Download presentation

Presentation is loading. Please wait.

1
**CHAPTER 3 Describing Relationships**

3.1 Scatterplots and Correlation

2
**Scatterplots and Correlation**

IDENTIFY explanatory and response variables in situations where one variable helps to explain or influences the other. MAKE a scatterplot to display the relationship between two quantitative variables. DESCRIBE the direction, form, and strength of a relationship displayed in a scatterplot and identify outliers in a scatterplot.

3
**Case of the missing picture**

Measure the height and hand span of each member of your group to the nearest centimeter. (Hand span is the maximum distance from the tip of the thumb to the tip of the pinkie finger on a person’s fully stretched –out hand). Send a representative to record the hand span and height on the board. Make a scatterplot with the data with hand span on the x axis and height on the y axis. What does the graph tell you about the relationship between hand span and height? Summarize your observations in a sentence or two. Use the handprint and the heights of the teachers in the math department to suggest the most likely suspect

4
**List of suspects with heights**

Name Height (cm) Ms Tom 178 Mr. Horstmann 170 Ms. Anders 158 Mr. Farrington Mrs. Lew 160 Mr. Cook 188 Mrs. Worley 152 Mrs. Radostits 161 Mr. Robinson 186

5
**Explanatory and Response Variables**

Most statistical studies examine data on more than one variable. In many of these settings, the two variables play different roles. A response variable measures an outcome of a study. An explanatory variable may help explain or influence changes in a response variable. Note: In many studies, the goal is to show that changes in one or more explanatory variables actually cause changes in a response variable. However, other explanatory-response relationships don’t involve direct causation.

6
**Check Your Understanding**

Tim wants to know if there is a relationship between height and weight. Kelly wants to know if she can predict a student’s weight from his or her height. Information about height is easier to obtain than information about weight! Problem: Identify the explanatory and response variables, if possible for Tim and Kelly. Solution: Tim is just interested in the relationship between the two variables, so there is no clear explanatory or response variable. Kelly is treating a student’s height as the explanatory variable and the student’s weight as the response variable.

7
**Displaying Relationships: Scatterplots**

A scatterplot shows the relationship between two quantitative variables measured on the same individuals. The values of one variable appear on the horizontal axis, and the values of the other variable appear on the vertical axis. Each individual in the data appears as a point on the graph. How to Make a Scatterplot Decide which variable should go on each axis. • Remember, the eXplanatory variable goes on the X-axis! Label and scale your axes. Plot individual data values.

8
**Describing Scatterplots**

To describe a scatterplot, follow the basic strategy of data analysis from Chapters 1 and 2. Look for patterns and important departures from those patterns. How to Examine a Scatterplot As in any graph of data, look for the overall pattern and for striking departures from that pattern. • You can describe the overall pattern of a scatterplot by the direction, form, and strength of the relationship. • An important kind of departure is an outlier, an individual value that falls outside the overall pattern of the relationship.

9
**Describing Scatterplots**

Two variables have a positive association when above-average values of one tend to accompany above-average values of the other and when below-average values also tend to occur together. Two variables have a negative association when above-average values of one tend to accompany below-average values of the other. Strength Describe the scatterplot. Direction There is a moderately strong, negative, curved relationship between the percent of students in a state who take the SAT and the mean SAT math score. Further, there are two distinct clusters of states and two possible outliers that fall outside the overall pattern. Form

10
**Example: Describing a scatterplot**

Direction: In general, it appears that teams that score more points per game have more wins and teams that score fewer points per game have fewer wins. We say that there is a positive association between points per game and wins. Form: There seems to be a linear pattern in the graph (that is, the overall pattern follows a straight line). Strength: Because the points do not vary much from the linear pattern, the relationship is fairly strong. There do not appear to be any values that depart from the linear pattern, so there are no outliers. Try Exercise 7

11
**Summary: In Today’s Lesson we learned to…**

IDENTIFY explanatory and response variables in situations where one variable helps to explain or influences the other. MAKE a scatterplot to display the relationship between two quantitative variables. DESCRIBE the direction, form, and strength of a relationship displayed in a scatterplot and identify outliers in a scatterplot.

12
**Read pages 142-149 Problems #1,5,7,11,13 pages 159-160 **

Assignment Read pages Problems #1,5,7,11,13 pages

13
Warm Up Write a few sentences in your notebook to answer the following questions: If the findings of an experiment were “statistically significant” what would that mean? What does it mean for two variables to have a positive association? What does it mean for two variables to have a negative association?

14
**Learning Objectives INTERPRET the correlation.**

UNDERSTAND the basic properties of correlation, including how the correlation is influenced by outliers USE technology to calculate correlation. EXPLAIN why association does not imply causation.

15
**“He says we have ruined his positive correlation between height and weight.”**

16
**Correlation vs. Association**

A numerical measure of the direction and strength of the relationship between two quantitative variables Describes the relationship between two categorical variables or Describes the relationship between a categorical and a quantitative variable

17
**Measuring Linear Association: Correlation**

A scatterplot displays the strength, direction, and form of the relationship between two quantitative variables. Linear relationships are important because a straight line is a simple pattern that is quite common. Unfortunately, our eyes are not good judges of how strong a linear relationship is. The correlation r measures the direction and strength of the linear relationship between two quantitative variables. r is always a number between -1 and 1 r > 0 indicates a positive association. r < 0 indicates a negative association. Values of r near 0 indicate a very weak linear relationship. The strength of the linear relationship increases as r moves away from 0 towards -1 or 1. The extreme values r = -1 and r = 1 occur only in the case of a perfect linear relationship.

18
**Measuring Linear Association: Correlation**

19
Correlation Guess the correlation

20
**Calculating Correlation**

The formula for r is a bit complex. It helps us to see what correlation is, but in practice, you should use your calculator or software to find r. How to Calculate the Correlation r Suppose that we have data on variables x and y for n individuals. The values for the first individual are x1 and y1, the values for the second individual are x2 and y2, and so on. The means and standard deviations of the two variables are x-bar and sx for the x-values and y-bar and sy for the y-values. The correlation r between x and y is:

21
**Example of how to calculate r using TI-84**

Calculate and interpret r

22
**Check Your Understanding**

Each member of a small statistics class ran a 40-yard sprint and then did a long jump (with a running start). The table shows the sprint time (in seconds) and the long-jump distance (in inches) Sprint Time (s) Long Jump Distance (in) 5.41 171 5.05 184 7.01 90 7.17 65 6.73 78 5.68 130 5.78 173 6.31 143 6.44 92 6.50 139 6.80 120 7.25 110 Calculate and interpret r

23
**Correlation will always be 1 or -1 between 2 points**

24
**What does a correlation of .70 tell us about the form?**

Correlation tells us nothing about the form of the association. Be sure to check your scatterplot for form

25
**How is correlation influenced by a single point?**

A single point can have a large effect on the correlation. Correlation is not a resistant measure

26
**Oval method for determining the effect on correlation from a single point**

27
**Facts About Correlation**

How correlation behaves is more important than the details of the formula. Here are some important facts about r. Correlation makes no distinction between explanatory and response variables. r does not change when we change the units of measurement of x, y, or both. The correlation r itself has no unit of measurement. Cautions: Correlation requires that both variables be quantitative. Correlation does not describe curved relationships between variables, no matter how strong the relationship is. Correlation is not resistant. r is strongly affected by a few outlying observations. Correlation is not a complete summary of two-variable data.

28
**Correlation does not imply causation**

In months with higher ice cream sales, there are also a higher number of drowning deaths. Does eating ice cream cause drowning?

29
**Scatterplots and Correlation**

INTERPRET the correlation. UNDERSTAND the basic properties of correlation, including how the correlation is influenced by outliers USE technology to calculate correlation. EXPLAIN why association does not imply causation.

30
**Read pages 150-157 Problems #14-18, 21 pages 161-162 **

Assignment Read pages Problems #14-18, 21 pages

31
**Ticket Out the Door With Your Partner**

Following the debut of the new SAT Writing test in March 2005, Dr. Les Perelman from the Massachusetts Institute of Technology stirred controversy by reporting, “It appeared to me that regardless of what a student wrote, the longer the essay, the higher the score.” He went on to say, “I have never found a quantifiable predictor in 25 years of grading that was anywhere as strong as this one. If you just graded them based on length without ever reading them, you’d be right over 90% of the time.” The table below shows the data that Dr. Perelman used to draw his conclusions. Carry out your own analysis of the data. Write a few sentences with numerical support to respond to Dr. Perelman’s claim. Be sure to include the r value and comment on the scatterplot for the data.

Similar presentations

© 2021 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google