Below are the average heights for American boys in 1990. Age (years) Height (cm) birth 50.8 2 83.8 3 91.4 5 106.6 7 119.3 10 137.1 14 157.5 O Part (a) Decide which variable should be the independent variable and which should be the dependent variable. O Independent: age; Dependent: height O Independent: height; Dependent: age

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Please label each part

## Statistical Analysis of Height and Age

### Part (c)
**Question**: Does it appear from inspection that there is a relationship between the variables? Why or why not?

- **Options**:
  - Yes, it appears that height decreases as age increases.
  - Yes, it appears that height increases as age increases.
  - No, there is no visible relationship between the variables.

### Part (d)
**Objective**: Calculate the least squares line. Put the equation in the form of: \(\hat{y} = a + bx\). (Round your answers to three decimal places.)

- \(\hat{y} = \_\_\_\_\_\_\_\_\_\_\_\_ + \_\_\_\_\_\_\_\_\_\_\_\_ x\)

### Part (e)
**Task**: Find the correlation coefficient \( r \). (Round your answer to four decimal places.)

- \( r = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ \)

**Question**: Is it significant?
- **Options**:
  - Yes
  - No

### Part (f)
**Task**: Find the estimated average height for a one-year-old. (Use your equation from part (d). Round your answer to one decimal place.)

- \_\_\_\_\_\_\_\_\_\_\_\_ cm

**Task**: Find the estimated average height for an eleven-year-old. (Use your equation from part (d). Round your answer to two decimal places.)

- \_\_\_\_\_\_\_\_\_\_\_\_ cm

### Part (g)
**Question**: Does it appear that a line is the best way to fit the data? Why or why not?

- **Options**:
  - A line does appear to be the best way to fit the data because the data points follow a positive linear trend.
  - A line is not the best way to fit the data because it does not touch all the data points.
  - A line is the best way to fit the data because there is only one correct line that will fit a data set.
  - A line is the best way to fit the data because the slope of the line is positive and the linear correlation is positive. 

This exercise explores the relationship between age and height, using statistical tools such as least squares regression and the correlation coefficient to determine trends and predictions.
Transcribed Image Text:## Statistical Analysis of Height and Age ### Part (c) **Question**: Does it appear from inspection that there is a relationship between the variables? Why or why not? - **Options**: - Yes, it appears that height decreases as age increases. - Yes, it appears that height increases as age increases. - No, there is no visible relationship between the variables. ### Part (d) **Objective**: Calculate the least squares line. Put the equation in the form of: \(\hat{y} = a + bx\). (Round your answers to three decimal places.) - \(\hat{y} = \_\_\_\_\_\_\_\_\_\_\_\_ + \_\_\_\_\_\_\_\_\_\_\_\_ x\) ### Part (e) **Task**: Find the correlation coefficient \( r \). (Round your answer to four decimal places.) - \( r = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ \) **Question**: Is it significant? - **Options**: - Yes - No ### Part (f) **Task**: Find the estimated average height for a one-year-old. (Use your equation from part (d). Round your answer to one decimal place.) - \_\_\_\_\_\_\_\_\_\_\_\_ cm **Task**: Find the estimated average height for an eleven-year-old. (Use your equation from part (d). Round your answer to two decimal places.) - \_\_\_\_\_\_\_\_\_\_\_\_ cm ### Part (g) **Question**: Does it appear that a line is the best way to fit the data? Why or why not? - **Options**: - A line does appear to be the best way to fit the data because the data points follow a positive linear trend. - A line is not the best way to fit the data because it does not touch all the data points. - A line is the best way to fit the data because there is only one correct line that will fit a data set. - A line is the best way to fit the data because the slope of the line is positive and the linear correlation is positive. This exercise explores the relationship between age and height, using statistical tools such as least squares regression and the correlation coefficient to determine trends and predictions.
**Average Heights of American Boys in 1990**

The data below represents the average heights for American boys in 1990:

| Age (years) | Height (cm) |
|-------------|-------------|
| birth       | 50.8        |
| 2           | 83.8        |
| 3           | 91.4        |
| 5           | 106.6       |
| 7           | 119.3       |
| 10          | 137.1       |
| 14          | 157.5       |

**Part (a)**

**Decision on Variables:**
Decide which variable should be the independent variable and which should be the dependent variable.

- Independent: age; Dependent: height
- Independent: height; Dependent: age

**Part (b)**

**Scatter Plot:**
Draw a scatter plot of the data. Four possible plots are displayed:

1. Plot with x-axis as Height (cm) and y-axis as Age.
2. Plot with x-axis as Age and y-axis as Height (cm).
3. Plot with x-axis as Age and y-axis as Height (cm). Points form an increasing trend.
4. Plot with x-axis as Height (cm) and y-axis as Age. Points form a decreasing trend.

**Explanation of Graphs:**

1. The first graph displays the height (cm) on the x-axis and age on the y-axis, showing a linear increase.
2. The second graph shows the age on the x-axis and height (cm) on the y-axis, displaying a positive linear trend. 
3. The third graph is similar to the second, with age as the x-axis and height as the y-axis, depicting an upward trend.
4. The fourth graph presents height (cm) on the x-axis and age as the y-axis, showing a downward trend. 

These plots demonstrate the relationship between age and height, where height generally increases with age for American boys as recorded in 1990.
Transcribed Image Text:**Average Heights of American Boys in 1990** The data below represents the average heights for American boys in 1990: | Age (years) | Height (cm) | |-------------|-------------| | birth | 50.8 | | 2 | 83.8 | | 3 | 91.4 | | 5 | 106.6 | | 7 | 119.3 | | 10 | 137.1 | | 14 | 157.5 | **Part (a)** **Decision on Variables:** Decide which variable should be the independent variable and which should be the dependent variable. - Independent: age; Dependent: height - Independent: height; Dependent: age **Part (b)** **Scatter Plot:** Draw a scatter plot of the data. Four possible plots are displayed: 1. Plot with x-axis as Height (cm) and y-axis as Age. 2. Plot with x-axis as Age and y-axis as Height (cm). 3. Plot with x-axis as Age and y-axis as Height (cm). Points form an increasing trend. 4. Plot with x-axis as Height (cm) and y-axis as Age. Points form a decreasing trend. **Explanation of Graphs:** 1. The first graph displays the height (cm) on the x-axis and age on the y-axis, showing a linear increase. 2. The second graph shows the age on the x-axis and height (cm) on the y-axis, displaying a positive linear trend. 3. The third graph is similar to the second, with age as the x-axis and height as the y-axis, depicting an upward trend. 4. The fourth graph presents height (cm) on the x-axis and age as the y-axis, showing a downward trend. These plots demonstrate the relationship between age and height, where height generally increases with age for American boys as recorded in 1990.
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