Reviewing your previous reports, several questions came to Elizabeth Burke’s mind. Use point and interval estimates to answer these questions.
1. What portion of customers rate the company with “top box” Survey responses (which is defined as scale levels 4 and 5) on quality, ease of use, price, and service in the 2014 Customer Survey worksheet? How do these proportions differ by geographic region?
2. What estimates, with reasonable assurance, can PLE give customers for response times to customer service calls?
3. Engineering has collected data on alternative process costs for building transmissions in the worksheet Transmission Costs. Can you determine whether one of the proposed processes is better than the current process?
4. What would be a confidence interval for an additional sample of mower test performance as in the worksheet Mower Test?
5. For the data in the worksheet Blade Weight, what is the sampling distribution of the mean, and the standard error of the mean? Is a normal distribution an appropriate assumption for the sampling distribution of the mean?
6. How many blade weights must be measured to find a 95% confidence interval for the mean blade weight with a sampling error of at most 0 2? What if the sampling error is specified as 0.1?

Business Analytics

Order Description
Having covered the CRISP-DM methodology at length at the university, you decide to apply it to this project.
You are free to make reasonable assumptions about possible data sources. However, keep in mind that some data may not be allowed to be used in the Netherlands. You need not know the precise laws in the Netherlands or elsewhere, just highlight your legal/ethical concerns if any arise.
(a) Elaborate on the Business Understanding: determine business objectives and possible ways to achieve them. Assess the situation, making assumptions where necessary, and determine data mining goals. [35%]
(b) DiscussthenextstagesofDataUnderstandingandDataPreparation.Howdoesyour plan of these stages look like? Think of additional data sources that might be useful for this problem. Be creative but realistic. Describe all data sources in terms of their expected properties (structured, unstructured, 4Vs). Comment on practical challenges that may arise from using these sources. [30%]
(c) What variable do you expect to use as target? What specific challenges your predictive analytics on detecting fraudulent claims might face using the past data? Why will you need to partition the data for predictive modelling? Will over-sampling be needed? [35%]

Business Analytics

Order Description
Having covered the CRISP-DM methodology at length at the university, you decide to apply it to this project.
You are free to make reasonable assumptions about possible data sources. However, keep in mind that some data may not be allowed to be used in the Netherlands. You need not know the precise laws in the Netherlands or elsewhere, just highlight your legal/ethical concerns if any arise.
(a) Elaborate on the Business Understanding: determine business objectives and possible ways to achieve them. Assess the situation, making assumptions where necessary, and determine data mining goals. [35%]
(b) DiscussthenextstagesofDataUnderstandingandDataPreparation.Howdoesyour plan of these stages look like? Think of additional data sources that might be useful for this problem. Be creative but realistic. Describe all data sources in terms of their expected properties (structured, unstructured, 4Vs). Comment on practical challenges that may arise from using these sources. [30%]
(c) What variable do you expect to use as target? What specific challenges your predictive analytics on detecting fraudulent claims might face using the past data? Why will you need to partition the data for predictive modelling? Will over-sampling be needed? [35%]