Positive and Negative Relationships in Scatter Plots

Estimate the correlation coefficient.

• r is close to 1

• r is close to -1

• r is close to 0

• r is close to -0.5

• r is close to 0.5

Data is analyzed comparing hours studied and grades. It is determined that the correlation coefficient is 0.92. What does this mean?

• There is a strong positive correlation between studying and grades.

• There is a strong negative correlation between studying and grades.

• There is a weak positive correlation between studying and grades.

• There is a weak negative correlation between studying and grades.

Which of the following graphs do we use to represent bivariate data?

• Histogram

• Bar Graph

• Scatter Plot

• Pie Chart

The relationship and measure of a linear association that is shown by the two variables in a scatter plot is often called:

• Causation

• Correlation

• Outlier

• Can be called all of the above.

To describe the direction of an association we use:

• Positive/Negative

• Linear/Non-Linear

• Weak/Moderate/Strong

• Outliers

The scatter plot shows the relationship between the number of chapters and the total number of pages for several books.  Use the trend line to predict how many chapters would be in a book with 180 pages. 

• 12 chapters

• 15 chapters

• 18 chapters

• 21 chapters

Mrs. Collins made a scatterplot to show the relationship between the number of absences and a student’s final exam score. Based on this scatterplot, a student with 6 absences should get approximately what score on the final exam?

• 65

• 92

• 68

• 76

Which of the following could be a possible line of best fit for the data?

• y = -5/2x + 20

• y = -x + 19

• y = x + 20

• y = 2/3x + 15 

Which of the 6 scatter plots below has a correlation coefficient of -1.0?

• a.

• b.

• c.

• d.

• e.

This scatter plot shows a relationship between the age of a car and its value. Which best describes the relationship between the variables?

• Strong positive correlation

• Weak negative correlation

• Strong negative correlation

• No correlation