Log on to the website:http://www.teradatauniversitynetwork.com.And browse/learn the content of the site, library! Prepare a list of all the materials you find therein. Also list out the 20 items that you think are the most interesting and beneficial to you and briefly explain why for each item in your list. Limit yourself to the Business Intelligence/Analytics content (there is a lot of content on the site other than BI/BA as you will notice).
· Be succinct and summarize what you have to say in order to limit yourself to about (2 pages double-spaced, 12 font size).
Also read:
- Assignment: Brief 6: Flashy Flashers Purpose To assess your ability to apply the concepts from readings to a case situation. Action Items Read the Flashy Flashers case at the end of Chapter 16. Case Background: You are to take on the role of Kathryn Marley. You are to address your brief to the president of the company, Johnny Bennett. Download the 5-step critical-thinking decision-making matrix. Complete the questions concerning Step 1: Identify the problem(s) and uncertainties based on the assigned problem. CompleteStep 2: Obtaining Information on the matrix.Specifically, you are required to do A manual MRP explosion for the sidelamps and headlamps for the next 6 weeks (beginning with the current week). Assume that it is now the start of week 1. Fill in the planned order releases form provided in Table 16.15. It should show the planned order releases for all items for the next 6 weeks. Worksheets on the manual MRP explosion and list the actions that planners should consider this week to (1) release new orders, (2) expedite scheduled changes, and (3) delay a scheduled receipt's due date. Complete Step 3: Make predictions about the future on the matrix. Complete Step 4: Make decisions by choosing among the alternatives on the matrix. Review the Sample Business Brief and thegrading rubric for the assignment. Write aone-page analysisaccording to the Business Brief Guidelines.Complete sentences must be used (bullets not acceptable). Your analysis must be written using a concise writing style. Your brief should incorporate all of following instructions: An opening paragraph briefly introducing the case situation. Note: The first half of opening paragraph is to provide a synopsis of the company. The second half of the opening paragraph is state the problem (From Step 1 of the matrix). Analysis header – The analytical section should be based on your personal assessment of the situation. Develop your analysis by addressing the following points. The information for this section of brief should be derived from Steps 2 and 3 of the matrix. Analysis of the data derived when completing Step 2. Identification of the good and bad points of MRP implementation at Flashy Flashers. Conclusion header – This section should include your opinion based on your analysis of the case information and situation outcome. Your recommendation should address the following question and provide only key relevant information and logical discussion in support of your opinion: How can the resource planning process at Flashy Flashers be improved upon? (From Step 4 of the matrix) APA citation(s) and reference(s). Check your writing style by using theGrammarlyapp you downloaded. (See instructions under Week 1 Preparation.) Correct your business brief as needed. Submit your business brief toturnitin.com. Read the Originality Report you receive from turnitin.com and make any modifications as needed to your business brief. This may include adding proper citations or better paraphrasing. Attach the appendices to your paper: Appendix A containing the data information from Step 2. Appendix B containing the completed decision-making matrix. Appendix C containing the grading rubric for the assignment.
- Discuss the difference between criminal intelligence and business intelligence and the difference between business intelligence and institutional espionage.
- MMIS 671: Fundamentals of Analytics and Business Intelligence Final Exam, Fall 2015
- Question: Q: Chapter Chapter 11 of Mertler and Vannata; answer exercises on pages 306 and 307: This exercise utilizes the SPSS data setprofile-e.sav, which can be downloaded from this Web site: www.Pvrczak.com/data Conduct a Forward: LR logistic regression analysis with the following variables: IV—age, educ, hrsl, sibs, rincom91, life2 (categorical) DV—satjob2 Note: The variable Iife2 is categorical such that dull = 1, routine/exciting = 2, and all other values are system missing. Develop a research question for the following scenario. Conduct a preliminary Linear Regression to identify outliers and evaluate multicollinearity among the five continuous variables . Complete the following: a. Using the Chi-Square table in Appendix B, identify the critical value atp< .001 for identifying outliers. Use Explore to determine if there are outliers. Which cases should be eliminated? b. Is multicollinearity a problem among the five continuous variables? Conduct Binary Logistic Regression using the Forward: LR method. IV—age, educ, hrsl, sibs, rincom91, life2 (categorical; last is the reference category) DV—satjob2 Note: Make sure that any outliers identified in Exercise 2a are removed from data before running the logistic regression. Also, designating life2 as a categorical covariate with the last category as the reference, essentially makes "routine/exciting" = 0 and "dull" = 1, so interpret the results accordingly. a. Which variables were entered into the model? b. To what degree does the model fit the data? Explain. c. Is the generated model significantly different from the constant-only model? d. How accurate is the model in predicting job satisfaction? e. What are the odds ratios for the model variables? Explain. Module 14 – Multi-level linear analyses: When do you use multi-level linear analyzes? Chapter 8 of Cronk (chapter below I wasn’t sure what was being asked) and answer all practice exercises; post your results here:
- Strayer CIS500 Week 2 Assignment 1 The New Frontier Data Analytics
- Big Data and Analytics
- The New Frontier: Data Analytics
- Business Intelligence and Data Warehouses