6. Construct a normal probability plot for the following data. 0.77 1.20 3.00 1.62 2.81 2.48 1.74 0.47 3.09 1.31 1.87 0.96 0.81 1.43 1.51 0.32 1.18 1.89 1.20 3.37 2.10 0.59 1.35 0.90 1.95 2.20 0.52 0.81 4.75 2.05

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## Constructing a Normal Probability Plot

### Problem Statement:

Construct a normal probability plot for the following data set:

**Data:**
- 0.77, 1.20, 3.00, 1.62, 2.81, 2.48, 1.74, 0.47, 3.09, 1.31, 1.87, 0.96
- 0.81, 1.43, 1.51, 0.32, 1.18, 1.89, 1.20, 3.37, 2.10, 0.59, 1.35, 0.90
- 1.95, 2.20, 0.52, 0.81, 4.75, 2.05

### Explanation:

A normal probability plot is a graphical technique for assessing whether a data set follows a normal distribution. The data are plotted against a theoretical normal distribution in such a way that the points should form an approximate straight line. Deviations from this line indicate deviations from normality.

When constructing the plot:

1. **Order the Data:** Arrange the data in ascending order.
2. **Calculate the Quantiles:** Determine the expected value from the normal distribution for each data point considering its rank.
3. **Plot the Data:** Plot each ordered data value against its corresponding normal quantile.
4. **Interpret the Plot:** Analyze the linearity of the plotted points. If they fall along a straight line, the data approximately follows a normal distribution. Non-linearity can indicate skewness or kurtosis in the data.

This plot is an essential tool for identifying normality before performing data analysis that assumes a normal distribution.
Transcribed Image Text:## Constructing a Normal Probability Plot ### Problem Statement: Construct a normal probability plot for the following data set: **Data:** - 0.77, 1.20, 3.00, 1.62, 2.81, 2.48, 1.74, 0.47, 3.09, 1.31, 1.87, 0.96 - 0.81, 1.43, 1.51, 0.32, 1.18, 1.89, 1.20, 3.37, 2.10, 0.59, 1.35, 0.90 - 1.95, 2.20, 0.52, 0.81, 4.75, 2.05 ### Explanation: A normal probability plot is a graphical technique for assessing whether a data set follows a normal distribution. The data are plotted against a theoretical normal distribution in such a way that the points should form an approximate straight line. Deviations from this line indicate deviations from normality. When constructing the plot: 1. **Order the Data:** Arrange the data in ascending order. 2. **Calculate the Quantiles:** Determine the expected value from the normal distribution for each data point considering its rank. 3. **Plot the Data:** Plot each ordered data value against its corresponding normal quantile. 4. **Interpret the Plot:** Analyze the linearity of the plotted points. If they fall along a straight line, the data approximately follows a normal distribution. Non-linearity can indicate skewness or kurtosis in the data. This plot is an essential tool for identifying normality before performing data analysis that assumes a normal distribution.
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