Step 1 :State the null and alternative hypotheses: \n\[\begin{array}{l}H_{0}: \mu=49.0 \text { words } \H_{1}: \mu>49.0 \text { words }\end{array}\]
Step 2 :Calculate the test statistic using the formula for the z-score: \((x̄ - μ) / (s / √n)\), where x̄ is the sample mean, μ is the population mean under the null hypothesis, s is the sample standard deviation, and n is the sample size. The test statistic is approximately \boxed{3.47}.
Step 3 :Calculate the P-value, which is the probability of observing a test statistic as extreme as the one calculated, assuming the null hypothesis is true. The P-value is approximately \boxed{0.00026}.
Step 4 :Compare the P-value to the significance level. If the P-value is less than the significance level, reject the null hypothesis. In this case, the P-value is less than the significance level of 0.10, so we reject the null hypothesis.
Step 5 :Interpret the results in the context of the original claim. Rejecting the null hypothesis suggests that the mean number of words per page is greater than 49.0. In the context of the original claim, this suggests that there are more than 70,000 defined words in the dictionary.