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155S6.7_3 Assessing Normality
MAT 155 Statistical Analysis
March 04, 2011
Key Concept
Dr. Claude Moore
Cape Fear Community College
Chapter 6
Normal Probability Distributions
6­1 Review and Preview
6­2 The Standard Normal Distribution
6­3 Applications of Normal Distributions
6­4 Sampling Distributions and Estimators
6­5 The Central Limit Theorem
6­6 Normal as Approximation to Binomial
6­7 Assessing Normality
This section presents criteria for determining whether the requirement of a normal distribution is satisfied. The criteria involve visual inspection of a histogram to see if it is roughly bell shaped, identifying any outliers, and constructing a graph called a normal quantile plot.
Use Statdisk: Data, Normality Assessment. Use TI calculator:
Definition
A normal quantile plot (or normal probability plot) is a graph of points (x,y), where each x value is from the original set of sample data, and each y value is the corresponding z score that is a quantile value expected from the standard normal distribution. Procedure for Determining Whether It Is Reasonable to Assume that Sample Data are From a Normally Distributed Population
1. Histogram: Construct a histogram. Reject normality if the histogram departs dramatically from a bell shape. 2. Outliers: Identify outliers. Reject normality if there is more than one outlier present.
3. Normal Quantile Plot: If the histogram is basically symmetric and there is at most one outlier, use technology to generate a normal quantile plot. Not a Normal Distribution: The population distribution is not normal if either or both of these two conditions applies: • The points do not lie reasonably close to a straight line.
• The points show some systematic pattern that is not a straight­line pattern.
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155S6.7_3 Assessing Normality
Example
Normal: Histogram of IQ scores is close to being bell­shaped, suggests that the IQ scores are from a normal distribution. The normal quantile plot shows points that are reasonably close to a straight­line pattern. It is safe to assume that these IQ scores are from a normally distributed population.
Example
Skewed: Histogram of the amounts of rainfall in Boston for every Monday during one year. The shape of the histogram is skewed, not bell­shaped. The corresponding normal quantile plot shows points that are not at all close to a straight­line pattern. These rainfall amounts are not from a population having a normal distribution. March 04, 2011
Example
Uniform: Histogram of data having a uniform distribution. The corresponding normal quantile plot suggests that the points are not normally distributed because the points show a systematic pattern that is not a straight­line pattern. These sample values are not from a population having a normal distribution.
Manual Construction of a Normal Quantile Plot
Step 1. First sort the data by arranging the values in order from lowest to highest.
Step 2. With a sample of size n, each value represents a proportion of 1/n of the sample. Using the known sample size n, identify the areas of 1/2n, 3/2n, 5/2n, 7/2n, and so on. These are the cumulative areas to the left of the corresponding sample values.
Step 3. Use the standard normal distribution (Table A­2 or software or a calculator) to find the z scores corresponding to the cumulative left areas found in Step 2. (These are the z scores that are expected from a normally distributed sample.)
Step 4. Match the original sorted data values with their corresponding z scores found in Step 3, then plot the points (x, y), where each x is an original sample value and y is the corresponding z score.
Step 5. Examine the normal quantile plot and determine whether or not the distribution is normal.
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155S6.7_3 Assessing Normality
The Ryan­Joiner test is one of several formal tests of normality, each having their own advantages and disadvantages. STATDISK has a feature of Normality Assessment that displays a histogram, normal quantile plot, the number of potential outliers, and results from the Ryan­Joiner test. Information about the Ryan­Joiner test is readily available on the Internet.
March 04, 2011
Data Transformations
Many data sets have a distribution that is not normal, but we can transform the data so that the modified values have a normal distribution. One common transformation is to replace each value of x with log (x + 1). If the distribution of the log (x + 1) values is a normal distribution, the distribution of the x values is referred to as a lognormal distribution. Other Data Transformations
In addition to replacing each x value with the log (x + 1), there are other transformations, such as replacing each x value with , or 1/x, or x2. In addition to getting a required normal distribution when the original data values are not normally distributed, such transformations can be used to correct other deficiencies, such as a requirement (found in later chapters) that different data sets have the same variance.
Recap
In this section we have discussed:
• Normal quantile plot.
• Procedure to determine if data have a normal distribution.
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155S6.7_3 Assessing Normality
Interpreting Normal Quantile Plots. In Exercises 5–8, examine the normal quantile plot and determine whether it depicts sample data from a population with a normal distribution. 321/5. Old Faithful The normal 321/6. Heights of Women The quantile plot represents duration normal quantile plot represents times ( in seconds) of Old Faithful heights of women from Data Set 1 eruptions from Data Set 15 in in Appendix B.
Appendix B.
5. No, the data do not appear to come from a population with a normal distribution. The points are not reasonably close to a straight line.
6. Yes, the data appear to come from a population with a normal distribution. The points are
reasonably close to a straight line.
Determining Normality. In Exercises 9–12, refer to the indicated data set and determine whether the data have a normal distribution. Assume that this requirement is loose in the sense that the population distribution need not be exactly normal, but it must be a distribution that is roughly bell­shaped.
322/10. Astronaut Flights The numbers of flights by NASA astronauts, as listed in Data Set 10 in Appendix B. Normal Quantile Plot and Histogram were created with Statdisk.
10. No, the data do not appear to come from a population with a normal distribution. The
frequency distribution and the histogram indicate the data are positively skewed.
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Interpreting Normal Quantile Plots. In Exercises 5–8, examine the normal quantile plot and determine whether it depicts sample data from a population with a normal distribution. 322/7. Weights of Diet Coke The 322/8. Telephone Digits The normal quantile plot represents weights normal quantile plot represents the ( in pounds) of diet Coke from Data Set last two digits of telephone numbers 17 in Appendix B.
of survey subjects.
7. Yes, the data appear to come from a population with a normal distribution. The points are reasonably close to a straight line.
8. No, the data do not appear to come from a population with a normal distribution. The points
form an S­shaped pattern, and not a straight line.
Determining Normality. In Exercises 9–12, refer to the indicated data set and determine whether the data have a normal distribution. Assume that this requirement is loose in the sense that the population distribution need not be exactly normal, but it must be a distribution that is roughly bell­shaped.
322/12. Generator Voltage The measured voltage levels from a generator, as listed in Data Set 13 in Appendix B. Normal Quantile Plot and Histogram were created with Statdisk.
12. Yes, the data appear to come from a population with a normal distribution. The frequency
distribution and the histogram indicate the data is approximately bell­shaped.
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155S6.7_3 Assessing Normality
Constructing Normal Quantile Plots. In Exercises 19 and 20, use the given data values to identify the corresponding z scores that are used for a normal quantile plot. Then construct the normal quantile plot and determine whether the data appear to be from a population with a normal distribution. 323/19. Braking Distances A sample of braking distances (in feet) measured under standard conditions for an Acura RL, Acura TSX, Audi A6, BMW 525i, and Buick LaCrosse: 131, 136, 129, 127, and 146.
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323/19. Use TI to create table of cumulative areas and corresponding z­values. Use those plot original values (L1) and corresponding z­values (L2).
323/19. Use TI to graph normal quantile plot after entering original values in L1 as shown below.
19. The resulting normal quantile plot indicates that the data appear to come from a population
with a normal distribution.
Constructing Normal Quantile Plots. In Exercises 19 and 20, use the given data values to identify the corresponding z scores that are used for a normal quantile plot. Then construct the normal quantile plot and determine whether the data appear to be from a population with a normal distribution. 323/20. Satellites A sample of the numbers of satellites in orbit: 158 (United States); 17 (China); 18 (Russia); 15 (Japan); 3 (France); 5 (Germany).
323/20. Use TI to create table of cumulative areas and corresponding z­values. Use those plot original values (L1) and corresponding z­values (L2).
323/20. Use TI to graph normal quantile plot after entering original values in L1 as shown below.
20. The resulting normal quantile plot indicates that the data do not appear to come from a population with a normal distribution.
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