11. Correlation
Scatterplots & Intro to Correlation
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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
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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