Chapter 13 Resources

## Parent Guide

Chapter 13 builds on the statistical ideas of Chapter 2 and the probability ideas of Chapter 12 to show how to make predictions.

### Inferential statistics

Making predictions about a large group from a smaller group is called inferential statistics. One of the most familiar examples of inferential statistics is polling; it?s impossible to survey everyone, so only a sample is polled and the results are generalized to the whole group, or population. The validity of the generalization depends on how well the smaller sample represents the population. One way to get a representative sample is by using a random process.

After choosing a sample randomly, someone trying to describe the full population must describe the sample, using statistics studied in Chapter 2. These include measures of central tendency, such as mean and median, and measures of spread, such as standard deviation. They also include a new idea: the shape of the data?generally, the degree to which the data are symmetric to the mean.

The third step in making predictions about the population is to decide how confident you are that the sample statistics match the corresponding numbers in the population. Polling results are often reported as being ?accurate to within 4 points.? To make decisions about what it means to be ?accurate? and what interval is associated with that degree of accuracy, your student will think about what would happen if many random samples were taken.

### Bivariate statistics

Sometimes you want to make predictions about one characteristic of a population based on another characteristic. For example, you might want to predict the height of an oak tree based on its circumference. Here, students collect data about two characteristics and find ways of describing their correlation?either through a correlation coefficient or graphs.

### Summary problem

From the sample census data on age and income, what can you conclude about the population?

Age and Income

 Age (yr) Income (\$) 31 6000 43 0 48 35,000 5 0 41 60,000 37 65,970 21 20 50 3000 35 68,400 48 22,330 10 0 74 36,636 32 44,000 54 8500 54 27,000 42 50,125 16 600 45 31,300 38 34,500 77 18,000 46 63,058 2 0 67 65,568 33 50,592 54 80,000

 Age (yr) Income (\$) 16 0 14 0 44 106,000 8 0 56 36,868 42 20,000 39 6500 61 35,000 19 750 22 2400 40 17,000 27 10,090 22 15,000 38 72,000 39 51,000 30 0 36 1500 4 0 22 1561 37 52,523 23 6000 40 15,462 31 24,634 3 0 42 87,500

Questions you might ask in your role as student to your student:

• What is the population from which this sample was taken?
• What do you think the mean age of the population is?
• What?s a 95% confidence interval for that age?
• How confident can you be that the mean income of the population is \$19,000?
• How well do age and income correlate?