the single variables)
5. Analyze the coefficients
6. Prepare a Pearson Correlation Matrix for the independent variables
The 5th part
Replicate the operations you watched in the video to compute BOTH the solution AND the shadow prices for the various constraints.
It is also important that you set up the problem in an organized way.
LP Maximization Problem
The scenario is described in the video, but all of the parameters you need for this problem can be found in the data pack, in the LP_Max_Gateway tab. This concerns Gateway Chair Company’s profit maximization problem. If you don’t like my spreadsheet layout you can substitute your own, as long as its clean and easily readable.
Go to the EXCEL file tab “LP_Max_Gateway”.
LP Minimization Problem
The scenario is described in the video, but all of the parameters you need for this problem can be found in the data pack, in the LP_Min_XY tab. This concerns X&Y Industries cost minimization. Take note of the variety of inequality constraints you encounter in this one. If you don’t like my spreadsheet layout you can substitute your own, as long as its clean and easily readable.
Go to the EXCEL file tab “LP_Min_XY”.
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