This lesson covers sampling variability, focusing on estimating population means using random samples. It explains how to identify the longest and shortest times from a dataset, estimate the population mean, and use random digits for sampling. The lesson also demonstrates calculating sample means and understanding sampling variability through multiple samples, culminating in creating a dot plot for visualization.

What is the main focus of lesson 17?

• Exploring sampling variability

• Learning about geometric shapes

• Studying calculus principles

• Understanding algebraic expressions

What is the longest time spent at the gym according to the data?

• 92 minutes

• 100 minutes

• 83 minutes

• 78 minutes

How can you estimate the population mean without calculating all values?

• By asking the gym owner

• By guessing

• By using a random sample

• By using a calculator

What is the purpose of using a table of random digits?

• To organize data

• To find the median

• To select a random sample

• To calculate the exact mean

What should you do if a number from the random digit table is 800 or greater?

• Subtract 800 from it

• Add it to the sample

• Use it as is

• Ignore it and select the next number

How many values should you initially select from the population to form your sample?

• 10 values

• 5 values

• 20 values

• 15 values

What does sampling variability refer to?

• The accuracy of a single sample mean

• The difference between the mean and median

• The variation in sample means from different samples

• The consistency of sample means

What is the benefit of taking multiple samples in this lesson?

• To ensure accuracy

• To demonstrate sampling variability

• To find the exact population mean

• To reduce calculation time

What does a dot plot help illustrate in this lesson?

• The exact population mean

• The mode of the data

• The distribution of sample means

• The median of the data

What might a statistician expect when taking a random sample of size five?

• The sample mean may vary from the population mean

• The sample mean will be zero

• The sample mean will be exactly half of the population mean

• The sample mean will always equal the population mean