Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality

Assessing Normality  Later on in this course, we will be using various statistical inference procedures to answer questions. These tests involve sampling individuals and recording data to gain insights about the populations from which they come (INFERENCE!!!!!).  When using the procedures, we usually have to assume the population has an approximately Normal distribution.  Today, we look at a strategy for assessing Normality.

Example, p. 122 By plotting the data, we can see that the distribution is roughly symmetric and unimodal

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Assessing Normality  Just because a distribution looks Normal, doesn’t mean we can say it is.  Rule can help to provide additional evidence for or against Normality  Normal Probability Plots can also be a useful tool when assessing Normality.

Example, p. 123 – Normal Probability Plot  How it’s made:  1. Arrange the observed data values form smallest to largest and record the percentile for each one.  2. Use the standard Normal distribution to find the z-scores at these same percentiles.  3. Plot each observation x against its expected z-score from Step 2.

Example, p. 123 – Normal Probability Plot

Interpreting a Normal Probability Plot If the points on a Normal probability plot lie close to a straight line, the plot indicates that the data are Normal. Systematic deviations from a straight line indicate a non-Normal distribution. Outliers appear as points that are far away from the overall pattern of the plot.

Example p. 124 – Guinea Pig Survival

Homework: Due Tuesday  P. 132 #65, 66, & 74