Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value usinga = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 232 158 357 15.4 483 153 530 14.9 264 15.7 What are the null and alternative hypotheses? OA Ho p=0 B. Ho p=0 H p#0 OD. Ho p#0 H: p=0 H, p<0 OC. Ho p=0 H p>0 Construct a scatterplot. Choose the correct graph below. OA OB. OC AY 17- 17- of
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value usinga = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 232 158 357 15.4 483 153 530 14.9 264 15.7 What are the null and alternative hypotheses? OA Ho p=0 B. Ho p=0 H p#0 OD. Ho p#0 H: p=0 H, p<0 OC. Ho p=0 H p>0 Construct a scatterplot. Choose the correct graph below. OA OB. OC AY 17- 17- of
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Question
6
![**Analysis of Lemon Imports and Crash Fatality Rates**
The dataset below presents annual data for lemon imports (in metric tons) and corresponding crash fatality rates per 100,000 population.
| Year | Lemon Imports (metric tons) | Crash Fatality Rate (per 100,000) |
|-------|-----------------------------|----------------------------------|
| 1 | 232 | 15.8 |
| 2 | 264 | 15.7 |
| 3 | 357 | 15.4 |
| 4 | 483 | 15.3 |
| 5 | 530 | 14.9 |
**Hypothesis Testing**
To understand the relationship between lemon imports and crash fatality rates, we set up the following hypotheses:
- **Null Hypothesis (H₀):** ρ = 0
- This suggests no correlation between lemon imports and crash fatality rates.
- **Alternative Hypothesis (H₁):** ρ ≠ 0
- This indicates a potential correlation between the variables.
**Scatter Plot & Analysis**
The correct scatter plot (option B) displays lemon imports on the x-axis (ranging from 0 to 600) and crash fatality rates on the y-axis (ranging from 14 to 17). The data points show a pattern that is useful for visual interpretation.
**Correlation Calculation**
Calculate the linear correlation coefficient, denoted as \( r \), to quantify the relationship. This value helps determine if there is statistically sufficient evidence to conclude a correlation based on a significance level of \( \alpha = 0.05 \).
**Conclusion Evaluation**
The results need to be scrutinized to ascertain whether a linear correlation suggests causation between lemon imports and crash fatality rates, keeping in mind the adage "correlation does not imply causation."](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fc117d69c-87e8-4335-ad73-fa80a21ecf45%2F986c1452-03ed-4f90-bdac-0f5664d48ef8%2F3rhrqxa_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Analysis of Lemon Imports and Crash Fatality Rates**
The dataset below presents annual data for lemon imports (in metric tons) and corresponding crash fatality rates per 100,000 population.
| Year | Lemon Imports (metric tons) | Crash Fatality Rate (per 100,000) |
|-------|-----------------------------|----------------------------------|
| 1 | 232 | 15.8 |
| 2 | 264 | 15.7 |
| 3 | 357 | 15.4 |
| 4 | 483 | 15.3 |
| 5 | 530 | 14.9 |
**Hypothesis Testing**
To understand the relationship between lemon imports and crash fatality rates, we set up the following hypotheses:
- **Null Hypothesis (H₀):** ρ = 0
- This suggests no correlation between lemon imports and crash fatality rates.
- **Alternative Hypothesis (H₁):** ρ ≠ 0
- This indicates a potential correlation between the variables.
**Scatter Plot & Analysis**
The correct scatter plot (option B) displays lemon imports on the x-axis (ranging from 0 to 600) and crash fatality rates on the y-axis (ranging from 14 to 17). The data points show a pattern that is useful for visual interpretation.
**Correlation Calculation**
Calculate the linear correlation coefficient, denoted as \( r \), to quantify the relationship. This value helps determine if there is statistically sufficient evidence to conclude a correlation based on a significance level of \( \alpha = 0.05 \).
**Conclusion Evaluation**
The results need to be scrutinized to ascertain whether a linear correlation suggests causation between lemon imports and crash fatality rates, keeping in mind the adage "correlation does not imply causation."
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