Frequent fliers of a certain airline fly a random number of miles each

Frequent fliers of a certain airline fly a random number of miles each year, having mean and standard deviation of 25,000 and 12,000 miles, respectively. If 30 such people are randomly chosen, ap- proximate the probability that the average of their mileages for this year will exceed 25,000; be between 23,000 and 27,000.


 

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What time series components appear?

The Research and Innovative Technology Administration’s Bureau of Transportation Statistics conducted a study to evaluate the impact of the September 11, 2001 terrorist attack on US transportation. The 2006 study report and the data can be found at http://goo.gl/w2lJPV. The goal of the study was stated as follows:The purpose of this study is to provide a greater understanding of the passenger travel behavior patterns of persons making long distance trips before and after 9/11.The report analyzes monthly passenger movement data between January 1990 and May 2004. Data on three monthly time series are given in file Sept11Travel.csv for this period: (1) Actual airline revenue passenger miles (Air), (2) rail passenger miles (Rail), and (3) vehicle miles traveled (Car).In order to assess the impact of September 11, BTS took the following approach: using data before September 11, they forecasted future data (under the assumption of no terrorist attack). Then, they compared the forecasted series with the actual data to assess the impact of the event. Our first step, therefore, is to split each of the time series into two parts: pre- and post September 11. We now concentrate only on the earlier time series. Plot the pre-event AIR time series. What time series components appear? The following figure shows a time plot of the seasonally adjusted pre-September-11 AIR series. Which of the following methods would be adequate for forecasting the series shown in the figure? Linear regression model seasonality Linear regression model with trend Linear regression model with trend and seasonality Specify a linear regression model for the AIR series that would produce a seasonally adjusted series similar to the one shown in the figure below, with multiplicative seasonality. What is the outcome variable? What are the predictors? Run the regression model from (3). Remember to use only pre-event data. What can we learn from the statistical insignificance of the coefficients for October and September? The actual value of AIR (air revenue passenger miles) in January 1990 was 35.153577 billion. What is the residual for this month, using the regression model? Report the residual in terms of air revenue passenger miles. Create an ACF (autocorrelation) plot of the regression residuals. What does the ACF plot tell us about the regression model’s forecasts? How can this information be used to improve the model? Fit linear regression models to Air, Rail, and to Auto with additive seasonality and an appropriate trend. For Air and Rail, fit a linear trend. For Rail, use a quadratic trend. Remember to use only pre-event data. Once the models are estimated, use them to forecast each of the three post-event series. For each series (Air, Rail, Auto), plot the complete pre-event and post-event actual series overlayed with the predicted series. What can be said about the effect of the September 11 terrorist attack on the three modes of transportation? Discuss the magnitude of the effect, its time span, and any other relevant aspects.


 

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Calculate the distance the burning surface will recede in 5 seconds.

Consider a solid propellant rocket engine with an end-burning configuration as shown in Fig. 9 .30a. The solid propellant is ammonium nitrate. The burning characteristics of this propellant when the initial grain temperature is 60°F are given by the following measured data: linear burning rate is 0.04 in/sat a combustion pressure of 500 /in2, and 0.058 in/s at a combustion pressure of 1000 lb/i.tt2. The rocket engine is operating at a combustion pressure of 1500 /in2• Calculate the distance the burning surface will recede in 5 seconds.


 

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Using a TNC example critically discuss how Liabilities of Foreignness (LoF) and Assets of Foreignness (AoF) have affected how it does business abroad

Using a TNC example critically discuss how Liabilities of Foreignness (LoF) and Assets of Foreignness (AoF) have affected how it does business abroad. Use the conceptualisations of LoF and AoF as proposed by Sethi and Judge (2009). Pick your Transnational Corporation (TNC) from the following lists:

a. UNCTAD’s World Investment Report (2014) list of The world’s top 100 non-financial TNCs, ranked by foreign assets (Annex Table 28)

b. UNCTAD’s World Investment Report (2014) list of The top 100 non-financial TNCs from developing and transition economies, ranked by foreign assets (Annex Table 29) available at: http://unctad.org/en/pages/DIAE/World%20Investment%20Report/Annex-Tables.aspx

c. The Global Fortune 500 (2014), available at http://fortune.com/fortune500/.

Please note that you may not use Ford as your chosen example.

Overall word limit: 2500 words maximum.

The word count should:

• Include all the text, including title, preface, introduction, in-text citations, quotations, footnotes and any other items not specifically excluded below.

• Exclude diagrams, tables (including tables/lists of contents and figures), equations, executive summary/abstract, acknowledgements, declaration, bibliography/list of references and appendices. However, it is not appropriate to use diagrams or tables merely as a way of circumventing the word limit. If a student uses a table or figure as a means of presenting his/her own words, then this is included in the word count.

MARKING GUIDELINES

Performance in the summative assessment for this module is judged against the following criteria:

• Relevance to question(s)

• Organisation, structure and presentation

• Depth of understanding

• Analysis and discussion

• Use of sources and referencing

• Overall conclusions


 

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