程序代写代做代考 python case study QBUS3830

QBUS3830
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Semester 2, 2018

Homework Task 2: Hypothesis Testing

1 Case study: Benford’s Law

1. Implement a Python function that performs Pearson’s χ2 test for multinomial data
and returns the test statistic and p-value.

2. The GDP dataset contains a list of countries ranked by their GDP in 2017 (in millions
of dollars), according to the International Monetary Fund (IMF). Make a basic table
to discuss how well the data conforms to Benford’s law for the first digit. Perform the
χ2 test and discuss the results.

3. The Fraud dataset contains three series. One is a real financial variable for a random
sample of companies listed in the New York Stock Exchange (NYSE). The other two
are the same series, but with random modifications of digits. Repeat the exercise
above for each series, and identify the two “fraudulent” series.

2 Case study: Verizon Repair Times

The Verizon dataset contains data from a court case that involved the American telecom-
munications company. Verizon is the primary local telephone company (incumbent local
exchange carrier, ILEC) for a large area of the Eastern United States. As such, it is re-
sponsible to provide repair services for the customers of other telephone companies know as
competing local exchange carriers (CLECs). Verizon is subject to fines if the repair times
for CLEC customers are worse than those for Verizon customers.

Assume a significance level of 1%.

1. Conduct a two-sample test based on large-sample theory and discuss the results.

2. Implement a Python function that conducts a permutation test based on the mean.

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3. Conduct the permutation test, plot the permutation distribution, and discuss the
results. Compare the permutation test to the large-sample test, and discuss which one
is more appropriate for this problem.

3 Rules

Do not use package versions of the χ2 and permutation tests. Do not look for similar code
in the internet. The code must be your own work.

4 Rubric

You will get the full marks if you follow the instructions, obtain the correct p-values, and
interpret the results correctly.

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