Chapter 6 The Standard Deviation as a Ruler

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Chapter 6 The Standard Deviation as a Ruler and the Normal Model Math 2200

Chapter 6 The Standard Deviation as a Ruler and the Normal Model Math 2200

How do we compare measurements on different scales? • SAT score 1500 versus ACT

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

The Standard Deviation as a Ruler • The standard deviation tells us how the

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.

Standardizing with z-scores (cont. ) • Standardized values (z-scores) have no units. • z-scores

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.

Benefits of Standardizing • Standardized values have been converted from their original units to

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.

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

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

Kluft versus Skujyte   Long jump shot put mean 6. 16 m 13. 29

Kluft versus Skujyte   Long jump shot put mean 6. 16 m 13. 29 m sd 0. 23 m 1. 24 m     Kluft 6. 78 m 14. 77 m z-score 2. 70 = (6. 78 -6. 16)/0. 23 1. 19 = (14. 77 -13. 29)/1. 24     Skujyte 6. 30 m 16. 40 m z-score 0. 61 = (6. 30 -6. 16)/0. 23 2. 51=(16. 40 -13. 29)/1. 24     Total z-score   Kluft 2. 70+1. 19=3. 89 Skujyte 0. 61+2. 51=3. 12  

Shifting Data • Adding (or subtracting) a constant to every data value adds (or

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

Shifting the data - Adding (or subtracting) a constant to each value will increase

Shifting the data - 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. Mean +c sd unchanged Median +c IQR unchanged Min +c     Max +c     Q 1 +c     Q 3 +c    

Rescaling Data • Multiply (or divide) all the data values by any constant •

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:

Rescaling Data (cont. ) • All measures of position (such as the mean, median,

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 Median Min Max *a *a sd IQR     *a *a     Q 1 Q 3 *a *a    

How does the standardization changes the distribution? • Standardizing data into z-scores shifts the

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

How should we read a z-score? • The larger a z-score is (negative or

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?

Normal models • Quantitative variable • Unimodal + symmetric • N(μ, σ) – –

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,

Data, Model • Data Statistics • Model Parameter – A tool to describe the

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

When to use the Normal model? • When we use the Normal model, we

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).

68 -95 -99. 7 rule • How do we measure how extreme a value

68 -95 -99. 7 rule • How do we measure how extreme a value is using normal models? – 68% within – 95% within – 99. 7% within

Finding normal probability using TI-83 • 2 nd + VARS (DISTR) • normalcdf(lowerbound, upperbound,

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?

From probability to scores • For a given probability, how do we find the

From probability to scores • For a given probability, how do we find the corresponding z-score or the original data value. • Example: What is Q 1 in a standard Normal model? • TI-83: inv. Norm(probability, μ, σ) – inv. Norm(0. 25, 0, 1) = -0. 6744897495

Example: SAT score • A college only admits people with SAT scores among the

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? – inv. Norm(0. 9, 500, 100) = 628. 1551567

Finding the parameters • While only 5% of babies have learned to walk by

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 = inv. Norm(0. 05, 0, 1) = -1. 644853626 – Z-score corresponding to 75%: • (13 - μ)/ σ = inv. Norm(0. 75, 0, 1) = 0. 6744897495 – Solve the two equations, we have • μ=12. 12756806 • σ=1. 293469536

Normal Probability Plots • If the distribution of the data is roughly Normal, the

Normal Probability Plots • 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 (cont. ) • A skewed distribution might have a histogram and

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

How to make Normal Probability Plots? • Suppose that we measured the fuel efficiency

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’

What Can Go Wrong? • Don’t use a Normal model when the distribution is

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 use the sample mean and sample

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.