Questions
5. Altria is reexamining the costs of capital it uses to decide on investments in its...

5. Altria is reexamining the costs of capital it uses to decide on investments in its two
primary businessesó food and tobacco. The two divisions have about the same market
value.
Altria has an equity beta of 0.95 and a debt/equity ratio of 25%. The companyís debt is
priced to yield an expected return of 8%. The average equity beta of publicly traded Örms
in the tobacco business is 1.2, the average debt beta is 0.3, and the average debt/equity
ratio of such Örms is 20%. The average equity beta of publicly traded Örms in the food
business is 0.9, the average debt beta is close to zero, and the debt/equity ratio of such
Örms is 80%. The current interest rate is 7.1%.
Estimate the cost of capital for each of the two businesses

In: Finance

Explain how the below ratio could be affected if there was an increase in Publicly traded...

Explain how the below ratio could be affected if there was an increase in Publicly traded hospital costs or supply chain disruptions occurred for materials including equipment, medications and other supplies? And explain how these ratio can affect the Publicly traded hospital financial statement

Increase in costs

a. Debt service coverage ratio (Net Income/Debt Service)

b. Profit Margin Ratio (Net Income/Net Sales)

c. Return on Assets (Net Income/ total assets)

Supply chain disruptions

a. Current Ratio (Current Assets/Current Liabilities)

b. Inventory turnover (Net Sales/ Average Inventory)

c. Debt to total assets (debt/total assets)

d. Return on assets (Net Income/ total assets)

In: Economics

Refer to the Baseball 2016 data, which reports information on the 2016 Major League Baseball season....

Refer to the Baseball 2016 data, which reports information on the 2016 Major League Baseball season. Let attendance be the dependent variable and total team salary be the independent variable. Determine the regression equation and answer the following questions.

Draw a scatter diagram. From the diagram, does there seem to be a direct relationship between the two variables?

What is the expected attendance for a team with a salary of $100.0 million?

If the owners pay an additional $30 million, how many more people could they expect to attend?

At the .05 significance level, can we conclude that the slope of the regression line is positive? Conduct the appropriate test of hypothesis.

What percentage of the variation in attendance is accounted for by salary?

Determine the correlation between attendance and team batting average and between attendance and team ERA. Which is stronger? Conduct an appropriate test of hypothesis for each set of variables.

Show all work in Excel

Team League Year Opened Team Salary Attendance Wins ERA BA HR Year Average salary
Arizona National 1998 65.80 2080145 79 4.04 0.264 154 2000 1988034
Atlanta National 1996 89.60 2001392 67 4.41 0.251 100 2001 2264403
Baltimore American 1992 118.90 2281202 81 4.05 0.250 217 2002 2383235
Boston American 1912 168.70 2880694 78 4.31 0.265 161 2003 2555476
Chicago Cubs National 1914 117.20 2959812 97 3.36 0.244 171 2004 2486609
Chicago Sox American 1991 110.70 1755810 76 3.98 0.250 136 2005 2632655
Cincinnati National 2003 117.70 2419506 64 4.33 0.248 167 2006 2866544
Cleveland American 1994 87.70 1388905 81 3.67 0.256 141 2007 2944556
Colorado National 1995 98.30 2506789 68 5.04 0.265 186 2008 3154845
Detroit American 2000 172.80 2726048 74 4.64 0.270 151 2009 3240206
Houston American 2000 69.10 2153585 86 3.57 0.250 230 2010 3297828
Kansas City American 1973 112.90 2708549 95 3.73 0.269 139 2011 3305393
LA Angels American 1966 146.40 3012765 85 3.94 0.246 176 2012 3440000
LA Dodgers National 1962 230.40 3764815 92 3.44 0.250 187 2013 3650000
Miami National 2012 84.60 1752235 71 4.02 0.260 120 2014 3950000
Milwaukee National 2001 98.70 2542558 68 4.28 0.251 145 2015 4250000
Minnesota American 2010 108.30 2220054 83 4.07 0.247 156
NY Mets National 2009 100.10 2569753 90 3.43 0.244 177
NY Yankees American 2009 213.50 3193795 87 4.05 0.251 212
Oakland American 1966 80.80 1768175 68 4.14 0.251 146
Philadelphia National 2004 133.00 1831080 63 4.69 0.249 130
Pittsburgh National 2001 85.90 2498596 98 3.21 0.260 140
San Diego National 2004 126.60 2459742 74 4.09 0.243 148
San Francisco National 2000 166.50 3375882 84 3.72 0.267 136
Seattle American 1999 123.20 2193581 76 4.16 0.249 198
St. Louis National 2006 120.30 3520889 100 2.94 0.253 137
Tampa Bay American 1990 74.80 1287054 80 3.74 0.252 167
Texas American 1994 144.80 2491875 88 4.24 0.257 172
Toronto American 1989 116.40 2794891 93 3.8 0.269 232
Washington National 2008 174.50 2619843 83 3.62 0.251 177

In: Math

How does Zynga recognize revenue from virtual goods? Zynga was founded in July 2007 and is...

How does Zynga recognize revenue from virtual goods?


Zynga was founded in July 2007 and is headquartered in San Francisco, California. Around 80% of Zynga’s revenue comes from Facebook users. Facebook provides a social networking platform used by over 1 billion people, and Zynga is a video game developer with many products (e.g. FarmVille, MafiaWars) that interface with social technology sites like Facebook. Zynga has been publicly traded since December 16, 2011.


Zynga’s FarmVille players can use Facebook to purchase in-game currency they can use to acquire resources, such as hay and animals, in pursuit of a more productive virtual farm. Revenue from conversion of real dollars into in-game currency is big business: Zynga estimates that such sales, from FarmVille hay to Mafia Wars guns, accounted for nearly all of Zynga’s $1.1 billion in 2011 revenues and 12% of revenue for Facebook.


Revenue recognition in firms that earn money through socially-based use of virtual items is challenging. Zynga’s customers convert real dollars into FarmVille currency in order to purchase virtual goods. Customers’ real dollars become Farm Cash which the customers can use in the future to purchase virtual items in the Farmville application. When the customer uses Farm Cash to buy a tractor, for example, Facebook reduces the player’s Farm Cash, keeps 30% of the real dollar equivalent as a processing fee, and sends 70% to Zynga.


Starting in 2009, Zynga classified the game items it sells to players as either “consumable” or “durable” goods. The former category is for goods that players can immediately use, like energy in the game CityVille; the latter is for goods that players buy and keep for the duration of the game, such as tractors in FarmVille. Until 2010 Zynga estimated the average player life (the number of months a player on average continues to play the game) to be 19 months. In early 2011 it changed that estimate to 15 months. The shorter player life increased revenue for the six months by $27.3 million, turning a loss for the six months ended June 30, 2011 into a net profit of $18.1 million.


Required:
Discuss the revenue recognition at Zynga.

In: Accounting

Concord Corporation, a publicly-traded company, agreed to loan money to another company. On July 1, 2020,...

Concord Corporation, a publicly-traded company, agreed to loan money to another company. On July 1, 2020, the company received a five-year promissory note with a face value of $505,000, paying interest at a face rate of 5% on July 1 each year. The note was issued to yield an effective interest rate of 6%. Concord used the effective interest method of amortization for discounts or premiums, and the company’s year-end is September 30.

1. Use 1. PV.1 Tables, 2. a financial calculator, or 3. Excel functions to arrive at the amount to record the note receivable.

2. Prepare a schedule of note premium / discount amortization schedule

3. Prepare the journal entries to record the issue of the note on July 1, 2020, and any required accrual entries at the company’s year-end on September 30, 2020. Finally, prepare the journal entry to record the first cash collection received on July 1, 2021 for Concord Corporation.

In: Accounting

Part 2: Transforming data and computing descriptive statistics Create a quarterly real GDP series by dividing...

Part 2: Transforming data and computing descriptive statistics

Create a quarterly real GDP series by dividing nominal GDP by the GDP deflator. Also, create a money velocity series as PY/M where P is the price level, Y is real GDP, and M is the M3 money supply measure.

a. Plot the velocity of money (produce a graph similar to Figure 8.2 on page 212 of the textbook). Has velocity risen or fallen over the sample period? b. What is the mean and standard deviation of M3 velocity?

picture from textbook: https://media.cheggcdn.com/media%2Fb3d%2Fb3d56712-5053-405f-83e0-88009e1d6240%2FphpGAezM0.png

data to be used:

observation_date MABMM301CAQ189S observation_date CANGDPDEFQISMEI Quarterly v62295562 - Gross domestic product at market prices (x 1,000,000)
1981-01-01 2.04311E+11 1981-01-01 42.69811116 Q1 1981 354,784
1981-04-01 2.07984E+11 1981-04-01 43.66104146 Q2 1981 366,788
1981-07-01 2.16848E+11 1981-07-01 44.62899825 Q3 1981 371,560
1981-10-01 2.18082E+11 1981-10-01 45.29084386 Q4 1981 375,352
1982-01-01 2.17479E+11 1982-01-01 46.60831697 Q1 1982 381,676
1982-04-01 2.19886E+11 1982-04-01 47.57980057 Q2 1982 385,140
1982-07-01 2.2233E+11 1982-07-01 48.37395895 Q3 1982 388,116
1982-10-01 2.24304E+11 1982-10-01 49.3332838 Q4 1982 392,160
1983-01-01 2.2614E+11 1983-01-01 49.71327644 Q1 1983 401,680
1983-04-01 2.24478E+11 1983-04-01 50.26292877 Q2 1983 414,192
1983-07-01 2.25279E+11 1983-07-01 51.27358864 Q3 1983 427,308
1983-10-01 2.27179E+11 1983-10-01 51.61005878 Q4 1983 435,584
1984-01-01 2.283E+11 1984-01-01 51.97043324 Q1 1984 446,148
1984-04-01 2.32617E+11 1984-04-01 52.30782428 Q2 1984 457,828
1984-07-01 2.37141E+11 1984-07-01 52.72343939 Q3 1984 463,424
1984-10-01 2.40677E+11 1984-10-01 53.04197052 Q4 1984 473,572
1985-01-01 2.44981E+11 1985-01-01 53.42486468 Q1 1985 484,236
1985-04-01 2.48915E+11 1985-04-01 54.26513634 Q2 1985 493,432
1985-07-01 2.5245E+11 1985-07-01 54.50504061 Q3 1985 501,888
1985-10-01 2.5701E+11 1985-10-01 54.84025435 Q4 1985 512,744
1986-01-01 2.64237E+11 1986-01-01 55.25634718 Q1 1986 516,520
1986-04-01 2.68411E+11 1986-04-01 55.49059389 Q2 1986 521,696
1986-07-01 2.71948E+11 1986-07-01 56.09128688 Q3 1986 528,016
1986-10-01 2.8153E+11 1986-10-01 56.87836721 Q4 1986 531,568
1987-01-01 2.91177E+11 1987-01-01 57.53317263 Q1 1987 550,140
1987-04-01 2.99965E+11 1987-04-01 58.33295861 Q2 1987 565,020
1987-07-01 3.05585E+11 1987-07-01 58.89916591 Q3 1987 579,244
1987-10-01 3.08066E+11 1987-10-01 59.55513344 Q4 1987 593,300
1988-01-01 3.12459E+11 1988-01-01 60.19836959 Q1 1988 608,480
1988-04-01 3.22487E+11 1988-04-01 60.66882712 Q2 1988 618,684
1988-07-01 3.34801E+11 1988-07-01 61.66399317 Q3 1988 628,884
1988-10-01 3.42958E+11 1988-10-01 62.46758329 Q4 1988 641,556
1989-01-01 3.51835E+11 1989-01-01 62.92301878 Q1 1989 653,604
1989-04-01 3.62677E+11 1989-04-01 63.98918575 Q2 1989 667,232
1989-07-01 3.73418E+11 1989-07-01 64.65157352 Q3 1989 676,572
1989-10-01 3.85482E+11 1989-10-01 64.99380354 Q4 1989 678,696
1990-01-01 3.95554E+11 1990-01-01 65.40531748 Q1 1990 689,404
1990-04-01 4.0366E+11 1990-04-01 66.03354097 Q2 1990 693,132
1990-07-01 4.10993E+11 1990-07-01 66.70745429 Q3 1990 695,180
1990-10-01 4.1872E+11 1990-10-01 67.22290923 Q4 1990 694,272
1991-01-01 4.27352E+11 1991-01-01 67.93588676 Q1 1991 691,484
1991-04-01 4.32806E+11 1991-04-01 68.36517541 Q2 1991 699,036
1991-07-01 4.33277E+11 1991-07-01 68.59406171 Q3 1991 702,272
1991-10-01 4.39453E+11 1991-10-01 68.66059067 Q4 1991 704,220
1992-01-01 4.45823E+11 1992-01-01 68.94091388 Q1 1992 707,560
1992-04-01 4.50337E+11 1992-04-01 69.32202186 Q2 1992 712,328
1992-07-01 4.57429E+11 1992-07-01 69.62116203 Q3 1992 719,252
1992-10-01 4.64677E+11 1992-10-01 69.77651809 Q4 1992 724,936
1993-01-01 4.70009E+11 1993-01-01 69.96565871 Q1 1993 731,528
1993-04-01 4.72942E+11 1993-04-01 70.41575784 Q2 1993 742,932
1993-07-01 4.75799E+11 1993-07-01 70.19167988 Q3 1993 747,640
1993-10-01 4.79652E+11 1993-10-01 70.70364286 Q4 1993 756,332
1994-01-01 4.8357E+11 1994-01-01 70.95633662 Q1 1994 770,204
1994-04-01 4.89883E+11 1994-04-01 70.93029423 Q2 1994 781,204
1994-07-01 5.00109E+11 1994-07-01 71.5711307 Q3 1994 798,332
1994-10-01 5.03525E+11 1994-10-01 71.94169718 Q4 1994 808,288
1995-01-01 5.07562E+11 1995-01-01 72.43909165 Q1 1995 821,384
1995-04-01 5.15417E+11 1995-04-01 72.8401001 Q2 1995 826,212
1995-07-01 5.24551E+11 1995-07-01 73.10778253 Q3 1995 830,332
1995-10-01 5.29711E+11 1995-10-01 73.48182065 Q4 1995 837,964
1996-01-01 5.39297E+11 1996-01-01 73.73975025 Q1 1996 841,428
1996-04-01 5.45922E+11 1996-04-01 73.98403847 Q2 1996 850,092
1996-07-01 5.50767E+11 1996-07-01 74.34930978 Q3 1996 861,784
1996-10-01 5.55781E+11 1996-10-01 74.87572976 Q4 1996 874,788
1997-01-01 5.65662E+11 1997-01-01 75.08368347 Q1 1997 888,792
1997-04-01 5.70634E+11 1997-04-01 74.88081067 Q2 1997 896,372
1997-07-01 5.75825E+11 1997-07-01 75.08607625 Q3 1997 909,568
1997-10-01 5.85016E+11 1997-10-01 75.29788696 Q4 1997 920,876
1998-01-01 5.88563E+11 1998-01-01 75.10463509 Q1 1998 931,392
1998-04-01 5.92121E+11 1998-04-01 75.11357127 Q2 1998 931,908
1998-07-01 5.97459E+11 1998-07-01 74.72571561 Q3 1998 935,696
1998-10-01 6.02599E+11 1998-10-01 74.87131258 Q4 1998 950,184
1999-01-01 6.02129E+11 1999-01-01 75.21325796 Q1 1999 971,824
1999-04-01 6.13187E+11 1999-04-01 76.03927032 Q2 1999 990,748
1999-07-01 6.21062E+11 1999-07-01 76.91249304 Q3 1999 1,017,736
1999-10-01 6.32911E+11 1999-10-01 77.30843557 Q4 1999 1,037,516
2000-01-01 6.48037E+11 2000-01-01 78.22530767 Q1 2000 1,066,576
2000-04-01 6.58564E+11 2000-04-01 79.42312702 Q2 2000 1,095,808
2000-07-01 6.74681E+11 2000-07-01 80.21978873 Q3 2000 1,117,980
2000-10-01 6.83844E+11 2000-10-01 80.87774982 Q4 2000 1,129,156
2001-01-01 6.93689E+11 2001-01-01 81.65841791 Q1 2001 1,145,988
2001-04-01 6.96378E+11 2001-04-01 81.65117527 Q2 2001 1,148,844
2001-07-01 7.0454E+11 2001-07-01 80.70759895 Q3 2001 1,134,708
2001-10-01 7.1682E+11 2001-10-01 80.0583262 Q4 2001 1,132,480
2002-01-01 7.29263E+11 2002-01-01 80.41861287 Q1 2002 1,154,524
2002-04-01 7.34895E+11 2002-04-01 81.83423125 Q2 2002 1,181,544
2002-07-01 7.50367E+11 2002-07-01 82.38631683 Q3 2002 1,199,908
2002-10-01 7.58437E+11 2002-10-01 83.42950962 Q4 2002 1,221,832
2003-01-01 7.61874E+11 2003-01-01 84.59159619 Q1 2003 1,245,676
2003-04-01 7.82063E+11 2003-04-01 83.87561141 Q2 2003 1,233,300
2003-07-01 7.96029E+11 2003-07-01 84.95635352 Q3 2003 1,253,900
2003-10-01 8.07003E+11 2003-10-01 85.3524376 Q4 2003 1,268,384
2004-01-01 8.30867E+11 2004-01-01 86.29841734 Q1 2004 1,291,688
2004-04-01 8.50393E+11 2004-04-01 87.39335372 Q2 2004 1,323,544
2004-07-01 8.63961E+11 2004-07-01 87.89404677 Q3 2004 1,346,952
2004-10-01 8.85819E+11 2004-10-01 88.27669893 Q4 2004 1,362,528
2005-01-01 9.14545E+11 2005-01-01 88.82847112 Q1 2005 1,375,720
2005-04-01 9.38963E+11 2005-04-01 89.42817592 Q2 2005 1,394,868
2005-07-01 9.54247E+11 2005-07-01 90.72483988 Q3 2005 1,432,508
2005-10-01 9.62155E+11 2005-10-01 91.87374486 Q4 2005 1,465,016
2006-01-01 9.81505E+11 2006-01-01 91.54859597 Q1 2006 1,471,532
2006-04-01 9.99682E+11 2006-04-01 92.42400195 Q2 2006 1,486,320
2006-07-01 1.02234E+12 2006-07-01 93.05784619 Q3 2006 1,500,672
2006-10-01 1.04904E+12 2006-10-01 93.3031751 Q4 2006 1,510,304
2007-01-01 1.07602E+12 2007-01-01 94.71213816 Q1 2007 1,543,024
2007-04-01 1.10249E+12 2007-04-01 95.59095835 Q2 2007 1,572,372
2007-07-01 1.14279E+12 2007-07-01 95.53638302 Q3 2007 1,578,004
2007-10-01 1.17806E+12 2007-10-01 96.77660407 Q4 2007 1,600,728
2008-01-01 1.21117E+12 2008-01-01 98.67959821 Q1 2008 1,633,172
2008-04-01 1.25192E+12 2008-04-01 100.7423479 Q2 2008 1,673,096
2008-07-01 1.28028E+12 2008-07-01 100.9439217 Q3 2008 1,690,428
2008-10-01 1.30447E+12 2008-10-01 97.56793201 Q4 2008 1,614,996
2009-01-01 1.29867E+12 2009-01-01 96.02749385 Q1 2009 1,553,180
2009-04-01 1.29828E+12 2009-04-01 96.54850348 Q2 2009 1,544,376
2009-07-01 1.3059E+12 2009-07-01 97.33262931 Q3 2009 1,563,964
2009-10-01 1.31498E+12 2009-10-01 98.90114935 Q4 2009 1,607,940
2010-01-01 1.32874E+12 2010-01-01 99.68889426 Q1 2010 1,640,056
2010-04-01 1.36292E+12 2010-04-01 99.73092225 Q2 2010 1,649,184
2010-07-01 1.39202E+12 2010-07-01 99.76256572 Q3 2010 1,661,488
2010-10-01 1.40577E+12 2010-10-01 100.8028477 Q4 2010 1,697,792
2011-01-01 1.43135E+12 2011-01-01 102.1875932 Q1 2011 1,733,840
2011-04-01 1.45784E+12 2011-04-01 103.2762484 Q2 2011 1,755,640
2011-07-01 1.48928E+12 2011-07-01 103.3687047 Q3 2011 1,781,600
2011-10-01 1.52151E+12 2011-10-01 104.1128451 Q4 2011 1,808,604
2012-01-01 1.55116E+12 2012-01-01 104.2014187 Q1 2012 1,810,720
2012-04-01 1.57589E+12 2012-04-01 104.0827415 Q2 2012 1,814,628
2012-07-01 1.59536E+12 2012-07-01 104.5453546 Q3 2012 1,826,288
2012-10-01 1.61098E+12 2012-10-01 105.1788905 Q4 2012 1,839,596
2013-01-01 1.63607E+12 2013-01-01 105.9475736 Q1 2013 1,872,136
2013-04-01 1.67053E+12 2013-04-01 105.8155098 Q2 2013 1,881,924
2013-07-01 1.69833E+12 2013-07-01 106.3926058 Q3 2013 1,907,692
2013-10-01 1.75066E+12 2013-10-01 106.4727711 Q4 2013 1,928,372
2014-01-01 1.78916E+12 2014-01-01 108.0058207 Q1 2014 1,958,572
2014-04-01 1.81251E+12 2014-04-01 108.0948485 Q2 2014 1,983,684
2014-07-01 1.85501E+12 2014-07-01 108.6916969 Q3 2014 2,009,164
2014-10-01 1.89181E+12 2014-10-01 108.2081793 Q4 2014 2,009,312
2015-01-01 1.92827E+12 2015-01-01 107.1608189 Q1 2015 1,985,880
2015-04-01 1.95461E+12 2015-04-01 107.4289334 Q2 2015 1,987,968
2015-07-01 2.01857E+12 2015-07-01 107.7699516 Q3 2015 2,005,556
2015-10-01 2.05577E+12 2015-10-01 107.3727069 Q4 2015 2,000,240
2016-01-01 2.09906E+12 2016-01-01 107.1804261 Q1 2016 2,008,964
2016-04-01 2.14464E+12 2016-04-01 107.4862374 Q2 2016 2,009,416
2016-07-01 2.19735E+12 2016-07-01 108.2285573 Q3 2016 2,044,564
2016-10-01 2.2347E+12 2016-10-01 109.4481763 Q4 2016 2,079,080
2017-01-01 2.25137E+12 2017-01-01 110.2520312 Q1 2017 2,115,064
2017-04-01 2.30112E+12 2017-04-01 110.1958834 Q2 2017 2,136,712
2017-07-01 2.29036E+12 2017-07-01 110.2431625 Q3 2017

In: Economics

1. Two researcher want to find out whether there is a difference among graduation rates (these...

1. Two researcher want to find out whether there is a difference among graduation rates (these are in percentages) of five colleges over a 10-year period. Using the Data set below, determine if there a difference between colleges? Do this manually and show all eight steps in computing. (HINT: There is one variable being examined (i.e. graduation rates) for more than two groups (i.e. college 1, 2, 3, 4, 5) that are tested only once).

College 1

College 2

College 3

College 4

College 5

2005

67

82

94

65

88

2006

68

87

78

65

87

2007

65

83

81

45

86

2008

68

73

76

57

88

2009

67

77

75

68

89

2010

71

74

81

76

87

2011

78

76

79

77

81

2012

76

78

89

72

78

2013

72

76

76

69

89

2014

77

86

77

58

87

2. How do you interpret = 18.9, p < .05?

3. If the correlation between variable X and variable Y is perfect, what do you know about the prediction?

In: Statistics and Probability

1. Two researcher want to find out whether there is a difference among graduation rates (these...

1. Two researcher want to find out whether there is a difference among graduation rates (these are in percentages) of five colleges over a 10-year period. Using the Data set below, determine if there a difference between colleges? Do this manually and show all eight steps in computing. (HINT: There is one variable being examined (i.e. graduation rates) for more than two groups (i.e. college 1, 2, 3, 4, 5) that are tested only once).

College 1

College 2

College 3

College 4

College 5

2005

67

82

94

65

88

2006

68

87

78

65

87

2007

65

83

81

45

86

2008

68

73

76

57

88

2009

67

77

75

68

89

2010

71

74

81

76

87

2011

78

76

79

77

81

2012

76

78

89

72

78

2013

72

76

76

69

89

2014

77

86

77

58

87

2. How do you interpret = 18.9, p < .05?

3. If the correlation between variable X and variable Y is perfect, what do you know about the prediction?

In: Statistics and Probability

Many investors and financial analysts believe the Dow Jones Industrial Average (DJIA) gives a good barometer...

Many investors and financial analysts believe the Dow Jones Industrial Average (DJIA) gives a good barometer of the overall stock market. On January 31, 2006, 9 of the 30 stocks making up the DJIA increased in price (The Wall Street Journal, February 1, 2006). On the basis of this fact, a financial analyst claims we can assume that 30% of the stocks traded on the New York Stock Exchange (NYSE) went up the same day.

A sample of 62 stocks traded on the NYSE that day showed that 27 went up.

You are conducting a study to see if the proportion of stocks that went up is is significantly more than 0.3. You use a significance level of α=0.10α=0.10.

What is the test statistic for this sample? (Report answer accurate to three decimal places.)
test statistic =

What is the p-value for this sample? (Report answer accurate to four decimal places.)
p-value =

The p-value is...

  • less than (or equal to) αα
  • greater than αα



This test statistic leads to a decision to...

  • reject the null
  • accept the null
  • fail to reject the null



As such, the final conclusion is that...

  • There is sufficient evidence to warrant rejection of the claim that the proportion of stocks that went up is is more than 0.3.
  • There is not sufficient evidence to warrant rejection of the claim that the proportion of stocks that went up is is more than 0.3.
  • The sample data support the claim that the proportion of stocks that went up is is more than 0.3.
  • There is not sufficient sample evidence to support the claim that the proportion of stocks that went up is is more than 0.3.

In: Math

A cyclist won a bicycle race for seven consecutive years. His "winning" times and "victory" margins...

A cyclist won a bicycle race for seven consecutive years. His "winning" times and "victory" margins (time difference of the second place finisher) are given in the figure below.

Year Time
(h:m:s)
Margin
(m:s)
1999 91:32:19 7:35
2000 92:33:07 6:07
2001 86:17:27 6:41
2002 82:05:12 7:18
2003 83:41:12 1:05
2004 83:36:05 6:13
2005 86:15:05 4:20

(a) Find the mean, median and mode of the cyclist's times. (If an answer does not exist, enter DNE.)

mean     :  :  h:m:s
median     :  :  h:m:s
mode     :  :  h:m:s


(b) Find the mean, median and mode of the cyclist's margins. (If an answer does not exist, enter DNE.)

mean       :  m:s
median       :  m:s
mode       :  m:s

In: Statistics and Probability