1 Financial Condition of Banks. The file Banks.csv includes data on a sample of 20 banks. The “Financial Condition” column records the judgment of an expert on the financial condition of each bank. This outcome variable takes one of two possible values—weak or strong—according to the financial condition of the bank. The predictors are two ratios used in the financial analysis of banks: TotLns&Lses/Assets is the ratio of total loans and leases to total assets and TotExp/Assets is the ratio of total expenses to total assets. The target is to use the two ratios for classifying the financial condition of a new bank. Run a logistic regression model (on the entire dataset) that models the status of a bank as a function of the two financial measures provided. Specify the success class as weak (this is similar to creating a dummy that is 1 for financially weak banks and 0 otherwise), and use the default cutoff value of 0.5. 

a. Consider a new bank whose total loans and leases/assets ratio = 0.6 and total expenses/assets ratio = 0.11. From your logistic regression model, estimate the following four quantities for this bank (use R to do all the intermediate calculations; show your final answers to four decimal places): the logit, the odds, the probability of being financially weak, and the classification of the bank (use cutoff = 0.5). 

b. The cutoff value of 0.5 is used in conjunction with the probability of being financially weak. Compute the threshold that should be used if we want to make a classification based on the odds of being financially weak, and the threshold for the corresponding logit. 

c. When a bank that is in poor financial condition is misclassified as financially strong, the misclassification cost is much higher than when a financially strong bank is misclassified as weak. To minimize the expected cost of misclassification, should the cutoff value for classification (which is currently at 0.5) be increased or decreased?

2. Competitive Auctions on eBay.com. The file eBayAuctions.csv contains information on 1972 auctions transacted on eBay.com during May–June 2004. The goal is to use these data to build a model that will distinguish competitive auctions from noncompetitive ones. A competitive auction is defined as an auction with at least two bids placed on the item being auctioned. The data include variables that describe the item (auction category), the seller (his or her eBay rating), and the auction terms that the seller selected (auction duration, opening price, currency, day of week of auction close). In addition, we have the price at which the auction closed. The goal is to predict whether or not an auction of interest will be competitive. Data preprocessing. Create dummy variables for the categorical predictors. These include Category (18 categories), Currency (USD, GBP, Euro), EndDay (Monday–Sunday), and Duration (1, 3, 5, 7, or 10 days). 

a. Create pivot tables for the mean of the binary outcome (Competitive?) as a function of the various categorical variables (use the original variables, not the dummies). Use the information in the tables to reduce the number of dummies that will be used in the model. For example, categories that appear most similar with respect to the distribution of competitive auctions could be combined.

b. Split the data into training (60%) and validation (40%) datasets. Run a logistic model with all predictors with a cutoff of 0.5. c. If we want to predict at the start of an auction whether it will be competitive, we cannot use the information on the closing price. Run a logistic model with all predictors as above, excluding price. How does this model compare to the full model with respect to predictive accuracy? 

d. Interpret the meaning of the coefficient for closing price. Does closing price have a practical significance? Is it statistically significant for predicting competitiveness of auctions? (Use a 10% significance level.) 

e. Use stepwise selection (use function step() in the stats package or function stepAIC() in the MASS package) and an exhaustive search (use function glmulti() in package glmulti) to find the model with the best fit to the training data. Which predictors are used? 

f. Use stepwise selection and an exhaustive search to find the model with the lowest predictive error rate (use the validation data). Which predictors are used?

Using the guidelines provided in this week’s chapter (and other resources as needed), create a step-by-step IT security policy for handling user accounts/rights for a student who is leaving prematurely (drops, is expelled, and so on).

You will need to consider specialized student scenarios, such as a student who works as an assistant to a faculty member or as a lab assistant in a computer lab and may have access to resources most students do not.

Write your answer using a WORD document. Do your own work. Submit here. Note your Safe Assign score. Score must be less than 25 for full credit.

Required Videos:

VIDEO. System Admin Tools:

https://www.youtube.com/watch?v=i-HIXgjWd-E

 Week 5 assignment: Complete the following assignment in “one MS Word document“: 

Textbook:

Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support Dursun Delen 

Chapter 8

discussion question #1-4: (100 -120 words each question)

1. How does prescriptive analytics relate to descriptive and predictive analytics?

2. Explain the differences between static and dynamic models. How can one evolve into the other?

3. What is the difference between an optimistic approach and a pessimistic approach to decision making under assumed uncertainty?

4. Explain why solving problems under uncertainty sometimes involves assuming that the problem is to be solved under conditions of risk.

Exercise 4: – (1 page)

Investigate via a Web search how models and their solutions are used by the U.S. Department of Homeland Security in the “war against terrorism.” Also investigate how other governments or government agencies are using models in their missions.

Chapter 9 

Discussion question #1-5: (100 -120 words each question)

1. What is Big Data? Why is it important? Where does Big Data come from?

2. What do you think the future of Big Data will be? Will it lose its popularity to something else? If so, what will it be?

3. What is Big Data analytics? How does it differ from regular analytics?

4. What are the critical success factors for Big Data analytics?

5. What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

Exercise 3: (1 page) 

At teradatauniversitynetwork.com, go to the Sports Analytics page. Find applications of Big Data in sports. Summarize your findings.

Note: When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.

All work must be original (not copied from any source).

within 8hrs, with references, APA format, plagiarism check required

 The landscape is changing on the mobile front, just in September, Apple announced the iPhone 14 would only use the eSIM instead of a traditional SIM card. eSIMs are not new, but this is the first announcement that they are eliminating the SIM card to use eSIMs. Perform research on the internet to determine what the impact to the forensics profession with the industry transition to eSIMs. The paper should be 1-2 pages and use up to 5 sources.

General Paper Guidelines 

Use up to five sources for the assignment. You can use your textbook as a reference, but it will not count as one of your sources. Your sources should be reputable—do not rely on Websites not associated with professional organizations and institutions (blogs, etc.). Any items taken from the Web should be carefully evaluated to ensure that they are appropriate for a college-level assignment. You may want to consult the Collin College library’s guide or Use Collin’s Lexis Nexis database in your research if possible and cite the case, outcomes, etc. http://library.collin.edu/login?url=http://www.nexisuni.com (Links to an external site.)

If you have any questions regarding the validity of a source, don’t hesitate to get in touch with me. 

Your papers should incorporate concepts discussed in our classroom and within the textbook, modules, and other reading materials. 

Formatting Guidelines for Papers 

Papers must written in 12-point Times New Roman font on US Letter paper with proper citations and a bibliography. Use the American Psychological Association (APA) 7th edition style guide (https://apastyle.apa.org/style-grammar-guidelines/paper-format (Links to an external site.)) for citations, references, headings, and paper format. The bibliography will not count toward the page total. Papers should contain the following sections: 

1. Introduction—This is an introductory statement about your paper. What point are you making, or what are you hoping to discover? Provide the thesis or purpose of the paper here. 
2. Body—This is the main section of the paper. Make sure you thoroughly fulfill the requirements of the assignment. Do what you stated in the introduction and support your conclusions. 
3. Conclusion—This is the part of your paper where you give your final thoughts. Based on your analysis, what have you concluded about the points or questions raised in the introduction? 
4. Bibliography (does not count toward page total)—This contains your reference list, which must be formatted in APA 7th edition guidelines. — On a separate page.

PreviousNext