Fraud.org was created by the National Consumers League (NCL) to combat the growing problem of telemarketing and Internet fraud by improving prevention and enforcement. NCL works to protect and promote social and economic justice for consumers and workers in the United States and abroad.

You work for the NCL as a statistical analyst. You are studying data on fraud. Part of your analysis involves testing the goodness-of-fit, testing for independence, comparing variances, and performing ANOVA.

EXERCISES
1. The table at the right shows an expected distribution of the ages of fraud victims. The table also shows the results of a survey of 1000 randomly selected fraud victims. Using \(\alpha=0.01\), perform a chi-square goodness-of-fit test. What can you conclude?

2. The contingency table below shows the results of a random sample of 2000 fraud victims classified by age and type of fraud. The frauds were committed using bogus sweepstakes or credit card offers.

(a) Calculate the expected frequency for each cell in the contingency table. Assume the variables age and type of fraud are independent.

(b) Can you conclude that the ages of the victims are related to the type of fraud? Use \(\alpha=0.01\).

Transcribed Image Text:

Expected Survey Age distribution results Under 18 0.66% 8 18-25 14.92% 148 26-35 22.09% 206 36-45 17.29% 171 46-55 17.05% 175 56-65 14.71% 153 Over 65 13.28% 139