Final answer:
Measuring phone time does not automatically ensure better efficiency and loyalty. Small companies might track calls for productivity, but the probability of exceeding an average requires additional data. Hypothesis testing can verify changes in phone usage averages among teenagers, with careful consideration of Type I error.
Step-by-step explanation:
Measuring time spent by employees on telephone contacts does not always guarantee better efficiency and stronger loyalty. Although tracking telephone interactions can provide insights into productivity and customer service efforts, there are several factors to consider for improving efficiency and loyalty. For instance, it's essential to balance quantitative measures, like the number of calls, with qualitative aspects, such as the purpose and outcome of the calls.
Small companies may monitor the number of long-distance phone calls to manage costs and evaluate productivity during peak times. If the average is 20 calls, the probability of exceeding this number depends on additional data, such as the variance or distribution of calls. Without this information, we cannot calculate the exact probability.
Regarding the time teenagers spend on the phone, an organization may compile data that suggests an increase in usage. To determine if the average time has genuinely increased, a hypothesis test is conducted using the null hypothesis (teenagers still spend 4.5 hours per week on average) and the alternative hypothesis (teenagers now spend more than 4.5 hours). Type I error refers to the chance of incorrectly rejecting the null hypothesis when it is actually true. The test results, including the calculated p-value, will determine whether the null hypothesis can be rejected.