A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatte of her results and computer regression analysis of egg production versus fish length are given below. Note that Number of eggs is given in thousands (.e., "40" means 40,000 eggs). Egg production vs fish length 70 60 50 - 40 - 30 - 20 4.2 4.4 4.6 4.8 5.0 5.2 Fish length, cm. Predictor Coef SE Coef T P Constant 25.55 0.697 0.13 0.899 Calories 5.392 0.2409 2.61 0.027 S = 6.751 R-Sq = 83.5% R-Sq (adj) = 74.8% Use Scenario 3-8. Which of the following statements can be made on the basis of the computer output? 83.5% of the variation in egg production can be accounted for by the linear regression. O 83.5% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in egg production can be accounted for by the linear regression. Number of eggs (in thousands)
A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatte of her results and computer regression analysis of egg production versus fish length are given below. Note that Number of eggs is given in thousands (.e., "40" means 40,000 eggs). Egg production vs fish length 70 60 50 - 40 - 30 - 20 4.2 4.4 4.6 4.8 5.0 5.2 Fish length, cm. Predictor Coef SE Coef T P Constant 25.55 0.697 0.13 0.899 Calories 5.392 0.2409 2.61 0.027 S = 6.751 R-Sq = 83.5% R-Sq (adj) = 74.8% Use Scenario 3-8. Which of the following statements can be made on the basis of the computer output? 83.5% of the variation in egg production can be accounted for by the linear regression. O 83.5% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in egg production can be accounted for by the linear regression. Number of eggs (in thousands)
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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![A fisheries biologist studying whitefish in a Canadian lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatter plot of her results and computer regression analysis of egg production versus fish length are given below.
*Note that the number of eggs is given in thousands (i.e., "40" means 40,000 eggs).*
**Graph: Egg production vs fish length**
- **Y-Axis (Number of eggs in thousands):** Ranges from 20 to 70.
- **X-Axis (Fish length, cm):** Ranges from 4.2 to 5.2.
- The graph features a scatter plot showing a positive correlation between fish length and egg production.
- A line of best fit is included, indicating a trend where egg production increases as the fish length increases.
**Regression Analysis Output:**
| Predictor | Coef | SE Coef | T | P |
|-------------|-------|---------|------|------|
| Constant | 25.55 | 0.697 | 0.13 | 0.899|
| Length | 5.392 | 0.2409 | 2.61 | 0.027|
- **S** = 6.751
- **R-Sq** = 83.5%
- **R-Sq(adj)** = 74.8%
**Use Scenario 3-8. Which of the following statements can be made on the basis of the computer output?**
- [ ] 83.5% of the variation in egg production can be accounted for by the linear regression.
- [ ] 83.5% of the variation in fish length can be accounted for by the linear regression.
- [ ] 74.8% of the variation in fish length can be accounted for by the linear regression.
- [x] 74.8% of the variation in egg production can be accounted for by the linear regression.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fde9916fa-6016-44c8-956f-3529ab3ff55b%2F7b3f5f8e-06f5-4542-8db3-d6e29393bb0a%2F4y1zgmj_processed.jpeg&w=3840&q=75)
Transcribed Image Text:A fisheries biologist studying whitefish in a Canadian lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatter plot of her results and computer regression analysis of egg production versus fish length are given below.
*Note that the number of eggs is given in thousands (i.e., "40" means 40,000 eggs).*
**Graph: Egg production vs fish length**
- **Y-Axis (Number of eggs in thousands):** Ranges from 20 to 70.
- **X-Axis (Fish length, cm):** Ranges from 4.2 to 5.2.
- The graph features a scatter plot showing a positive correlation between fish length and egg production.
- A line of best fit is included, indicating a trend where egg production increases as the fish length increases.
**Regression Analysis Output:**
| Predictor | Coef | SE Coef | T | P |
|-------------|-------|---------|------|------|
| Constant | 25.55 | 0.697 | 0.13 | 0.899|
| Length | 5.392 | 0.2409 | 2.61 | 0.027|
- **S** = 6.751
- **R-Sq** = 83.5%
- **R-Sq(adj)** = 74.8%
**Use Scenario 3-8. Which of the following statements can be made on the basis of the computer output?**
- [ ] 83.5% of the variation in egg production can be accounted for by the linear regression.
- [ ] 83.5% of the variation in fish length can be accounted for by the linear regression.
- [ ] 74.8% of the variation in fish length can be accounted for by the linear regression.
- [x] 74.8% of the variation in egg production can be accounted for by the linear regression.
Transcribed Image Text:A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatter plot of her results and computer regression analysis of egg production versus fish length are given below.
Note that Number of eggs is given in thousands (i.e., “40” means 40,000 eggs).
**Graph: Egg production vs fish length**
- **X-axis:** Fish length, cm (ranging from 4.2 to 5.2)
- **Y-axis:** Number of eggs (in thousands) (ranging from 20 to 70)
- **Description:** The scatter plot shows a positive relationship between fish length and the number of eggs, with data points scattered around a line that appears to have a positive slope.
**Regression Analysis:**
| Predictor | Coef | SE Coef | T | P |
|-----------|-------|---------|-----|------|
| Constant | 25.55 | 0.697 | 0.13 | 0.899 |
| Calories | 5.392 | 0.2409 | 2.61 | 0.027 |
- **S = 6.751, R-Sq = 93.5%, R-Sq(adj) = 74.8%**
**Question:**
Use Scenario 3-8. What is the correlation coefficient (r)?
- ○ 0.835
- ○ 0.913
- ○ -0.835
- ○ -0.913
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