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

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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.

---
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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