Chapter 6: The Standard Deviation as a Ruler and the Normal Model

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Chapter 6: The Standard Deviation as a Ruler and the Normal Model. AP Statistics. The Standard Deviation as a Ruler. The trick in comparing very different-looking values is to use standard deviations as our rulers.
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Chapter 6: The Standard Deviation as a Ruler and the Normal ModelAP StatisticsThe Standard Deviation as a Ruler
  • The trick in comparing very different-looking values is to use standard deviations as our rulers.
  • The standard deviation tells us how the whole collection of values varies, so it’s a natural ruler for comparing an individual to a group.
  • As the most common measure of variation, the standard deviation plays a crucial role in how we look at data.
  • Standardizing with z-scores
  • We compare individual data values to their mean, relative to their standard deviation using the following formula:
  • We call the resulting values standardized values, denoted as z. They can also be called z-scores.
  • Standardizing with z-scores (cont.)
  • Standardized values have no units.
  • z-scores measure the distance of each data value from the mean in standard deviations.
  • You might say a data entry is (value of z-score) standard deviations higher or lower than the mean.
  • A negative z-score tells us that the data value is below the mean, while a positive z-score tells us that the data value is above the mean.
  • Shifting Data
  • Shifting data:
  • Adding (or subtracting) a constant amount to each value just adds (or subtracts) the same constant to (from) the mean. This is true for the median and other measures of position too.
  • In general, adding a constant to every data value adds the same constant to measures of center and percentiles, but leaves measures of spread unchanged. WHY?
  • Shifting Data (cont.)
  • The following histograms show a shiftfrom men’s actual weights to kilograms above recommended weight:
  • Rescaling Data
  • Rescaling data:
  • When we divide or multiply all the data values by any constant value, all measures of position (such as the mean, median and percentiles) and measures of spread (such as the range, IQR, and standard deviation) are divided and multiplied by that same constant value.
  • Why are measures of spread now affected?
  • Rescaling Data (cont.)
  • The men’s weight data set measured weights in kilograms. If we want to think about these weights in pounds, we would rescalethe data:
  • ExampleSuppose the class took a 40-point quiz. Results show a mean score of 30, median 32, IQR 8, SD 6, min 12, and Q1 27. (Suppose YOU got a 35.) What happens to each of the statistics if…
  • I decide to weight the quiz as 50 points, and will add 10 points to every score. Your score is now 45.
  • I decide to weight the quiz as 80 points, and double each score. Your score is now 70.
  • I decide to count the quiz as 100 points; I’ll double each score and add 20 points. Your score is now 90.
  • Example (cont.)
  • Here is a chart to give you a visual of how the statistics are affected:
  • Back to z-scores
  • Standardizing data into z-scores shiftsthe data by subtracting the mean and rescalesthe values by dividing by their standard deviation.
  • Standardizing into z-scoresdoes not change the shapeof the distribution.
  • Standardizing into z-scoreschanges the centerby making the mean 0.
  • Standardizing into z-scoreschanges the spreadby making the standard deviation 1.
  • When is a z-score BIG?
  • A z-score gives us an indication of how unusual a value is because it tells us how far it is from the mean.
  • A data value that sits right at the mean, has a z-score equal to 0.
  • A z-score of 1 means the data value is 1 standard deviation above the mean.
  • A z-score of –1 means the data value is 1 standard deviation below the mean.
  • When is a z-score BIG? (cont.)
  • How far from 0 does a z-score have to be to be interesting or unusual?
  • There is no universal standard, but the larger a z-score is (negative or positive), the more unusual it is.
  • Remember that a negative z-score tells us that the data value is below the mean, while a positive z-score tells us that the data value is above the mean.
  • When is a z-score Big? (cont.)
  • While there is no universal standard for z-scores, there is a model that shows up over and over in Statistics.
  • This model is called the Normal model (You may have heard of “bell-shaped curves.”).
  • Normal models are appropriate for distributions whose shapes are unimodal and roughly symmetric.
  • These distributions provide a measure of how extreme a z-score is.
  • When is a z-score Big? (cont.)
  • There is a Normal model for every possible combination of mean and standard deviation.
  • We write N(μ,σ)to represent a Normal model with a mean of μand a standard deviation of σ.
  • We use Greek letters because this mean and standard deviation are not numerical summaries of the data. They are part of the model. They don’t come from the data. They are numbers that we choose to help specify the model.
  • Such numbers are called parametersof the model.
  • When is a z-score Big? (cont.)
  • Summaries of data, like the sample mean and standard deviation, are written with Latin letters. Such summaries of data are called statistics.
  • When we standardize Normal data, we still call the standardized value a z-score, and we write
  • When is a z-score Big? (cont.)
  • Once we have standardized, we need only one model:
  • The N(0,1) model is called the standard Normal model (or the standard Normal distribution).
  • Be careful—don’t use a Normal model for just any data set, since standardizing does not change the shape of the distribution.
  • When is a z-score Big? (cont.)
  • When we use the Normal model, we are assuming the distribution is Normal.
  • We cannot check this assumption in practice, so we check the following condition:
  • Nearly Normal Condition: The shape of the data’s distribution is unimodal and symmetric.
  • This condition can be checked with a histogram or a Normal probability plot (to be explained later).
  • Z-Score ExampleSuppose two competitors tie in each of the first eight events in a decathlon. In the ninth event, the high jump, one clears the bar 1 in. higher. Then in the 1500-meter run the other one runs 5 seconds faster. Who wins? It boils down to knowing whether it is harder to jump an inch higher or run 5 seconds faster.
  • We have to be able to compare two fundamentally different activities involving different units. Standard deviations! If we knew the mean performance (by world-class athletes) in each event, and the standard deviation, we could compute how far each performance was from the mean in SD units (called z-scores).
  • So consider the three athletes’ performances shown in a three-event competition. Note that each placed first, second, and third in an event. Who gets the gold medal? Who turned in the most remarkable performance of the competition?
  • Z-Score Example (cont.)Day 2The 68-95-99.7 Rule
  • Formerly known as the Empirical Rule
  • Normal models give us an idea of how extreme a value is by telling us how likely it is to find one that far from the mean.
  • We can find these numbers precisely, but until then we will use a simple rule that tells us a lot about the Normal model…
  • The 68-95-99.7 Rule (cont.)
  • It turns out that in a Normal model:
  • about 68% of the values fall within one standard deviation of the mean;
  • about 95% of the values fall within two standard deviations of the mean; and,
  • about 99.7% (almost all!) of the values fall within three standard deviations of the mean.
  • The 68-95-99.7 Rule (cont.)
  • The following shows what the 68-95-99.7 Rule tells us:
  • Section off the percentiles for each standard deviation.
  • The First Three Rules for Working with Normal Models
  • Make a picture.
  • And, when we have data, make a histogram to check the Nearly Normal Condition to make sure we can use the Normal model to model the distribution.
  • Sketching a Normal Model
  • It is bell-shaped and symmetric around the mean.
  • You only need to extend out 3 standard deviations-everything else is not worth sketching.
  • The place where the bell shape changes from curving downward to curving back up is the inflection point and it is exactly one standard deviation away form the mean.
  • Inflection PointExample: Sketch a Normal CurveSketch Normal models using the 68-95-99.7 Rule:
  • Birth weights of babies, N(7.6 lb, 1.3 lb)
  • ACT scores at a certain college, N(21.2, 4.4)
  • Finding Normal Percentiles by Hand
  • When a data value doesn’t fall exactly 1, 2, or 3 standard deviations from the mean, we can look it up in a table of Normal percentiles.
  • Table Z in Appendix D provides us with normal percentiles, but many calculators and statistics computer packages provide these as well.
  • Finding Normal Percentiles by Hand (cont.)
  • Table Z (Table A in AP formula packet) is the standard Normal table. We have to convert our data to z-scores before using the table.
  • The figure shows us how to find the area to the left when we have a z-score of 1.80:
  • Finding Normal Percentiles Using Graphing Calculator
  • 2nd DISTR 
  • Normalpdf( calculates the x-values for graphing a normal curve. You probably won’t us this very often.
  • Normalcdf( finds the proportion of area under the curve between two z-score cut points by specifying Normalcdf( Lower bound, Upper bound)
  • Sometimes the left and right z-scores will be given to you, as you would want to find the percentage between. However, that is not always the case…
  • Finding Normal Percentiles Using Graphing Calculator (cont.)
  • In the last example we found that 680 has a z-score of
  • 1.8, thus is 1.8 standard deviations away from the mean.
  • The z-score is 1.8, so that is the left cut point.
  • Theoretically the standard Normal model extends rightward forever, but you can’t tell the calculator to use infinity as the right cut point. It is suggested that you use 99 (or -99) when you want to use infinity as your cut point.
  • Normalcdf(1.8,99) = approx. 0.0359 or about 3.6%
  • CONTEXT: Thus, approximately 3.6% of SAT scores are higher than 680.
  • From Percentiles to Scores: z in Reverse
  • Sometimes we start with areas and need to find the corresponding z-score or even the original data value.
  • Example: What z-score represents the first quartile in a Normal model?
  • From Percentiles to Scores: z in Reverse (cont.)
  • Look in Table Z for an area of 0.2500.
  • The exact area is not there, but 0.2514 is pretty close.
  • This figure is associated with z = –0.67, so the first quartile is 0.67 standard deviations below the mean.
  • From Percentiles to Scores: z in Reverse Using Graphing Calculator
  • 2nd DISTR  invNorm(
  • Specify the desired percentile
  • invNorm(.25) = approximately -0.674
  • Thus the z-score is -0.674
  • 0.674 standard deviations below the mean
  • Be careful with percentiles: If you are asked what z-score cuts off the highest 10% of a Normal model remember that is the 90th percentile. So you would use invNorm(.90).
  • Day 3Are You Normal? How Can You Tell?
  • When you actually have your own data, you must check to see whether a Normal model is reasonable.
  • Looking at a histogram of the data is a good way to check that the underlying distribution is roughly unimodal and symmetric.
  • Are You Normal? How Can You Tell? (cont.)
  • A more specialized graphical display that can help you decide whether a Normal model is appropriate is the Normal probability plot.
  • If the distribution of the data is roughly Normal, the Normal probability plot approximates a diagonal straight line. Deviations from a straight line indicate that the distribution is not Normal.
  • Are You Normal? How Can You Tell? (cont.)
  • Nearly Normal data have a histogram and a Normal probability (fairly straight) plot that look somewhat like this example:
  • Are You Normal? How Can You Tell? (cont.)
  • A skewed distribution might have a histogram and Normal probability plot like this:
  • Creating a Normal Probability Plot:Boys agility test scores: 22, 17, 18, 29, 22, 23, 17, 21
  • Enter these into L1
  • Turn a STATPLOT on
  • Normal Probability chart is the last icon
  • Specify L1 and which axis you want it on (use Y to look like the example)
  • ZoomStat
  • What Can Go Wrong?
  • Don’t use a Normal model when the distribution is not unimodal and symmetric.
  • What Can Go Wrong? (cont.)
  • Don’t round off too soon.
  • Don’t round your results in the middle of a calculation.
  • Don’t worry about minor differences in results.
  • Recap
  • The story data can tell may be easier to understand after shifting or rescaling the data.
  • Shifting data by adding or subtracting the same amount from each value affects measures of center and position but not measures of spread.
  • Rescaling data by multiplying or dividing every value by a constant changes all the summary statistics—center, position, and spread.
  • Recap (cont.)
  • We’ve learned the power of standardizing data.
  • Standardizing uses the SD as a ruler to measure distance from the mean (z-scores).
  • With z-scores, we can compare values from different distributions or values based on different units.
  • z-scores can identify unusual or surprising values among data.
  • Recap (cont.)
  • We’ve learned that the 68-95-99.7 Rule can be a useful rule of thumb for understanding distributions:
  • For data that are unimodal and symmetric, about 68% fall within 1 SD of the mean, 95% fall within 2 SDs of the mean, and 99.7% fall within 3 SDs of the mean.
  • Recap (cont.)
  • We see the importance of Thinking about whether a method will work:
  • Normality Assumption: We sometimes work with Normal tables (Table A). These tables are based on the Normal model.
  • Data can’t be exactly Normal, so we check the Nearly Normal Condition by making a histogram (is it unimodal, symmetric and free of outliers?) or a normal probability plot (is it straight enough?).
  • Terminology
  • When writing in context, remember the new terminology:
  • Chapter 6 Assignments: pp. 129 – 133
  • Day 1: # 1, 3, 6, 8, 10 – 12, 15, 18
  • Day 2: # 25, 33, 34, 36
  • Day 3: # 32, 40, 44, 45, 48
  • Day 4: # 22, 37, 43
  • Reading: Chapter 7
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