1. Chapter 6 The Standard Deviation as a Ruler and the Normal Model Math2200

2. How do we compare measurements on different scales? SAT score 1500 versus ACT score 21 Women’s heptathlon –200-m runs, 800-m runs, 100-m high hurdles –Shot put, javelin, high jump, long jump –How to combine the scores to rank the athletes? Make them on the scale by standardization –Count how many standard deviations away from the mean

3. The Standard Deviation as a Ruler 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. 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.

4. Standardizing with z-scores (cont.) Standardized values (z-scores) have no units. z-scores measure the distance of each data value from the mean in standard deviations. 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.

5. Benefits of Standardizing Standardized values have been converted from their original units to the standard statistical unit of standard deviations from the mean. Thus, we can compare values that are measured on different scales, or with different units.

6. Heptathlon: Kluft versus Skujyte Carolina Kluft (Sweden) Gold Medal Austra Skujyte (Lithuania) Silver Medal

7. Kluft versus Skujyte Long jumpshot put mean6.16m13.29m sd0.23m1.24m Kluft6.78m14.77m z-score2.70 = (6.78-6.16)/0.231.19 = (14.77-13.29)/1.24 Skujyte6.30m16.40m z-score0.61 = (6.30-6.16)/0.232.51=(16.40-13.29)/1.24 Total z-score Kluft2.70+1.19=3.89 Skujyte0.61+2.51=3.12

8. Shifting Data Adding (or subtracting) a constant to every data value adds (or subtracts) the same constant to measures of position.

9. Shifting the data Mean +csdunchanged Median +cIQRunchanged Min +c Max +c Q1 +c Q3 +c - Adding (or subtracting) a constant to each value will increase (or decrease) measures of position: center, percentiles, max or min by the same constant. - Its shape and spread - range, IQR, standard deviation - remain unchanged.

10. Rescaling Data Multiply (or divide) all the data values by any constant The men’s weight data set measured weights in kilograms. If we want to think about these weights in pounds, we would rescale the data:

11. Rescaling Data (cont.) All measures of position (such as the mean, median, and percentiles) and measures of spread (such as the range, the IQR, and the standard deviation) are multiplied (or divided) by that same constant. Mean*asd*a Median*aIQR*a Min*a Max*a Q1*a Q3*a

12. How does the standardization changes the distribution? Standardizing data into z-scores shifts the data by subtracting the mean and rescales the values by dividing by their standard deviation. Shape –No change Mean –0 after standardization Standard deviation –1 after standardization

13. How should we read a z-score? The larger a z-score is (negative or positive), the more unusual it is. How do we evaluate how unusual it is?

14. Normal models Quantitative variable Unimodal + symmetric N(μ, σ) – μ : mean – σ: standard deviation –Standard normal N(0,1) –Z-scores always follow the standard normal distribution Parameters (μ, σ) Statistics (, ) If (μ, σ) are given, If (μ,σ) are not given,

15. Data, Model Data  Statistics Model  Parameter –A tool to describe the data with a number of parameters –Questions related to the data can be answered by the value of the parameters –Most of the time, parameters are unknown quantities and are estimated by certain statistics calculated from the data Mean from the model, sample mean SD from the model, sample standard deviation Proportion parameter, sample proportion

16. When to use the Normal model? When we use the Normal model, we are assuming the distribution is Normal. –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).

17. 68-95-99.7 rule How do we measure how extreme a value is using normal models? –68% within –95% within –99.7% within

18. Finding normal probability using TI-83 2 nd + VARS (DISTR) normalcdf(lowerbound, upperbound, μ, σ) –If the data follow the standard normal distribution, what is the chance to see a value between -0.5 and 1? normalcdf(-0.5,1,0, 1) = 0.5328072082 –If the data follow the N(1,1.5) distribution, what is the chance to see a value less or equal to 5? normalcdf(-1E99, 5,1,1.5) = 0.9961695749

19. From probability to scores For a given probability, how do we find the corresponding z-score or the original data value. Example: What is Q1 in a standard Normal model? TI-83: invNorm(probability, μ, σ) –invNorm(0.25,0,1) = -0.6744897495

20. Example: SAT score A college only admits people with SAT scores among the top 10%. Assume that SAT scores follow the N(500,100) distribution. If you want to be admitted, how high your SAT score needs to be? –invNorm(0.9,500,100) = 628.1551567

21. Finding the parameters While only 5% of babies have learned to walk by the age of 10 months, 75% are walking by 13 months of age. If the age at which babies develop the ability to walk can be described by a Normal model, find the parameters? –Z-score corresponding to 5%: (10- μ)/ σ = invNorm(0.05,0,1) = -1.644853626 –Z-score corresponding to 75%: (13- μ)/ σ = invNorm(0.75,0,1) = 0.6744897495 –Solve the two equations, we have μ=12.12756806 σ=1.293469536

22. 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. Nearly Normal data have a histogram and a Normal probability plot that look somewhat like this example: Normal Probability Plots

23. A skewed distribution might have a histogram and Normal probability plot like this: Normal Probability Plots (cont.)

24. How to make Normal Probability Plots? Suppose that we measured the fuel efficiency of a car 100 times The smallest has a z-score of -3.16 If the data are normally distributed, the model tells us that we should expect the smallest z-score in a batch of 100 is -2.58. –This calculation is beyond the scope of this class. X-axis is for the scores given by the normal model Y-axis is for that from the data Plot the point (-2.58,-3.16) Keep doing this for every value in the data set If the data are normally distributed, the two scores should be close, and graphically, all the points should be roughly on a diagonal line. TI-83 can make normal probability plots –The last icon in ‘Type’

25. What Can Go Wrong? Don’t use a Normal model when the distribution is not unimodal and symmetric.

26. What Can Go Wrong? (cont.) Don’t use the sample mean and sample standard deviation when outliers are present—the mean and standard deviation can both be distorted by outliers. Don’t round your results in the middle of a calculation.