Graphical Analysis In Exercises 11–14, determine whether there is a perfect positive linear correlation, a strong positive linear correlation, a perfect negative linear correlation, a strong negative linear correlation, or no linear correlation between the variables.
Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 22m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
11. Correlation
Scatterplots & Intro to Correlation
Problem 9.1.14
Textbook Question
Graphical Analysis In Exercises 11–14, determine whether there is a perfect positive linear correlation, a strong positive linear correlation, a perfect negative linear correlation, a strong negative linear correlation, or no linear correlation between the variables.


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Step 1: Observe the scatterplot provided in the image. The points are closely aligned along a straight line that slopes upward from left to right.
Step 2: Recall the definition of correlation. A perfect positive linear correlation occurs when all data points lie exactly on a straight line with a positive slope, indicating a direct proportional relationship between the variables.
Step 3: Compare the alignment of the points in the scatterplot to the characteristics of a perfect positive linear correlation. Since the points are perfectly aligned along the upward-sloping line, this matches the definition of a perfect positive linear correlation.
Step 4: Note that there is no deviation of the points from the line, which confirms the relationship is perfect and positive.
Step 5: Conclude that the scatterplot demonstrates a perfect positive linear correlation between the variables.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Correlation
Correlation measures the strength and direction of a linear relationship between two variables. It is quantified by the correlation coefficient, which ranges from -1 to 1. A value of 1 indicates a perfect positive correlation, meaning as one variable increases, the other also increases proportionally. Conversely, a value of -1 indicates a perfect negative correlation, where one variable increases as the other decreases.
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Linear Relationship
A linear relationship between two variables means that the relationship can be represented by a straight line on a graph. This implies that changes in one variable result in proportional changes in the other. In the context of correlation, a strong linear relationship suggests that the data points closely follow a straight line, indicating predictability in the relationship between the variables.
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Scatterplot
A scatterplot is a graphical representation of two variables, where each point represents an observation. It helps visualize the relationship between the variables, making it easier to identify patterns, trends, or correlations. In the provided image, the points are closely aligned along an upward sloping line, indicating a perfect positive linear correlation, where increases in one variable correspond to increases in the other.
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