Questions
America Online (AOL) is a leader in the Internet access provider industry. In 1996, the company...

America Online (AOL) is a leader in the Internet access provider industry. In 1996, the company changed a controversial accounting method involving the treatment of the cost of advertising and free trials. The following is an excerpt from a May 15, 2000, CNET News.com article:

America Online will pay a civil penalty of $3.5 million as part of a settlement with the Securities and Exchange Commission over the accounting of advertising costs. According to the SEC, the Internet and media giant improperly reported most of the costs of acquiring new subscribers – such as the expense of sending computer disks to potential customers – as an asset. As a result, the SEC said AOL posted a profit for six of eight quarters in 1995 and 1996 but would have recorded a loss if the company followed recommended accounting practices.

AOL, backed by its auditor, defended the accounting method of capitalizing these costs arguing that spreading the costs over two years was a justifiable way to match expenses against revenue flows that would emerge later. In 1996, AOL switched to expensing these costs in the period incurred.

Consider the general treatment of advertising and promotion costs.  

  • Why are these costs normally expensed in the period incurred even though they are incurred with the intention of generating future revenues?  
  • Why did they expense these costs over a two-year period?  
  • Then discuss the possible reasons why AOL chose a different approach followed by a discussion of the possible reasons why the company decided to change its method. Also include the civil penalty of $3.5 million leveled by the SEC four years after AOL changed its method.  

In: Finance

The following data set provides information on the lottery sales, proceeds, and prizes by year in...

The following data set provides information on the lottery sales, proceeds, and prizes by year in Iowa.

FYI Sales Proceeds Prizes
1986 $85,031,584 $27,631,613 $39,269,612
1987 $98,292,366 $31,157,797 $47,255,945
1988 $128,948,560 $40,090,157 $65,820,798
1989 $172,488,594 $49,183,227 $92,563,898
1990 $168,346,888 $50,535,644 $90,818,207
1991 $158,081,953 $44,053,446 $86,382,329
1992 $166,311,122 $45,678,558 $92,939,035
1993 $207,192,724 $56,092,638 $116,820,274
1994 $206,941,796 $56,654,308 $116,502,450
1995 $207,648,303 $58,159,175 $112,563,375
1996 $190,004,182 $51,337,907 $102,820,278
1997 $173,655,030 $43,282,909 $96,897,120
1998 $173,876,206 $42,947,928 $96,374,445
1999 $184,065,581 $45,782,809 $101,981,094
2000 $178,205,366 $44,769,519 $98,392,253
2001 $174,943,317 $44,250,798 $96,712,105
2002 $181,305,805 $48,165,186 $99,996,233
2003 $187,829,568 $47,970,711 $104,199,159
2004 $208,535,200 $55,791,763 $114,456,963
2005 $210,669,212 $51,094,109 $113,455,673
2006 $339,519,523 $80,875,796 $122,258,603
2007 $235,078,910 $58,150,437 $133,356,860
2008 $249,217,468 $56,546,118 $144,669,575
2009 $243,337,101 $60,553,306 $138,425,341
2010 $256,255,637 $57,907,066 $150,453,787
2011 $271,391,047 $68,001,753 $158,961,078
2012 $310,851,725 $78,731,949 $182,442,447
2013 $339,251,420 $84,890,729 $200,801,768
2014 $314,055,429 $73,972,114 $186,948,985
2015 $324,767,416 $74,517,068 $196,882,289
2016 $366,910,923 $88,024,619 $221,767,401

You decided to find the linear equation that corresponds to sales and year. Create a graph using the sales and year. Add the linear equation to the graph. What is the y-intercept of the linear equation?

Round each value below to the nearest integer.

Provide your answer below: ____E+ ___

In: Statistics and Probability

Average hourly earnings in the U.S. retail trade industry (in current dollars and constant dollars) are...

Average hourly earnings in the U.S. retail trade industry (in current dollars and constant dollars) are shown in the table.

Year

1990

1995

2000

2002

2003

Current dollars

4.88

5.94

6.75

7.13

7.29

Constant dollars

5.70

5.39

5.07

5.00

4.97

a. Define the terms current dollars and constant dollars. You will have to

look these terms up

b. Find the least squares regression line that approximates the average hourly earnings in both current dollars and constant dollars for this industry. Find the correlation coefficient in both cases. Comment on the meanings of the correlation coefficients.

c. Find where the two regression lines that you obtained in part b intersect. What does this point mean?

d. What are the slopes of the regression lines? What do they mean? (Use correct units.) What does this say about the long-term prospects of retail trade industry employees?

e. Use the regression lines to estimate the difference in current dollar and constant dollar hourly earnings in the year 2005.

Graph both of the regression lines on the same set of axes. (in Excel)

If you were a union negotiator for employees in the retail trade industry, how would you use this information?

  1. Use function notation when you write the linear regression equations.
  2. Use descriptive variables.
  3. Precisely define the variables.
  4. Make sure you show your work for questions c and e.
  5. You should only have one graph. It should contain both regression equations and the data.

In: Statistics and Probability

Case Study When Jack Welch assumed the top position at General Electric in 1981, he inherited...

Case Study

When Jack Welch assumed the top position at General Electric in 1981, he inherited a company that had a market value of $12 billion — certainly a modest number, by today’s standards. By the time he left in 1998, GE was worth $280 billion.While leading GE, Welch was charged with the task of making the conglomerate better by any means necessary. With his gut telling him that his company was due for a complete overhaul, Welch decided to implement Six Sigma at GE in 1995.

Six-Sigma is a methodology that aims to reduce defects and errors in all processes, including transactional processes and manufacturing processes. Organizations that use Six Sigma test their processes again and again to make sure that they are as close to perfect as possible.Five years after Welch’s decision to implement Six Sigma, GE had saved a mind-blowing $10 billion.

Welch claimed to have spent as much as half of his time working on people issues.By assembling the right team and ingraining them with the right management philosophies, Welch successfully oversaw the transformation of GE from a relatively strong company to a true international juggernaut.

Questions:

  1. Explain the possible key concepts behind the change methodology in General Electric Company? Do you agree with the change methodology introduced in the General Electric Company by its leader? Give logical reasons to defend your answer

Important Points:

  • The Case study background/introduction must be written before answering the questions
  • Proper referencing needed if the student uses any resources to answer the questions

In: Operations Management

Please Use R studio and show all the steps to answer this question NY Marathon 2013...

Please Use R studio and show all the steps to answer this question

NY Marathon 2013 the table below shows the winning times (in minutes) for men and women in the new york city marathon between 1978 and 2013. (the race was not run in 2012 because of superstorm sandy.) assuming that performances in the big apple resemble performances elsewhere, we can think of these data as a sample of performance in marathon competitions. Create a 90% confidence interval for the mean difference in winning times for male and female marathon competitors.

Year

Men

Women

Year

Men

Women

1978

132.2

152.5

1996

129.9

148.3

1979

131.7

147.6

1997

128.2

148.7

1980

129.7

145.7

1998

128.8

145.3

1981

128.2

145.5

1999

129.2

145.1

1982

129.5

147.2

2000

130.2

145.8

1983

129.0

147.0

2001

127.7

144.4

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

134.9

131.6

131.1

131.0

128.3

128.0

132.7

129.5

129.5

130.1

131.4

131.1

149.5

148.6

148.1

150.3

148.1

145.5

150.8

147.5

144.7

146.4

147.6

148.1

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

128.1

130.5

129.5

129.5

130.0

129.1

128.7

129.3

128.3

125.1

Cancelled

128.4

145.9

142.5

143.2

144.7

145.1

143.2

143.9

148.9

148.3

143.3

Cancelled

140.1

In: Statistics and Probability

Please Use R studio to answer this question NY Marathon 2013 the table below shows the...

Please Use R studio to answer this question

NY Marathon 2013 the table below shows the winning times (in minutes) for men and women in the new york city marathon between 1978 and 2013. (the race was not run in 2012 because of superstorm sandy.) assuming that performances in the big apple resemble performances elsewhere, we can think of these data as a sample of performance in marathon competitions. Create a 90% confidence interval for the mean difference in winning times for male and female marathon competitors.

Year

Men

Women

Year

Men

Women

1978

132.2

152.5

1996

129.9

148.3

1979

131.7

147.6

1997

128.2

148.7

1980

129.7

145.7

1998

128.8

145.3

1981

128.2

145.5

1999

129.2

145.1

1982

129.5

147.2

2000

130.2

145.8

1983

129.0

147.0

2001

127.7

144.4

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

134.9

131.6

131.1

131.0

128.3

128.0

132.7

129.5

129.5

130.1

131.4

131.1

149.5

148.6

148.1

150.3

148.1

145.5

150.8

147.5

144.7

146.4

147.6

148.1

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

128.1

130.5

129.5

129.5

130.0

129.1

128.7

129.3

128.3

125.1

Cancelled

128.4

145.9

142.5

143.2

144.7

145.1

143.2

143.9

148.9

148.3

143.3

Cancelled

140.1

In: Statistics and Probability

Barbara Lynch, the product manager for a line of skiwear produced by HeathCo Industries, has been...

Barbara Lynch, the product manager for a line of skiwear produced by HeathCo Industries, has been working on developing sales forecasts for the skiwear that is sold under the Northern Slopes and Jacque Monri brands. She has had various regression-based forecasting models developed. Quarterly sales for 1988Q1 through 1997Q4 are as follows:

Sales
Year Q1 Q2 Q3 Q4
1988 72,962 81,921 97,729 142,161
1989 145,592 117,129 114,159 151,402
1990 153,907 100,144 123,242 128,497
1991 176,076 180,440 162,665 220,818
1992 202,415 211,780 163,710 200,135
1993 174,200 182,556 198,990 243,700
1994 253,142 218,755 225,422 253,653
1995 257,156 202,568 224,482 229,879
1996 289,321 266,095 262,938 322,052
1997 313,769 315,011 264,939 301,479

a) Prepare a time-series plot of the data, and on the basis of what you see in the plot, write a brief paragraph in which you explain what patterns you think are present in the sales series.

b) Smooth out seasonal influences and irregular movement by calculating the center moving averages. Add the centered moving averages to the original data you plotted in part a. Has the process of calculating center moving averages been effective in smoothing out the seasonal and irregular fluctuations in the data? Explain.

c) Determine the degree of seasonality by calculating seasonal indexes for each quarter of the year.

d) Develop a forecast for Ms Lynch for the four quarters of 1998.

In: Statistics and Probability

3300 Econometric HW obs RWAGES PRODUCT 1959 59.87100 48.02600 1960 61.31800 48.86500 1961 63.05400 50.56700 1962...

3300 Econometric HW

obs RWAGES PRODUCT
1959 59.87100 48.02600
1960 61.31800 48.86500
1961 63.05400 50.56700
1962 65.19200 52.88200
1963 66.63300 54.95000
1964 68.25700 56.80800
1965 69.67600 58.81700
1966 72.30000 61.20400
1967 74.12100 62.54200
1968 76.89500 64.67700
1969 78.00800 64.99300
1970 79.45200 66.28500
1971 80.88600 69.01500
1972 83.32800 71.24300
1973 85.06200 73.41000
1974 83.98800 72.25700
1975 84.84300 74.79200
1976 87.14800 77.14500
1977 88.33500 78.45500
1978 89.73600 79.32000
1979 89.86300 79.30500
1980 89.59200 79.15100
1981 89.64500 80.77800
1982 90.63700 80.14800
1983 90.59100 83.00100
1984 90.71200 85.21400
1985 91.91000 87.13100
1986 94.86900 89.67300
1987 95.20700 90.13300
1988 96.52700 91.50600
1989 95.00500 92.40800
1990 96.21900 94.38500
1991 97.46500 95.90300
1992 100.00000 100.00000
1993 99.71200 100.38600
1994 99.02400 101.34900
1995 98.69000 101.49500
1996 99.47800 104.49200
1997 100.51200 106.47800
1998 105.17300 109.47400
1999 108.04400 112.82800
2000 111.99200 116.11700
2001 113.53600 119.08200
2002 115.69400 123.94800
2003 117.70900 128.70500
2004 118.94900 132.39000
2005 119.69200 135.02100
2006 120.44700 136.40000

Problem 2.

Use the data in the “Autocorrelation” tab to test

  1. For Autocorrelation using the Durbin Watson Test

  2. Graph the Residuals and determine whether they are distributed normally or whether they are biased

In: Math

<< UNIX >>> BY this file contains ELMER SOLVER (v 8.4) STARTED AT: 2020/03/18 13:04:22 ParCommInit:...

<< UNIX >>>

BY this file contains

ELMER SOLVER (v 8.4) STARTED AT: 2020/03/18 13:04:22
ParCommInit: Initialize #PEs: 1
MAIN:
MAIN: =============================================================
MAIN: ElmerSolver finite element software, Welcome!
MAIN: This program is free software licensed under (L)GPL
MAIN: Copyright 1st April 1995 - , CSC - IT Center for Science Ltd.
MAIN: Webpage http://www.csc.fi/elmer, Email [email protected]
MAIN: Version: 8.4 (Rev: unknown, Compiled: 2020-02-28)
MAIN: Running one task without MPI parallelization.
MAIN: Running with just one thread per task.
MAIN: HYPRE library linked in.
MAIN: MUMPS library linked in.
MAIN: =============================================================
MAIN:
MAIN:
MAIN: -------------------------------------
MAIN: Reading Model: case.sif
LoadInputFile: Scanning input file: case.sif
LoadInputFile: Loading input file: case.sif
WARNING:: LoadInputFile: There are no BCs in the system!
LoadInputFile: Number of Body Forces: 1
LoadInputFile: Number of Initial Conditions: 0
LoadInputFile: Number of Materials: 1
LoadInputFile: Number of Equations: 1
LoadInputFile: Number of Solvers: 1
LoadInputFile: Number of Bodies: 1
Loading user function library: [HeatSolve]...[HeatSolver_Init0]
LoadMesh: Base mesh name: ./.
LoadMesh: Elapsed REAL time: 0.5294 (s)

Question: I want to create new file that just print between "==" by sing Unix/Linx

this code isn't complete because the output is not correct

#!/bin/bash
while ISF= read line
do

if [[ $line = *"="* ]]

then

echo $line >> Lines

fi

done < "unix"

I REALLY NEED HELP

THANKS,

In: Computer Science

****C language**** char lName[][15] = {"Brum","Carroll","Carter","Dodson","Garbus", "Greenwood", "Hilliard", "Lee", "Mann", "Notz", "Pastrana", "Rhon", "Rodriguez", "Wilson", "Zimmerman"};...

****C language****

char lName[][15] = {"Brum","Carroll","Carter","Dodson","Garbus", "Greenwood", "Hilliard", "Lee", "Mann", "Notz", "Pastrana", "Rhon", "Rodriguez", "Wilson", "Zimmerman"};

char fName [][15] = {"Natalie","Cody","Sophia","Dominic","Chandler","Caleb","Sydnee","Peyton","Brianna","Zachery","Kevin","Luke","Juan","Kelci","Adam"};

char middleInitial[15]={'N','L','X','L','O','L','M','B','S','T','J','C','P','D','Z'};

char dob[][11]={"05/27/1935","11/27/1971","10/17/2003","12/08/1990","11/25/1991","10/30/1992","09/22/1993","08/04/1994","07/11/1995","06/18/1996","05/28/1997","04/07/1998","03/12/1999","02/23/2000","01/15/2001"};

How would we make a list ordered by their age, oldest first, Print the patient's full name and then their age.  Left justify the name and right justify the age.

Example:
Johnson, Fred N 80

**Half of the code is provided**

int patientAge[15] = {0};

for(int p = 0; p <15; p++)
{
int year = ((dob[p][6] - '0') * 1000) + ((dob[p][7] - '0') *100) + ((dob[p][8] - '0') * 10) + ((dob[p][9] - '0') * 1);

patientAge[p] = 2019 - year;
printf("%s, %s %c Age: %d\n",lName[p], fName[p], middleInitial[p], patientAge[p]);
}

In: Computer Science