Consider the four primary uses of nonverbal behavior—expressing emotion, conveying attitudes, communicating personality traits, and facilitating verbal communication. There are two parts to this activity. First, over the course of 2 days, pay close attention to the amount of eye contact, types of voice changes, body positions, and movements others make with you in different situations. This could include casual friends at work, family members in your home, strangers waiting for a bus, fellow bar patrons or church attendees, etc. Second, for the next 2 days, again observe others’ behaviors in a variety of situations. This time, however, try minimizing your own use of nonverbal communication (hint: dark sunglasses may help!).

Describe the patterns of nonverbal behavior you observed in others for both parts of the activity. Why do you think different people engaged in different types of nonverbal behavior? What emotions, attitudes, and personality traits did these nonverbal cues suggest? How did people respond to your lack of nonverbal cues?

Table 4.1 below presents the numbers of full-time teaching staff at Canadian universities in 2019, by academic rank and by gender. Source: https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3710007601

Table 4.1

4.1 What is the probability that a randomly selected university teaching staff member was a female?

Fraction answer: ____________           Answer: __________ (4 decimal pl)

4.2 What is the probability that a randomly selected university teaching staff member was a male and a full professor?                                                                                                

Fraction answer: ____________           Answer: __________ (4 decimal pl)

4.3 What is the probability that a randomly selected university teaching staff member was a male or an assistant professor?

Fraction answer: ____________           Answer: __________ (4 decimal pl)

4.4 Given a male teaching staff member, what is the probability that this person is a lecturer?   

Fraction answer: ____________           Answer: __________ (4 decimal pl)

4.5 Is there statistical dependence between gender and academic rank based on the data in Table 4.1? Support your answer with appropriate statistical calculations.

Relevant formulas: ___________________________________________

Supporting calculations (4 decimal places): ______________________________________

Decision: ______________________

4.6 Table 4-2 below lists the approximate probabilities for different academic ranks for staff in Canadian universities and typical corresponding salaries. Source: https://www.macleans.ca/education/comparing-the-average-salaries-of-canadian-professors-in-2018/

Table 4.2

Evaluate the expected value and the standard deviation of the academic worker salaries:

Calculator functions for expected value:    __________________________

Expected value: _________________________ (to nearest $1000)

Calculator functions for standard deviation:   

Standard deviation: ______________________ (to nearest $1000)

Nicholas Grammas is an investment analyst examining the performance of two mutual funds with Janus Capital Group: The Janus Balanced Fund and the Janus Overseas Fund.The following table reports the annual returns (in percent) of these two funds over the past 10 years. We assume the sample returns are drwan independently from normally distributed populations.

In a report, use the above information to:

1. Describe the similarities and differences in these two funds’ returns that you can observe from their descriptive statistics.

2. What is the two-tailed p-value?

3. Determine whether the risk of one fund is different from the risk of the other fund at the 5% significance level. (Two Sentences: one stating your decision using the p-value approach, and another stating your conclusion.)

Show all working out and reasoning, be specific and detailed please. Please do all working out in Excel only. Thank you. This is about Chi Squared Distribution:Statistical Inference Concerning Variance and F Distribution:Inference Concerning Ratio of Two Population Variances to give you an idea about what formulas I'm looking for. Thank you.

1) The worksheet Engines in the HW8 data workbook on Moodle describe a suppliers shipments of engines per year to their customers from 1999 through 2018.

a) Use simple regression with Shipments as the independent or Y variable and Year as the dependent or X variable to fit the data. Determine MAE, MSE and MAPE for the simple regression model. Construct a chart that has the observed data and the fit line by Year. Use the simple regression model to predict Shipments for years 2019 and 2020.

b) Use a three time period Moving Average to fit the rate data. Determine MAE, MSE and MAPE for the Moving Average model. Construct a chart that has the observed data and the fit line by Year. Use the Moving Average model to predict Shipments for years 2019 and 2020.

c) Use exponential smoothing with a smoothing constant of 0.15 to fit the data. Determine MAE, MSE and MAPE for the exponential smoothing model. Use the model to forecast Shipments for years 2019 and 2020.

d) Short answer. Which of the three above forecasting models (simple regression, moving average and exponential smoothing) would you use to model the data and why would you use that model.

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.

A common tactic to manage earnings is to “stuff the channels”, that is, to ship product prematurely to dealers and customers, thereby inflating sales for the period. A case in point is Bristol-Myers Squibb Co. (BMS), a multinational pharmaceutical company headquartered in New York. In August 2004, the SEC announced a $150 million penalty levied against BMS. This was part of an agreement to settle charges by the SEC that the company had engaged in a fraudulent scheme to inflate sales and earnings in order to meet analysts’ earnings forecasts.

The scheme involved recognition of revenue on pharmaceutical products shipped to its wholesalers in excess of the amounts demanded by them. These shipments amounted to $1.5 billion U.S. during 2001-2002. To persuade its wholesalers to accept this excess inventory, BMS agreed to cover their carrying costs, amounting to millions of dollars per quarter. In addition, BMS understated its accruals for rebates and discounts allowed to its large customers.

According to the SEC, the company also engaged in “cookie jar” accounting. That is, it created phony reserves for disposals of unneeded plants and divisions during high-profit quarters. These would be transferred to reduce operating expenses in low-profit quarters when BMS’s earnings still fell short of amounts needed to meet forecasts.

Required:

1. Give reasons why managers would resort to extreme earnings management tactics such as these.

[4 marks]

2. Evaluate the effectiveness of stuffing the channels as an earnings management device. Consider both from the standpoint of a single year and over a series of years.

[5 marks]

1. Evaluate the effectiveness of cookie jar accounting as an earnings management device.

The following six (4) questions are based on the following data:

When performing calculations in the following problems, use the numbers in the table as-is. I.e., do NOT convert 8.5985 to 8.5985% (or 0.085985). Just use plain 8.5985.

1. Using the basic market model regression, R p = α + β R m + ϵ, what is the beta of this portfolio? Yes, this is an opportunity to practice regression analysis. You can use Excel or other tool of choice.

2. For precision, find the portfolio beta using the excess return market model:

R p − R f = α + β ∗ ( R m − R f ) + ϵ

[Hint: compute annual excess returns first, then run regression.]

3. Using the excess return beta β ∗ from the previous problem, what is Jensen's alpha for the portfolio?

[Hint: use Equation (17.6) from Moore (2015)]

4. What is the portfolio's M2 measure?