### Creating a Normal Quantile Plot Using Z ScoresUse the given data values (a sample of female arm circumferences in centimeters) to identify the corresponding z scores that are used for a normal quantile plot. Then identify the coordinates of each point in the normal quantile plot. Construct the normal quantile plot and determine whether the data appear to be from a population with a normal distribution.**Data Values:**- 41.3- 44.1- 32.2- 33.8- 38.8**Task:**List the z scores for the normal quantile plot.**Input Boxes:**- [ ]- [ ]- [ ]- [ ]- [ ]*(Round to two decimal places as needed. Use ascending order.)*### Instructions:1. **Identify the Corresponding Z Scores:** - Use statistical tables or software to determine the z scores corresponding to the given data values for female arm circumferences. 2. **Plot the Points:** - Once you have the z scores, plot the corresponding points in a normal quantile plot.3. **Determine Normality:** - Based on the plotted points, assess if the data follow a straight line pattern, which would indicate that they come from a population with a normal distribution.### Example:To illustrate, let's assume we have calculated the z scores for the given data values. If the data values are approximately linear in the plot, they likely follow a normal distribution.

Use the given data values (a sample of female arm circumferences in centimeters) to identify the corresponding z scores that are used for a normal quantile plot, then identify the coordinates of each point in the normal quantile plot. Construct the normal quantile plot, then determine whether the data appear to be from a population with a normal distribution. 41.3, 44.1, 32.2, 33.8, 38.8 D List the z scores for the normal quantile plot. (Round to two decimal places as needed. Use ascending order.)

Use the given data values (a sample of female arm circumferences in centimeters) to identify the corresponding z scores that are used for a normal quantile plot, then identify the coordinates of each point in the normal quantile plot. Construct the normal quantile plot, then determine whether the data appear to be from a population with a normal distribution. 41.3, 44.1, 32.2, 33.8, 38.8 D List the z scores for the normal quantile plot. (Round to two decimal places as needed. Use ascending order.)

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Concept explainers

Confidence Intervals

When we calculate intervals in probability and statistics, it can be simply termed as finding out a confidence interval. The confidence interval is usually calculated from the data set which is observed. The value of the parameter that needs to be calculated is present here only.

Question

### Creating a Normal Quantile Plot Using Z Scores

Use the given data values (a sample of female arm circumferences in centimeters) to identify the corresponding z scores that are used for a normal quantile plot. Then identify the coordinates of each point in the normal quantile plot. Construct the normal quantile plot and determine whether the data appear to be from a population with a normal distribution.

**Data Values:**
- 41.3
- 44.1
- 32.2
- 33.8
- 38.8

**Task:**
List the z scores for the normal quantile plot.

**Input Boxes:**
- [ ]
- [ ]
- [ ]
- [ ]
- [ ]

*(Round to two decimal places as needed. Use ascending order.)*

### Instructions:

1. **Identify the Corresponding Z Scores:**
- Use statistical tables or software to determine the z scores corresponding to the given data values for female arm circumferences.

2. **Plot the Points:**
- Once you have the z scores, plot the corresponding points in a normal quantile plot.

3. **Determine Normality:**
- Based on the plotted points, assess if the data follow a straight line pattern, which would indicate that they come from a population with a normal distribution.

### Example:

To illustrate, let's assume we have calculated the z scores for the given data values. If the data values are approximately linear in the plot, they likely follow a normal distribution.

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