Final answer:
To address the student's question, we evaluate waiting times for transportation and parking times at the garage, identifying quantitative, discrete data for car numbers, and calculate expected values using normal and binomial distributions for parking behavior.
Step-by-step explanation:
The Sky Train arrival times are uniformly distributed, so the optimal choice would be based on when Scott arrives relative to the train schedule due to consistent waiting times. For the parking garage, the time to find a parking space follows a normal distribution with a mean of five minutes and a standard deviation of two minutes.
Regarding exercise 70, being surprised if it took less than one minute to find a parking space would depend on our understanding of a normal distribution. Given that finding a parking space is normally distributed, anything beyond two standard deviations from the mean is considered unusual. Since one minute is two standard deviations away (mean 5 - 2 x SD 2 = 1), it would be unexpected but not impossible.
For the Try It Σ question about data types, the number of cars in a parking lot is quantitative data that is discrete, as it involves a countable number of items.
Concerning the De Anza parking garage situation, for exercise 8, calculating the expected number of cars parked crookedly would involve multiplying 22 cars by the proportion of 37.5%, which gives us 8.25 crookedly parked cars (option A). Exercise 9 deals with the probability of at least 10 of the 22 cars being parked crookedly, which can be computed using a binomial distribution with n=22 and p=0.375, yielding probabilities for the possible outcomes.
Finally, the SCV design team compiled information on vehicular commuting and traffic congestion by engaging stakeholders and examining personal commuting experiences.