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NEW QUESTION: 1
A researcher has a sample of 400 observations from a population whose standard deviation is known to be
136. The mean of the sample is calculated to be 17.2. The null hypothesis is stated as Ho: mean = 4. The p-value under the alternative hypothesis H1: mean > 4 equals ________.
A. 3.92%
B. 5.2%
C. 1.96%
D. 2.6%
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The z-statistic under the null is calculated to be (17.2 - 4)/(136/(400

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.5)) = 1.94. The right-tailed probability of observing a z-statistic at least as big as 1.94 equals 1.0 - 0.9738 = 0.026 = 2.6%. This is the p-value of the right-tailed test in this sample.

NEW QUESTION: 2

A. Option C
B. Option A
C. Option D
D. Option B
Answer: A

NEW QUESTION: 3
A telecommunications company needs to predict customer churn (i.e. customers eho decide to switch a computer). The company has historic records of each customer, including monthly consumption patterns, calls to customer service, and whether the customer ultimately quit th eservice. All of this data is stored in Amazon S3. The company needs to know which customers are likely going to churn soon so that they can win back their loyalty.
What is the optimal approach to meet these requirements?
A. Use the Amazon Machine Learning service to build the binary classification model based on the dataset stored in Amazon S3. The model will be used regularly to predict churn attribute for existing customers
B. Use a Redshift cluster to COPY the data from Amazon S3. Create a user Define Function in Redshift that computers the likelihood of churn
C. Use AWS QuickSight to connect it to data stored in Amazon S3 to obtain the necessary business insight. Plot the churn trend graph to extrapolate churn likelihood for existing customer
D. Use EMR to run the Hive queries to build a profile of a churning customer. Apply the profile to existing customers to determine the likelihood of churn
Answer: A
Explanation:
Explanation
https://aws.amazon.com/blogs/machine-learning/predicting-customer-churn-with-amazon-machine-learning/