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In this activity, you will...

In this activity, you will use scatterplots to investigate the relationship between the governor’s salary and the average per capita income in the state. You will also analyze differences between scatterplots and other types of graphs. The following table gives the average per capita income and the governor’s salary in 2003 for each state in a group of nine northeastern states.

State Per Capita Income 2003 Governor’s Salary 2003

Connecticut 43,173&nb sp; 150,000

Maine & nbsp; 28,831 70,000

Massachusetts &nbs p; 39,815 135,000

New Hampshire 34,702 &n bsp; 100,690

New Jersey 40,427 &nbs p; 157,000

New York & nbsp; 36,574 179,000

Pennsylvania 31,998 142,142

Rhode Island 31,916&n bsp; 105,000

Vermont &n bsp; 30,740 127,456

1. Explain how you could use a bar graph to represent and interpret one or

more aspects of the data. Which aspects of the data would the bar graph help interpret?

2. Would a histogram help you interpret the data, or an aspect of the data? Explain.

3. Which type of graph would help you answer the question: Is there any relationship between per capita income in a state and the governor’s salary?

4. Create a scatterplot of the data using the two quantitative variables, “per capita income” and “governor’s salary.”

5. Which variable did you use on the x-axis? Why?

6. Which variable did you use on the y-axis? Why?

7. Explain what your graph shows about the relationship of the two variables.

8. To analyze this relationship further you will create a scatterplot using data on all 50 states. To do so, you will first create a two-column table that records the governor’s salary and the average per capita income for each state.

9. Create a scatterplot of this data set. You should change the scale of the horizontal (or x) and vertical (or y) axes so the data are easier to read. (To change the scale on an axis, point to the axis and either double-click or right click and select Format axis. Then on the Scale tab, enter the appropriate minimum and maximum values for the axis.) What is a reasonable minimum value of x to use? What about y?

10. Are there any trends or patterns to this data? Explain

11. Are there any data points that appear to be “away from” the rest of the data? If so, which one(s) and what makes them stand out?

State Per Capita Income 2003 Governor’s Salary 2003

Connecticut 43,173&nb sp; 150,000

Maine & nbsp; 28,831 70,000

Massachusetts &nbs p; 39,815 135,000

New Hampshire 34,702 &n bsp; 100,690

New Jersey 40,427 &nbs p; 157,000

New York & nbsp; 36,574 179,000

Pennsylvania 31,998 142,142

Rhode Island 31,916&n bsp; 105,000

Vermont &n bsp; 30,740 127,456

1. Explain how you could use a bar graph to represent and interpret one or

more aspects of the data. Which aspects of the data would the bar graph help interpret?

2. Would a histogram help you interpret the data, or an aspect of the data? Explain.

3. Which type of graph would help you answer the question: Is there any relationship between per capita income in a state and the governor’s salary?

4. Create a scatterplot of the data using the two quantitative variables, “per capita income” and “governor’s salary.”

5. Which variable did you use on the x-axis? Why?

6. Which variable did you use on the y-axis? Why?

7. Explain what your graph shows about the relationship of the two variables.

8. To analyze this relationship further you will create a scatterplot using data on all 50 states. To do so, you will first create a two-column table that records the governor’s salary and the average per capita income for each state.

9. Create a scatterplot of this data set. You should change the scale of the horizontal (or x) and vertical (or y) axes so the data are easier to read. (To change the scale on an axis, point to the axis and either double-click or right click and select Format axis. Then on the Scale tab, enter the appropriate minimum and maximum values for the axis.) What is a reasonable minimum value of x to use? What about y?

10. Are there any trends or patterns to this data? Explain

11. Are there any data points that appear to be “away from” the rest of the data? If so, which one(s) and what makes them stand out?

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