Within a health care network I would give preference to a variable expense department. This is because a variable budget gives the financial manager an opportunity to evaluate departments’ productivity, efficiency and performance. Within a variable budget one can use this information to identify costs and revenues in order to determine a department’s financial operating performance. The positive result of using a variable expense department is the ability it gives to improve efficiency and productivity with minimal government regulation and control. Managing a variable expense department can result in the ability to adapt quickly. Along with knowing the health care organizations product line costs, and the competitive and profitable price of services. The negative side of a variable expense department is that it must be managed daily. A fixed expense department can be successful only if the expenses are established. Fixed expenses can be positive, because they appropriate funds for specific departments. I would consider healthcare reform to be the biggest challenge within healthcare financial planning and budgeting. Medicare and Medicaid reimbursement have the greatest affect on hospital budgeting. This creates a challenge for a health care operations manager, because Medicare and Medicaid reimbursement health care reform has led to lower revenues. It is a challenge for a health care operations manager to maintain a high quality service level while receiving lower revenues. This causes tough decisions to be made in financial planning and the health care system budget. This includes cutting department budgets and coming up with expense reduction plans. Strategic budgeting can be used to address this challenge. Health care operations managers need to establish targets within their budgets. The key is to develop a budget that is able to support the overall plan of the health care organization. A health care operations manager should look at each department and...

...expected value and variance of Y=5X-3 for each distribution? (Hint: Use the ‘Summary of the Laws of Expected Value and Variance’ slide in the lecture notes.)
Exercise 5.3. Now assume the manager receives a daily salary of $200 plus $85 per car sold. What is the expected value and standard deviation of her salary? (Hint: Use the ‘Summary of the Laws of Expected Value and Variance’ slide in the lecture notes.)
Exercise 5.4. Now assume the company managing the network gets penalised $900 for each interruption. Calculate the expected daily penalty. Calculate the standard deviation of the daily penalty. (Hint: Use the ‘Summary of the Laws of Expected Value and Variance’ slide in the lecture notes.)
Part C: Week 4
Exercise 5.7 (a,b,d).
Exercise 5.11 (a,b).
Exercise 5.12 (Expected return only).
Exercise 5.13 (a,b).
Exercise 5.14 (Expected return only).
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STAT11-111 Business Statistics
WEEK 3 and WEEK 4: WORKSHOPS
______________________________________________
This workshop is to be completed during Week 3 and Week 4 workshops and will depend on how quickly we get through the lecture material.
Part A: Week 3
Exercise 4.2, Exercise 4.4.
Exercise 4.5, Exercise 4.6, Exercise 4.7. Your tutor will discuss these 3 questions with you in the class.
Exercise 4.8, Exercise 4.9.
Exercise 4.10, Exercise 4.12, Exercise 4.14.
Exercise 4.15, Exercise 4.16, Exercise 4.18.
Part B: Week 3 or 4...

...Case on Mean-Variance Frontiers
1. Ignoring the risk-free asset, draw the frontier in mean-std space.
We solve the problem by Matlab:
clear; clc;
% input data
temp = xlsread('30_Industry_Portfolios');
ret = temp(:,2:end)/100; （this step is to get all the returns from the file）
rf = 0.01/100; (The risk free rate is rf =0.01%= 0.0001 per month.)
% compute moments
er = (mean(ret))'; (the (30.1)vector of returns on the 30 industries)
V = cov(ret); (the covariance matrix of the returns)
% draw the frontier
reqrets = 0.00:0.001:0.02;
for i = 1:length(reqrets)
[trash, rfront(i), varfront(i)] = frontierp(reqrets(i),V,er);
end
figure (1);
plot (varfront.^.5,rfront);
title ('Mean-Std Frontier');
ylabel('E[ret]'); xlabel('\sigma(ret)');
2. Now also consider the risk-free rate. Draw the eﬃcient frontier (do it on the same ﬁgure as 1).
% now add frontier with rf
for i = 1:length(reqrets)
[trash, rfront(i), varfront(i)] = rffrontierp(reqrets(i),V,er,+rf);
end
hold on;
plot(varfront.^.5,rfront);
hold off
3. Compute the tangency portfolio and plot it in the ﬁgure. Write the expected return and standard deviation of the portfolio.
clear;
% input data
temp = xlsread('30_Industry_Portfolios');
ret = temp(:,2:end)/100;
rf = 0.01/100;
% compute moments
er = (mean(ret))';
V = cov(ret);
>> one = ones(T,1); % unit vector
>> T = length(er)
T =
30
>> one = ones(T,1); % unit vector
>> A = one'*inv(V)*er;
>> C = one'*inv(V)*one
C =...

...Variance Analysis: Year 6
In this part of the report, we analyze the variances between our pro-forma statements we had projected and the actual results we received from the BPG game. Looking at the variances, it can be seen that most of the numbers compared are not too far apart from each other when comparing our actual numbers to the projected analysis. This is due to the fact our forecast was successful. It was however not 100% accurate in terms of predicting our future numbers.
Looking at the variances for the consolidated income statement for year 6, sales to customers only had a negative 1.3% variance. Since this variance was negative, it indicates that sales were a little less than what was projected, but still very close. For both Cost of Goods Sold and Value added tax a negative 5.8% and 18.9% variance can be seen. This shows that both the costs that go into making the product and the tax were slightly lower than expected. An explanation for this would be that our company only did overtime for one quarter in year 6 instead of the anticipated 3 quarters of overtime. Actual Gross Profit was slightly higher than expected with a positive variance of 2.1%. The resulting percentages are due to the fact that even though sales were slightly lower than what was estimated, the cost of producing them and value added tax were significantly lower....

...Assignment 7: Mean-Variance Portfolio Theory
-------------------------------------------------
Top of Form
1 .
Consider, as in Lecture 7.1, a portfolio of two risky assets, with expected returns rˉ1,rˉ2, variances σ21,σ22 and covariance σ1,2. No other assets are available. You have to allocate $1 mln of investment in the portfolio of the two assets in order to minimize total portfolio variance. What is the optimal amount of investment in asset 1 (in mln dollars)? Assume expected returns are positive.
radio button to select σ_22rˉ_2−σ_12rˉ_1σ_12+σ_22−2σ_1,2 as your response
σ22rˉ2−σ21rˉ1σ21+σ22−2σ1,2
radio button to select σ_12rˉ_1−σ_1,2rˉ_2σ_12+σ_22−2σ_1,2 as your response
σ21rˉ1−σ1,2rˉ2σ21+σ22−2σ1,2
radio button to select σ_22−σ_1,2σ_12+σ_22−2σ_1,2 as your response
σ22−σ1,2σ21+σ22−2σ1,2
radio button to select σ_22−σ_1,2σ_12+σ_22−σ_1,2 as your response
σ22−σ1,2σ21+σ22−σ1,2
Correct Answer
σ22−σ1,2σ21+σ22−2σ1,2
Instructor Explanation
Call a the proportion of total portfolio value invested in first stock. Then we have to minimize the total variance
f(a)=a2σ21+(1−a)2σ22+2a(1−a)σ1,2
First-order conditions imply
0=∂f∂a=2aσ21−2(1−a)σ22+(2−4a)σ1,2
Solving this for a yields a=σ22−σ1,2σ21+σ22−2σ1,2
Second order conditions also hold, which is trivial to check.
2 .
Similarly to in-class problem, consider 3 assets with expected returns 1, 2 and 3, respectively. Assume all...

...Mean-Variance Analysis
Mean-variance portfolio theory is based on the idea that the value of investment opportunities can be meaningfully measured in terms of mean return and variance of return. Markowitz called this approach to portfolio formation mean-variance analysis. Mean-variance analysis is based on the following assumptions:
1. All investors are risk averse; they prefer less risk to more for the same level of expected return.
2. Expected returns for all assets are known.
3. The variances and covariances of all asset returns are known.
4. Investors need only know the expected returns, variances, and covariances of returns to determine optimal portfolios. They can ignore skewness, kurtosis, and other attributes.
5. There are no transaction costs or taxes.
The Mean-Variance Approach
The mean-variance theory postulated that in determining a strategic asset allocation an investor should choose from among the efficient portfolios consistent with that investor’s risk tolerance amongst other constraints and objectives. Efficient portfolios make efficient use of risk by offering the maximum expected return for specific level of variance or standard deviation of return. Therefore, the asset returns are considered to be normally distributed. Efficient portfolios plot graphically on the efficient frontier, which is...

...Harry W. Markowitz, the father of “Modern Portfolio theory”, developed the mean-variance analysis, which focuses on creating portfolios of assets that minimizes the variance of returns i.e. risk, given a level of desired return, or maximizes the returns given a level of risk tolerance. This theory aids the process of portfolio construction by providing a quantitative take on it. It integrates the field of quantitative analysis with portfolio management. Meanvariance analysis has found wide applications both inside and outside financial economics. However it is based on certain assumptions which do not hold good in practice. Hence there have been certain revisions to it, so as to make it a more useful tool in portfolio management.
Mean Variance Analysis
Within the mean variance approach of Markowitz, the basic assumption is that risk is measured by variance, and the investment decision is based on the trade-off between higher mean and lower variance of the returns. The locus of optimal mean-variance combinations is called the efficient frontier, on which all rational investors would desire to be positioned. Asset returns are assumed to be (jointly) normally distributed random variables, and correlations between assets are also assumed to be fixed and constant forever. (Mz)
Other assumptions include: (Mz)
All investors aim to maximize economic utility (in...

...were done using variance, heritability, beak size traits, and variable precipitation. Wallace Island is the control and Darwin Island is my treatment. The figures 1, 2, 3, and 4 are summaries of the inputs used in the experiments.
Figure 1 Input Summary Figure 2 Input Summary
Figure 3 input summary Figure 4 input summary
Figure 1 Result: By increased the beak size to 24 mm, increased the variance of the beak trait to 2.0, and reduced the precipitation to 0 on the Darwin Island to get all 100% hard larger seeds, I expect the Darwin finch’s beak two times larger than the finch’s on beak as on the Wallace Island and should have higher survival rates. Based on the regression lines, the birds’ beak size on the Darwin Island were twice larger then the Wallace Island and were able to adapt to hard larger seeds and increased the population in the rate set the variance trait.
Figure 2 Result: I decreased the beak size to 10 mm and reduced the variance of the trait to 1 as well as increased the precipitation to .82 to have more small soft seeds available on Darwin Island. I expected the birds at Darwin Island to adapt the new environment and increased the population over time. The experiment showed result that the birds with smaller beak size survive at higher rate than the Wallace Island and also increased its population.
Figure 3 Result: In the 3rd experiment, I changed the beak...

...Sub: Finance Question:
Calculation of variance of portfolio.
Topic: Portfolio management
ClassOf1 provides expert guidance to College, Graduate, and High school students on homework and assignment problems in Math, Sciences, Finance, Marketing, Statistics, Economics, Engineering, and many other subjects.
Suppose there are three risky assets, A, B and C with the following expected returns, standard deviations of returns and correlation coefficients. E (rA)= 4% E (rB)=5% E (rC) =15% S.DEVA=5% S.DEVB=7% S.DEVC=10%
A, B=0.7 A, C=-0.2 B,C=0.3
QUESTION 1: Solving for the Global Minimum Variance Portfolio Consider a world where there are no risk free assets, and just these three risky assets. Suppose short sales are permitted. Solve for the weights and variance of the global minimum variance portfolio. If short sales are not permitted is the solution affected?
Solution:
Global Minimum Variance Portfolio is that set of portfolios that will provide the minimum level of risk for a given level of expected return. Given a world with just the three given risk assets we can use the Solver function in Excel to ascertain the weights and variance of the global minimum variance portfolio. We have to calculate the Variance Covariance matrix for the given set of assets. While variance is given by the square of Standard Deviation and is thus entered...