2. When we carry out a 1-sample hypothesis test for the mean, one of the ways that we can check for normality is to look at the normal plot. How do we know if our normality assumption is satisfied by looking at the normal plot? Be as specific as possible.

IQ tests are approximately normally distributed with a mean of 100 for adults. A tech company CEO wants to conduct a hypothesis test to see if the average IQ of employees at high-tech firms in California is higher than average. What is Type I error in the context of this problem?

Select all true statements below (Multiple or none can be true). With a large, representative sample, the histogram of the sample data will follow the normal curve closely. If two lists of numbers have exactly the same average of 50 and SD of 10, then the percentage of entries between 40 and 60 must be exactly the same for both lists. Half of a list of numbers is always below its average. The median and average of a list of numbers are not necessarily close together.

A random sample of 10,000 bottles of cola was taken to see whether the mean weight was 16 fluid ounces, as marked on the container. The null hypothesis is that the population mean is 16 ounces. Use the read-out below to test the hypothesis that the colas do not have a population mean of 16 fluid ounces. You do not have to do any calculations; just interpret the given data clearly and thoroughly in the context of the problem. | One-Sample T: ounces Test of u = 16 vs = 16 Variable Mean StDev SE Mean 95 CI T P ounces 10000 15.9988 0.0985 0.0010 (15.9969, 16.0007) -1.21 0.225