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 fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports. Is the prediction worthwhile? Find the equation of the regression line. a. Modifying Above y with caret equals (Round the y-intercept to three decimal places as needed. Round the slope to four decimal places as needed.) b. The best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imp
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 fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports. Is the prediction worthwhile? Find the equation of the regression line. a. Modifying Above y with caret equals (Round the y-intercept to three decimal places as needed. Round the slope to four decimal places as needed.) b. The best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imp
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
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 fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports. Is the prediction worthwhile?
Find the equation of the regression line.
a. Modifying Above y with caret equals
(Round the y-intercept to three decimal places as needed. Round the slope to four decimal places as needed.)
b. The best predicted crash fatality rate for a year in which there are
475 metric tons of lemon imports is
fatalities per 100,000 population.
(Round to one decimal place as needed.)
![---
### Lemon Imports vs. Crash Fatality Rate
The table below presents data that compares lemon imports with crash fatality rates over a series of measurements:
| **Lemon Imports** | 231 | 263 | 360 | 493 | 511 |
|-------------------------|-----|-----|-----|-----|-----|
| **Crash Fatality Rate** | 16 | 15.9| 15.6| 15.4| 14.9|
#### Explanation
- **Lemon Imports**: These numbers represent the quantity of lemon imports. The values show an increasing trend.
- **Crash Fatality Rate**: This represents the rate of fatalities in crashes. The values show a decreasing trend.
This table can be used to explore the concept of correlation versus causation in statistical analysis. It is a classic example that demonstrates how two variables may show a correlation, even when they are not causally related.
---](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F088c6bbf-0f7b-4fdc-adc9-13a5b0833a78%2F2fad6b68-7cee-47bc-9a53-33f53b16a57b%2Fkkcy88w_processed.png&w=3840&q=75)
Transcribed Image Text:---
### Lemon Imports vs. Crash Fatality Rate
The table below presents data that compares lemon imports with crash fatality rates over a series of measurements:
| **Lemon Imports** | 231 | 263 | 360 | 493 | 511 |
|-------------------------|-----|-----|-----|-----|-----|
| **Crash Fatality Rate** | 16 | 15.9| 15.6| 15.4| 14.9|
#### Explanation
- **Lemon Imports**: These numbers represent the quantity of lemon imports. The values show an increasing trend.
- **Crash Fatality Rate**: This represents the rate of fatalities in crashes. The values show a decreasing trend.
This table can be used to explore the concept of correlation versus causation in statistical analysis. It is a classic example that demonstrates how two variables may show a correlation, even when they are not causally related.
---
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