Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the the best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports Crash Fatality Rate 16 15.9 228 262 362 466 512 0 15.7 15.5 14.9 ..... Find the equation of the regression line.
Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the the best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports Crash Fatality Rate 16 15.9 228 262 362 466 512 0 15.7 15.5 14.9 ..... Find the equation of the regression line.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
11
![**Title: Regression Analysis of Lemon Imports and Crash Fatality Rates**
---
**Objective:**
Find the regression equation with lemon imports (in metric tons) as the predictor (x) variable to estimate the crash fatality rate (per 100,000 people). Determine the predicted fatality rate for a year with 475 metric tons of lemon imports and assess the prediction's validity.
**Data Provided:**
| Lemon Imports (Metric Tons) | Crash Fatality Rate (per 100,000 People) |
|-----------------------------|------------------------------------------|
| 228 | 16 |
| 262 | 15.9 |
| 362 | 15.7 |
| 466 | 15.5 |
| 512 | 14.9 |
**Task:**
Determine the equation of the regression line:
\[ \hat{y} = b_0 + b_1x \]
- **Calculate coefficients:**
- \( b_0 \): y-intercept (round to three decimal places)
- \( b_1 \): slope (round to four decimal places)
**Application:**
- Use the regression equation to find the predicted crash fatality rate for 475 metric tons of lemon imports.
- Evaluate whether this prediction is worthwhile based on the analysis.
---
By following these steps, you will understand how lemon imports potentially correlate with crash fatality rates and refine predictive models accordingly.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fc117d69c-87e8-4335-ad73-fa80a21ecf45%2Fd343c764-4a1c-49ae-bb85-9310f616ae00%2Fdh0hc9e_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Title: Regression Analysis of Lemon Imports and Crash Fatality Rates**
---
**Objective:**
Find the regression equation with lemon imports (in metric tons) as the predictor (x) variable to estimate the crash fatality rate (per 100,000 people). Determine the predicted fatality rate for a year with 475 metric tons of lemon imports and assess the prediction's validity.
**Data Provided:**
| Lemon Imports (Metric Tons) | Crash Fatality Rate (per 100,000 People) |
|-----------------------------|------------------------------------------|
| 228 | 16 |
| 262 | 15.9 |
| 362 | 15.7 |
| 466 | 15.5 |
| 512 | 14.9 |
**Task:**
Determine the equation of the regression line:
\[ \hat{y} = b_0 + b_1x \]
- **Calculate coefficients:**
- \( b_0 \): y-intercept (round to three decimal places)
- \( b_1 \): slope (round to four decimal places)
**Application:**
- Use the regression equation to find the predicted crash fatality rate for 475 metric tons of lemon imports.
- Evaluate whether this prediction is worthwhile based on the analysis.
---
By following these steps, you will understand how lemon imports potentially correlate with crash fatality rates and refine predictive models accordingly.
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