The Anti-Capitalistic Mentality

 

The Anti-Capitalistic Mentality
by Ludwig Von Mises
Publisher: Libertarian Press 1994
ISBN/ASIN: 0910884293
ISBN-13: 9780910884297
Number of pages: 97
Description:
Professor Mises searches for the roots and consequences of the common anti-capitalist bias. What makes so many people unhappy in the private property order? It is precisely the fact that it grants to everyone the opportunity to secure maximum income and obtain the most desirable position. In such a system, the failures need a scapegoat. People whose ambitions have not been fully satisfied and whose dreams are not fully realized blame the system. Frustrated intellectuals, writers, and literati become vocal foes of the system.


Download or read it online here:

Download link

 (600KB, PDF)

ΠΡΟΣΟΧΗ στα φροντιστήρια για το ΕΑΠ

http://deoeap.blogspot.com/2011/10/prosox.html

Αυτό περιλαμβάνει και το ΜΒΑ

MBA60: ADVANCED QUANTITATIVE METHODS FOR MANAGERS

 Assignment 4

2008/9

 

HELLENIC OPEN UNIVERSITY

MBA PROGRAMM

MBA60: ADVANCED QUANTITATIVE METHODS FOR MANAGERS

Advanced Quantitative Analysis – 4th Assignment

A real estate developer in northern Greece wants to predict heating oil consumption based on atmospheric temperature, the amount of insulation used and the type of house (detached house or otherwise). From a sample of 15 houses selected randomly (of the same more or less area), he collected the following data:

Dependent variable (Y)                                     = Monthly heating oil consumption in liters

X1: Temperature                                                                      = Mean atmospheric temperature (in C degrees)

X2:       Insulation                                                                                  =          Insulation used (in cubic meters)

D (dummy: Detached House)    =          1                     if it is a detached house

                                                                                                                                                            =          0                      otherwise

House	Y (liters)	X1 (C degrees)	X2 (cubic meters)	D (dummy)
1	1101.2	4.4	3	1
2	1455.2	-2.8	3	0
3	657.2	4.4	10	0
4	163.2	22.8	6	1
5	377.2	17.8	6	0
6	923.6	1.1	6	1
7	1466.8	-12.8	6	1
8	1202.4	-13.3	10	1
9	951.2	-5.0	10	0
10	485.6	17.2	3	1
11	125.6	18.3	10	0
12	814.0	5.0	6	1
13	1764.4	-6.1	3	0
14	1292.0	3.3	3	0
15	210.0	14.4	10	0

Assume that you want to build a regression model that explains the monthly heating oil consumption.

1. Use the least squares method to estimate the following model

Y = b₀ + b₁ X1 + b₂ X2 + b₃ D + U        

2. State the estimated regression equation.

3. Comment the significance of the regression coefficients.

4. Give the interpretation of the regression coefficients.

5. Determine the coefficient of determination and explain its meaning.

6. Perform a residual analysis (i.e. plot the residuals versus the Observation). Is there any evidence of a pattern in the residuals? Explain.

7. Using the above model estimate the predicted monthly heating oil consumption for a detached house with 8 cubic meters of insulation and an average atmospheric temperature of 5 C degrees.

8. What should be the monthly heating oil consumption, for a non detached house (with 8 cubic meters of insulation and an average temperature of 5 C degrees)?

9. Do you trust the above predictions? Explain.

10. If you were a journalist specializing in house construction, prepare a short article that explains how a house heating oil consumption is determined.

.

MBA60: ADVANCED QUANTITATIVE METHODS FOR MANAGERS

Assignment 3

2008/9

HELLENIC OPEN UNIVERSITY

MBA PROGRAMM

MBA60: ADVANCED QUANTITATIVE METHODS FOR MANAGERS

Advanced Quantitative Analysis – 3rd Assignment

A mail-order catalog business that sells personal computer supplies, software, and hardware maintains a centralized warehouse for the distribution of products ordered. Management is currently examining the process of distribution from the warehouse and is interesting in studying the factors that affect warehouse distribution costs. Data have been collected over the past 24 months indicating the warehouse distribution costs and the number of orders received. The results are as follows:

Month	Number of Orders (thousands)	Distribution Cost (thousands of €)
1	4.015	52.951
2	3.806	71.664
3	5.309	85.582
4	4.262	63.694
5	4.296	72.813
6	4.097	68.442
7	3.213	52.468
8	4.809	70.772
9	5.237	82.036
10	4.732	74.396
11	4.413	70.847
12	2.921	54.088
13	3.977	62.983
14	4.428	72.309
15	3.964	58.994
16	4.582	79.383
17	5.582	94.449
18	3.450	59.741
19	5.079	90.502
20	5.735	93.245
21	4.269	69.336
22	3.708	53.714
23	5.387	89.187
24	4.161	66.804

Assume that you want to develop a model to predict distribution cost based on the number of orders received.

1. Construct a scatter diagram.

2. Use the least squares method to estimate the regression coefficients b₀ and b₁.

3. State the regression equation.

4. Plot on the same graph, the scatter diagram and the regression line.

5. Give the interpretation of the regression coefficients b₀ and b₁.

6. Determine the coefficient of correlation and explain its meaning in this problem.

7. Determine the coefficient of determination and explain its meaning in this problem.

8. Determine the standard error of the estimate and explain its meaning in this problem.

9. Perform a residual analysis [i.e. plot the residuals versus time (month)]. Is there any evidence of a pattern in the residuals? Explain.

10. At the 0.01 level of significance, is there any evidence of a linear relationship between distribution cost and the number of orders?

11. Would it be appropriate to use the model to predict the monthly distribution cost for 10 thousands received orders? Explain.

12. Predict the average distribution cost for 6,000 received orders.

13. Set up a 95% confidence interval estimate of the average distribution cost for 6,000 orders.

14. Construct a ±2*Se band about the regression line and determine the percentage of the points of the scatter diagram that lie inside this band.

15. Set up a 95% confidence interval estimate of the population slope.

16. Explain how the results obtained above can help the company to budget distribution costs.

[Adapted from: Berenson, Levine and Krehbiel, Basic Business Statistics, Prentice Hall, 2004]

MBA60: ADVANCED QUANTITATIVE METHODS FOR MANAGERS

Assignment 2

2008/9

HELLENIC OPEN UNIVERSITY: MBA PROGRAM

MBA60: ADVANCED QUANTITATIVE METHODS FOR MANAGERS

Advanced Quantitative Analysis - 2nd Assignment

Exercise 1:

Α gasoline additive is being tested to see whether it increases mileage. Twenty-five cars are supplied with 5 litres of gasoline and are run until the gasoline is exhausted. Αt the completion of the experiment the average mileage for each car is computed. Calculations with the data of this one experiment gave a mean of = 18.5 km per litre and a standard deviation of s = 2.2 km per litre for the 25 cars. Long-term experience with cars of the same kind that were used before when no additive was employed indicates that, µ = 18.0 and σ = 2.0 km per litre.

Assuming that the additive had no effect on mileage, answer the following questions:

1.1 Determine the probability accuracy of as an estimate of µ. What is the actual accuracy? Is the sample estimate compatible with what was to be expected by theory?

1.2 Find how large an experiment should have been conducted if one wished to be certain with a probability of 0.95 that the estimate would not be in error by more than ½ km per litre.

1.3 Find a 95 percent confidence interval for µ. Does this interval actually contain µ?

1.4 Dropping the assumption that the additive had nο effect οn either the mean or variance, use Student's t variable to find a 95 percent confidence interval for µ.

Exercise 2:

Answer the following questions:

2.1. Does the accuracy of the sample depend on the size of the population or on the size of the sample?

2.2. If you double the sample size, how much more accurate will your sample become?

2.3. If you decide to accept the null hypothesis, can you be sure that it is really true?

2.4. If you decide to reject the null hypothesis, can you be sure that it is really false?

2.5. Why is it necessary to choose a test statistic, whose distribution is known if the null hypothesis is true?

2.6. How much you have to increase the sample size in order to reduce the confidence interval width by one-half?

Exercise 3:

The Chamber of Commerce and Industry in a recent publication suggests that Small and Medium Enterprises (SME) borrow mainly for working capital and not for investing in equipment. In a survey of 763 SME who recently took bank loans, 229 said that they use the loans for buying new machinery equipment, 435 for working capital, and only 99 for other purposes (letter of credits, etc).

3.1 Set up a 95% confidence interval estimate of the population proportion of SME who borrow for buying new machinery equipment.

3.2 Set up a 95% confidence interval estimate of the population proportion of SME who take loans for working capital.

3.3 Last year in a program financed by the European Union called “Financing SME”, 125 thousand applications for bank loans were approved. Set up a 95% confidence interval estimate of the population number of SME who applied for loans which will be used for working capital.

Exercise 4:

The director of manufacturing at a clothing factory needs to determine whether a new machine is producing a particular type of cloth according to the manufacturer’s specifications, which indicate that the cloth should have a mean breaking strength of at least 70 pounds and a standard deviation of 3.5 pounds. The director is concerned that if the mean breaking strength is actually less than 70 pounds, the company will face too many lawsuits. A sample of 49 pieces of cloth reveals a sample mean of 69.3 pounds.

4.1 State the null and alternative hypotheses.

4.2 At the 0.05 level of significance, is there evidence that the mean breaking strength is less than 70 pounds?

4.3 What will be your answer in 4.2 if you use a 0.01 level of significance?

4.4 What will be your answer in 4.2 if the standard deviation is 4.5 pounds?

(NOTE: Try 4.2, 4.3, 4.4 with sample mean = 69.1 pounds)

Exercise 5:

Feta cheese is placed in packages of nominal weight 500g. The actual weight delivered to a package is normally distributed about the set weight with a standard deviation of 6g.

Calculate the proportion of packages containing

5.1 Less than 485g

5.2 More than 540g

5.3 Between 490g and 520g

5.4 Between what limits of weight lies the middle 95%.