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 Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 25m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA1h 57m
11. Correlation
Scatterplots & Intro to Correlation
Problem 9.3.31
Textbook Question
"Old Vehicles In Exercises 31–34, use the figure shown at the left.

Scatter Plot Construct a scatter plot of the data. Show y and x on the graph."
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Step 1: Identify the variables for the scatter plot. Here, the independent variable (x-axis) is the Year, and the dependent variable (y-axis) is the Average age of vehicles in years.
Step 2: Create a coordinate system with the x-axis labeled as 'Year' and the y-axis labeled as 'Average age (in years)'. Choose an appropriate scale for each axis to accommodate the range of data values.
Step 3: Plot each data point on the graph by pairing each year (x) with its corresponding average age (y). For example, plot the point (2014, 11.4), then (2015, 11.5), and so on for all years up to 2021.
Step 4: After plotting all points, review the scatter plot to observe any trends or patterns, such as whether the average age of vehicles is increasing over the years.
Step 5: Optionally, you can connect the points with a line to better visualize the trend, but remember that a scatter plot primarily shows individual data points.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Scatter Plot
A scatter plot is a graphical representation that displays values for two variables as points on a coordinate plane. Each point corresponds to one observation, with the x-axis representing the independent variable (year) and the y-axis representing the dependent variable (average vehicle age). This helps visualize trends or relationships between variables.
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Independent and Dependent Variables
In this context, the year is the independent variable (x), which is controlled or selected, while the average age of vehicles is the dependent variable (y), which changes in response to the year. Understanding this distinction is crucial for correctly plotting and interpreting the data.
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Trend Analysis
Trend analysis involves examining data points over time to identify patterns or directions, such as increasing or decreasing trends. Here, plotting the average vehicle age over years helps determine if vehicles are generally getting older on U.S. roads, indicating changes in usage or longevity.
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