6. Bags of \(\pounds 1\) coins are paid into a bank. Each bag contains 20 coins.
The bank manager believes that \(5 \%\) of the \(\pounds 1\) coins paid into the bank are fakes. He decides to use the distribution \(X \sim \mathrm {~B} ( 20,0.05 )\) to model the random variable \(X\), the number of fake \(\pounds 1\) coins in each bag.
- State the assumptions necessary for the binomial distribution to be an appropriate model in this case.
The bank manager checks a random sample of 150 bags of \(\pounds 1\) coins and records the number of fake coins found in each bag. His results are summarised in Table 1.
\begin{table}[h]
| Number of fake coins in each bag | 0 | 1 | 2 | 3 | 4 or more |
| Observed frequency | 43 | 62 | 26 | 13 | 6 |
| Expected frequency | 53.8 | 56.6 | \(r\) | 8.9 | \(s\) |
\captionsetup{labelformat=empty}
\caption{Table 1}
\end{table} - Calculate the values of \(r\) and \(s\), giving your answers to 1 decimal place.
- Carry out a hypothesis test, at the \(5 \%\) significance level, to see if the data supports the bank manager's statistical model. State your hypotheses clearly.
Question 6 parts (d) and (e) are continued on page 24
The assistant manager thinks that a binomial distribution is a good model but suggests that the proportion of fake coins is higher than \(5 \%\). She calculates the actual proportion of fake coins in the sample and uses this value to carry out a new hypothesis test on the data. Her expected frequencies are shown in Table 2.
\begin{table}[h]
| Number of fake coins in each bag | 0 | 1 | 2 | 3 | 4 or more |
| Observed frequency | 43 | 62 | 26 | 13 | 6 |
| Expected frequency | 44.5 | 55.7 | 33.2 | 12.5 | 4.1 |
\captionsetup{labelformat=empty}
\caption{Table 2}
\end{table} - Explain why there are 2 degrees of freedom in this case.
- Given that she obtains a \(\chi ^ { 2 }\) test statistic of 2.67 , test the assistant manager's hypothesis that the binomial distribution is a good model for the number of fake coins in each bag. Use a \(5 \%\) level of significance and state your hypotheses clearly.