The table shows the mean driving speed of drivers in a 55mph zone and the number of speeding tickets they've received in the past 10 years. Plot the data in a scatterplot with speed on the x-axis. What can you determine about the relationship between mean speed and the number of speeding tickets?
Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 2m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables3h 28m
- 6. Normal Distribution & Continuous Random Variables2h 21m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 37m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - ExcelBonus23m
- Introduction to Confidence Intervals22m
- Confidence Intervals for Population Mean1h 26m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - ExcelBonus28m
- Confidence Intervals for Population Means - ExcelBonus25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 20m
- 9. Hypothesis Testing for One Sample5h 15m
- Steps in Hypothesis Testing1h 13m
- Performing Hypothesis Tests: Means1h 1m
- Hypothesis Testing: Means - ExcelBonus42m
- Performing Hypothesis Tests: Proportions39m
- Hypothesis Testing: Proportions - ExcelBonus27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions29m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 35m
- Two Proportions1h 12m
- Two Proportions Hypothesis Test - ExcelBonus28m
- Two Means - Unknown, Unequal Variance1h 2m
- Two Means - Unknown Variances Hypothesis Test - ExcelBonus12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - ExcelBonus9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - ExcelBonus21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - ExcelBonus12m
- Two Variances and F Distribution29m
- Two Variances - Graphing CalculatorBonus15m
- 11. Correlation1h 24m
- 12. Regression3h 42m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - ExcelBonus8m
- Finding Residuals and Creating Residual Plots - ExcelBonus11m
- Inferences for Slope32m
- Enabling Data Analysis ToolpakBonus1m
- Regression Readout of the Data Analysis Toolpak - ExcelBonus21m
- Prediction Intervals13m
- Prediction Intervals - ExcelBonus19m
- Multiple Regression - ExcelBonus29m
- Quadratic Regression23m
- Quadratic Regression - ExcelBonus10m
- 13. Chi-Square Tests & Goodness of Fit2h 31m
- 14. ANOVA2h 32m
11. Correlation
Scatterplots & Intro to Correlation
Multiple Choice
Engineers are studying how cargo weight affects the flight duration of a delivery drone. The data below shows the cargo weight (pounds) and the corresponding flight time (minutes) for 12 test flights. Generate a scatterplot using a graphing calculator with cargo weight as the x-axis. Is there a correlation between cargo weight and flight duration.

A
Positive correlation
B
Negative correlation
C
Nonlinear correlation
D
No correlation
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Verified step by step guidance1
Step 1: Begin by organizing the data provided into two variables: cargo weight (x-axis) and flight duration (y-axis). The cargo weight values are [1, 7, 8, 4, 2, 3, 9, 6, 2, 6, 5, 10], and the flight duration values are [62, 45, 43, 53, 59, 56, 41, 48, 60, 47, 51, 38].
Step 2: Use a graphing calculator or software (e.g., Excel, Google Sheets, or statistical tools like R or Python) to create a scatterplot. Plot cargo weight on the x-axis and flight duration on the y-axis. Each pair of values (cargo weight, flight duration) will represent a point on the graph.
Step 3: Observe the pattern of the points on the scatterplot. Look for trends or relationships between the x-axis (cargo weight) and y-axis (flight duration). Specifically, check if the points show a positive slope, negative slope, nonlinear pattern, or no discernible pattern.
Step 4: Analyze the direction of the relationship. If flight duration decreases as cargo weight increases, this indicates a negative correlation. If flight duration increases as cargo weight increases, this indicates a positive correlation. If the points form a curve, it suggests a nonlinear correlation. If the points are scattered randomly, it suggests no correlation.
Step 5: Based on the scatterplot, determine the type of correlation. In this case, as cargo weight increases, flight duration appears to decrease, suggesting a negative correlation. Confirm this observation by visually inspecting the graph and considering the trend of the data points.
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