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jQuery / Prototype

Explain the benefits of using olap to analyze data for the example you’ve presented.

Introduction
Assume your organization sells cars and there are 5 models of cars. The company sells all over the United States and so the organization is broken down into regions of the country which include East, West and Central. Further, each region has multiple dealerships that sells cars, and each dealership has many salespeople. If you wanted to find total sales for the company or a region you could simply query the database. Because this data is made up of multiple facets (different models of cars, different regions, different dealerships, different sale people), if you want to delve further into analyzing the data, such as comparing dealership sales and within region by model, you would need to look at these different aspects of the data for comparison. Online Analytical Processing (OLAP) is a concept used just for this purpose. Instructions
Write a 2 page paper on the concept of OLAP. Summarize an example of a dataset that could be used for OLAP. Compare and contrast OLAP to database querying. Explain the benefits of using OLAP to analyze data for the example you’ve presented. Writing Requirements
· 2 pages (approx. 400 words per page), not including title page or references page
· Cite 2 different sources in paper
Tips for Success
Instead of just Googling for answers try using the Tiffin University Library databases and/or library guide for this course.

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jQuery / Prototype

explain the difference between python and C+ Why does round(5 / 2) return 2 inst

explain the difference between python and C+
Why does round(5 / 2) return 2 instead of 3? The issue here is that Python’s round method implements banker’s rounding, where all half values will be rounded to the closest even number.

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jQuery / Prototype

Learning Goal: I’m working on a jquery / prototype question and need an explanat

Learning Goal: I’m working on a jquery / prototype question and need an explanation and answer to help me learn.Question 1. Telecommunications companies providing cell-phone service are interested in customer retention. In particular, identifying customers who are about to churn (cancel their service) is potentially worth millions of dollars if the company can proactively address the reason that customer is considering cancellation and retain the customer. Data on past customers (some of whom churned and some who did not) has been collected.Import the data to JMP and set correct data types for each column.
Create a k-nearest neighbors classifier to predict whether a customer’s churn based on AccountWeeks, Contract Renewal, DataUsage, CustServCalls, DayMins, and Monthly Charge. Select K = 100, and set the random seed to 10. Don’t forget to save the script to the data table.
Answer the following questions:Consider k=19.What is the confusion matrix?
What is the misclassification error rate?
Compute and interpret the sensitivity .
Compute and interpret the specificity.
How many false positives and false negatives did the model commit?
What percentage of predicted churners were false positives?
What percentage of predicted non-churners were false negatives?
Consider now k=7.What is the confusion matrix?
What is the misclassification error rate?
In this problem, we are mainly interested in predicting well the customers who will churn, and not so much the ones that won’t. This means that, more than the classification error, we are interested in a measure that will reflect how good the model is at predicting the customers who churn. What measure is that?
Based on the measure you answered above, compare the models for k=19 and k=7. What model would you choose to predict churners and why?

Categories
jQuery / Prototype

Learning Goal: I’m working on a jquery / prototype question and need an explanat

Learning Goal: I’m working on a jquery / prototype question and need an explanation and answer to help me learn.Question 1. Suppose you are a sports agent negotiating a contract for Titus A., an athlete in the National Football League( NFL). An important aspect of any NFL contract is the amount of guaranteed money over the life of the contract. You have gathered data on 300 NFL athletes who have recently signed new contracts. Each observation (NFL athlete) includes values for:percentage of his team’s plays that the athlete is on the field (SnapPercent),
the number of awards an athlete has received recognizing on-field performance (Awards),
the number of games the athlete has missed due to injury (GamesMissed),
millions of dollars of guaranteed money in the athlete’s most recent contract (Money, dependent variable).
You have trained the following decision tree to predict the dependent variable: (attaching image below) (referhwtress)Is this a regression or a classification tree?
Titus’s variable values are: SnapPercent = 95, Awards = 6, and GamesMissed = 1. How much guaranteed money does the tree predict that a player with Titus’s profile should earn in his contract?
Assume Titus feels that he was denied an additional award in the past season due to some questionable voting by some sports media. If Titus had won this additional award, how much money would the tree predict for Titus versus the prediction in part (b)? Comment on the result.
According to the tree, what characteristics does an athlete must have to be offered the least amount of contract money.
Question 2. Refer to the file named Cellphone.xlsx for this question.
Make sure to read the contents of sheet “Description” for details of what the data is about.
Import the data to JMP and set correct data types for each column.
Create a decision tree classifier to predict the variable named Churn based on AccountWeeks, Contract Renewal, DataUsage, CustServCalls, DayMins, and Monthly Charge.
Create 3 splits.
Select Display Options > Show split prob; Show split count
Select Show Fit Details
Select Leaf Report
Don’t forget to save the script to the data table.
Inspect the output, and answer the following questions:
List and interpret the set of rules given by the tree that characterizes churners.
Is a customer who has an account for 40 weeks, renewed the contract recently, has a data usage of 5 Gb, made 6 calls to customer service, and the average number of daytime minutes per month is 350 predicted to churn or not? And with what probability?
Compute and interpret the accuracy of the model.
Compute and interpret the sensitivity.
Compute and interpret the specificity.
What percentage of predicted churners were false positives?
What percentage of predicted non-churners were false negatives?