1. Define the term skewness and kurtosis. With the aid of a diagram, describe the three types of kurtosis.            4marks
2. Describe the components of a time series and mention their applicability. 4marks

3. The table below relates to the sales of refrigerators by a certain firm in Kenya.

Year production in (000) Year Production in (000)

1996 17    2002 35

1997 20 2003   55

1998 19 2004 50

1999 26 2005 74

2000 24 2006 69

2001 40

Using this data;

1. Fit a straight line trend by the method of ordinary least squares
2. ii. Estimate the number of refrigerators that will be sold in the year 2009.

The table below shows the murder rate per 100,000 residents for a large American city over a twelve-year period.

For the data below:

(a)           Determine the least squares regression line using Excel.

(b)           Determine the predicted value for 2004.

(c)           Determine the 3 year moving average.

(d)           Determine the MSE for the trend line (in a) and the 3 year moving average (in c.) Which forecasting method is better? Explain.

All Time Box Office Revenues Aggregated by Months

1. Calculate the mean for the gross revenues.
2. Calculate the mean for the Movies tracked.
3. Calculate the standard deviation for the movies tracked.

Please Convert yearly data to quarterly data on excel

Income Level

Canada United States

Year Annual Annual

2000 22750 59938

2001 23110 58609

2002 23580 57947

2003 25480 57875

2004 29530 57674

2005 34300 58291

2006 37890 58746

2007 41530 59534

2008 44930 57412

2009 43220 57010

2010 44480 55520

2011 47180 54673

2012 51080 54569

2013 52800 56479

2014 52190 55613

2015 47580 58476

2016 43940 60309

2017 43000

2018 44860

Using the data provided:

1. Plot the data. What does the plot tell you about the appropriateness of using a trend and seasonally adjusted model for your data?
2. Develop a forecast model that incorporates both a trend and seasonal adjustment (even if its not necessarily there).
3. Develop a forecast for the four periods beyond your data.
4. Calculate a MAD and Bias based on the last 12 periods of known data. What do the MAD and bias indicate about your model?
5. Overall, are you satisfied with your model? If so, why? If not, suggest some things that could be done to improve your model.

data:

The following selected transactions relate to liabilities of United Insulation Corporation. United’s fiscal year ends on December 31.

2021

2022

Required:

Prepare the appropriate journal entries through the maturity of each liability.

1) Record a revolving credit agreement negotiated with Parish Bank that can be renewed annually upon bank approval. The amount available under the line of credit is $24.0 million at the bank’s prime rate.

2) Record a three-month bank loan of $7.6 million with Parish Bank under the line of credit agreement. Interest at the prime rate of 13% was payable at maturity

3) Record the payment of the 13% note at maturity

4) Record the issuance of $10.6 million of commercial paper on a nine-month note, supported by the credit line. Interest was discounted at issuance at a 12% discount rate

5) Record necessary adjusting entry to accrue interest on December 31

6) Record interest on commercial paper in 2022

7) Record the repayment of commercial paper at maturity

Energy consumed in the US can be classified ascoming from one of three sources: fossil fuels, nuclear power, andrenewable energy. In 2014, the energy from these three sourceswas 80.3, 8.3, and 9.6 quadrillion BTU, respectively. In 2004, thecorresponding amounts were 85.8, 8.2, and 6.1. Write a descriptionof the changes from 2004 to 2014 expressed in these data. Illustrateyour summary with appropriate graphical summaries. Be sure todiscuss both the amounts of energy from each source as well as thepercents.

An article in Information Security Technical Report [“Malicious Software—Past, Present and Future” (2004, Vol. 9, pp. 6–18)] provided the following data on the top 10 malicious software instances for 2002. The clear leader in the number of registered incidences for the year 2002 was the Internet worm “Klez,” and it is still one of the most widespread threats. This virus was first detected on 26 October 2001, and it has held the top spot among malicious software for the longest period in the history of virology.

The 10 most widespread malicious programs for 2002

(Source: Kaspersky Labs).

Suppose that 20 malicious software instances are reported. Assume that the malicious sources can be assumed to be independent. (a) What is the probability that at least one instance is “Klez?” (b) What is the probability that three or more instances are “Klez?” (c) What are the mean and standard deviation of the number of “Klez” instances among the 20 reported?

1. Do the variables have significant correlation? For full credit, you must show each step of the hypothesis test. Use the 0.05 significance.

2. In 2008, the price of gas dropped drastically and hit a low average of $1.59 for the nation. What effect do you think this will have on the alternative-fuel car sales, if any? Do you think that this would affect the number of alternative-fueled vehicles used in the United States? Do you think that it would follow the same pattern as before 2008? Write 2 or 3 sentences explaining how you think the new vehicles will affect the number of alternative-fueled vehicles in the United States.

3. Use your regression equation to predict the number of alternative-fueled vehicles used in the United States in 2010. Assume that the pattern remains the same after the introduction of the electric-gas vehicles. Show your work.

4. Search online to find some evidence for or against your opinion in part e. Give the information that you found and state the URL to the data. Was your prediction correct or incorrect? Why do you think that happened? Write 2 or 3 sentences summarizing the information that you found and explain why you think that happened. Be sure to answer each question.