An economist with a major bank wants to learn, quantitatively, how much spending on luxury goods and services can be explained based on consumers’ perception about the current state of the economy and what do they expect in the near future (6 months ahead).  Consumers, of all income and wealth classes, were surveyed.  Every year, 1500 consumers were interviewed.  The bank having all of the data from the 1500 consumers interviewed every year, computed the average level of consumer confidence (an index ranging from 0 to 100, 100 being absolutely optimistic) and computed the average dollar amount spent on luxuries annually.  Below is the data shown for the last 24 years.

Date                 X                     Y (in thousands of dollars)

1994                79.1                 55.6

1995                79                    54.8

1996                80.2                 55.4

1997                80.5                 55.9

1998                81.2                 56.4

1999                80.8                 57.3

2000                81.2                 57

2001                80.7                 57.5

2002                80.3                 56.9

2003                79.4                 55.8

2004                78.6                 56.1

2005                78.3                 55.7

2006                78.3                 55.7

2007                77.8                 55

2008                77.7                 54.4

2009                77.6                 54

2010                77.6                 56

2011                78.5                 56.7

2012                78.3                 56.3

2013                78.5                 57.2

2014                78.9                 57.8

2015                79.8                 58.7

2016                80.4                 59.3

2017                80.7                 59.9

Question:

1. Measure the strength of the linear association between consumers’ moods and the dollar amounts spent on luxury items.

An economist with a major bank wants to learn, quantitatively, how much spending on luxury goods and services can be explained based on consumers’ perception about the current state of the economy and what do they expect in the near future (6 months ahead).  Consumers, of all income and wealth classes, were surveyed.  Every year, 1500 consumers were interviewed.  The bank having all of the data from the 1500 consumers interviewed every year, computed the average level of consumer confidence (an index ranging from 0 to 100, 100 being absolutely optimistic) and computed the average dollar amount spent on luxuries annually.  Below is the data shown for the last 24 years.

Date                 X                     Y (in thousands of dollars)

1994                79.1                 55.6

1995                79                    54.8

1996                80.2                 55.4

1997                80.5                 55.9

1998                81.2                 56.4

1999                80.8                 57.3

2000                81.2                 57

2001                80.7                 57.5

2002                80.3                 56.9

2003                79.4                 55.8

2004                78.6                 56.1

2005                78.3                 55.7

2006                78.3                 55.7

2007                77.8                 55

2008                77.7                 54.4

2009                77.6                 54

2010                77.6                 56

2011                78.5                 56.7

2012                78.3                 56.3

2013                78.5                 57.2

2014                78.9                 57.8

2015                79.8                 58.7

2016                80.4                 59.3

2017                80.7                 59.9

Question:

1. Do you think that measuring the level of optimism is a good predictor for trying to forecast future spending on luxury items?  Explain why or why not.

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

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+ ___

Problem #3

Faith and Healing.

A higher percentage of southerners believe in God and prayer, according to a

1998 study by the University of North Carolina’s Institute for Research in Social Science. The

survey was conducted by means of telephone interviews with 844 adults in 12 southern states and

413 adults in other states. One of the findings was that 46% of southerners believe they have been

healed by prayer, compared with 28% of others. Assume that the results of the UNC survey are the

true, population percentages for these regions. Suppose that 20 southerners are to be selected at

random and asked if they believe they have been healed by prayer. We are interested in the number

who answer “Yes” to this question.

a) What is an appropriate statistical model?

Clearly specify and define a random variable. (Let X = ...)

State the model, verify conditions, and identify all parameters.

b)Of the 20 southerners selected, what is the expected number of “Yes” responses? Write an

expected value statement, give the formula, give the formula with numbers plugged in, give

your final answer.

For parts c), d), e), and f) write a probability statement and give a numerical answer.

c) What is the probability that exactly 10 responded “Yes”?

d) What is the probability that between 10 and 15 (

both inclusive) responded “Yes”?

e) What is the probability that over 75% of the 20 responded “Yes”?

f) What is the probability that less than 8 responded “Yes”?

g) Suppose a sample of 100 non-southerners were to be selected at random and asked if they

believe they have been healed by prayer. What is the expected number of “Yes” responses

and what is the standard deviation for the number of “Yes” responses?

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

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.

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.

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:

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.

1. Periodically, the county Water Department tests the drinking water of homeowners for contminants such as lead and copper. The lead and copper levels in water specimens collected in 1998 for a sample of 10 residents of a subdevelopement of the county are shown below.

(a)    Construct a 99% confidence interval for the mean lead level in water specimans of the subdevelopment.
≤μ≤

(b)    Construct a 99% confidence interval for the mean copper level in water specimans of the subdevelopment.  
≤μ≤

2.

The scientific productivity of major world cities was the subject of a recent study. The study determined the number of scientific papers published between 1994 and 1997 by researchers from each of the 20 world cities, and is shown below.

Construct a 97 % confidence interval for the average number of papers published in major world cities.

<μ<

answer part b only

(dont post the screenshot of the answere kindly post it in text form)

Required:

1. Please make a cash budget for the months of January, February and March 1999 based on the data for:

View Receivable Trend:

• 30% of Sales are collected in the month of sale
• 30% of Sales are collected after the month of sale
• 40% of Sales are collected two months after the sale is made

View Payable Trend:

• 10% of Purchases are paid for in the month of purchase
• 35% of Purchases are paid after the month of purchase
• 55% of Purchases are paid two months after the purchase is made

Additional Information:

• Rent and Insurance expense were prepaid at the end of 1998
• All other expenses are paid for in the month they were incurred
• November Sales = 195,000
• November Purchases = 100,000
• December Sales = 250,000
• December Purchases = 165,000
• Please see attached Budgeted Income Statement for 1st Quarter 1999

2. Being a CFO of the company, interpret the importance budget in strategic and operational planning of the company (Word limit Max 150-200)