1. Insurance companies track life expectancy information to assist in determining the cost of life insurance policies. Life expectancy is a statistical measure of average time a person is expected to live, based on a number of demographic factors. Mathematically, life expectancy is the mean number of years of life remaining at a given age, assuming age-specific mortality rates remain at their most recently measured levels. Last year the average life expectancy of all the Life Insurance policyholders in Ontario at age 65 was 22.3 years (meaning that a person reaching 65 last year was expected to live, on average, until 87.3). The insurance company wants to determine if their clients now have a longer average life expectancy, so they randomly sample some of their recently paid policies. The insurance company will only change their premium structure if there is evidence that people who buy their policies are living longer than before. The sample data is provided in the excel file. Answer the following questions. Results should be support by excel output.

a. Construct a 95% and 99% confidence intervals for the true average life expectancy. Use t-distribution and Descriptive Statistics function from Data Analysis. Interpret each Confidence interval and comment on the difference between the 95% and 99% interval.

b. Write the null and alternative hypotheses for this test:

c. In this context, describe a Type I error possible. How might such an error impact Life Insurance company’s decision regarding the premium structure?

d. What is the value of the t-test statistic?

e. What is the associated P-value?

f. State the conclusion using α = 0.05. Do it using both P-value and critical value.

Find regression line for the data

1. X 0   1   2   3    4   5   6   7  8               [3 MARKS]

Y 11 21 31 41 51 61 71 81 91

b. X  0   2   4   6   8  10                            [3 MARKS]      

Y  12 15 17 18 20 22

Explain the following theories of Equity:

1. entity theory

2. fund theory

3. commander theory

4. enterprise theory

Calculate the sample covariance and also calculate the sample mean and variance for the advertising and sales variables.

1. Insurance companies track life expectancy information to assist in determining the cost of life insurance policies. Life expectancy is a statistical measure of average time a person is expected to live, based on a number of demographic factors. Mathematically, life expectancy is the mean number of years of life remaining at a given age, assuming age-specific mortality rates remain at their most recently measured levels. Last year the average life expectancy of all the Life Insurance policyholders in Ontario at age 65 was 22.3 years (meaning that a person reaching 65 last year was expected to live, on average, until 87.3). The insurance company wants to determine if their clients now have a longer average life expectancy, so they randomly sample some of their recently paid policies. The insurance company will only change their premium structure if there is evidence that people who buy their policies are living longer than before. The sample data is provided in the excel file. Answer the following questions. Results should be support by excel output.

a. Construct a 95% and 99% confidence intervals for the true average life expectancy. Use t-distribution and Descriptive Statistics function from Data Analysis. Interpret each Confidence interval and comment on the difference between the 95% and 99% interval.

b. Write the null and alternative hypotheses for this test:

c. In this context, describe a Type I error possible. How might such an error impact Life Insurance company’s decision regarding the premium structure?

d. What is the value of the t-test statistic?

e. What is the associated P-value?

f. State the conclusion using α = 0.05. Do it using both P-value and critical value.

Please answer it on excel. Thank you.

PERIOD

0    1 2 3   4

EBIT   $46,000 $57,000 $70,000 $80,000

The above table illustrates earnings before interest and taxes for a capital investment project. Additional information for this project:

initial cost of the investment = $600,000

no change in net working capital

tax rate = 32.0% depreciation = accelerated using the MACRS factors: 0.33330, 0.44450, 0.1481, 0.0741

projected cash flow from salvage = $0

projected erosion costs = $30,000 in Year 1 and $40,000 in Year 2 If the opportunity cost of capital is 11.2%, what is the net present value of this project?

4. Recall the cookie problem from lecture. We have two bowls, Bowl 1 and Bowl 2. Bowl 1 contains 25% chocolate and 75% vanilla cookies; Bowl 2 has 50% of each. For this problem, assume each bowl is large enough that drawing a single cookie does not appreciably alter this ratio. Suppose we draw two cookies from the bowl and they are both chocolate. Calculate the posterior probabilities of the two bowls in two ways: (a) by treating the two cookies as one simultaneous piece of evidence (b) by updating the prior probabilities once using the rst chocolate cookie, and using the posterior probabilities as prior probabilities in a second update.

5. Suppose instead we draw two cookies; one is chocolate and the other is vanilla. Calculate the posterior probabilities. Does it matter which cookie we drew rst? Why or why not?

please answer this two question

Carney, Pierce, Menton, and Hoehn are partners who share profits and losses on a 4:3:2:1 basis, respectively. They are beginning to liquidate the business. At the start of this process, capital balances are

Which of the following statements is true?

Multiple Choice

• The first available $2,300 will go to Hoehn.

• Carney will be the last partner to receive any available cash.

• Carney will collect a portion of any available cash before Hoehn receives money.

• The first available $3,400 will go to Menton.

Define the following as regards currency:
1) devaluation;
2) revaluation;
3) depreciation;
4) appreciation;
5) soft or weak;
6) hard or strong;
7) Eurodollar;
8) euroyen.