Presentation on theme: "Chapter 6: The Standard Deviation as a Ruler and the Normal Model"— Presentation transcript:
1Chapter 6: The Standard Deviation as a Ruler and the Normal Model
2The Standard Deviation as a Ruler Use standard deviation when comparing unlike measures.Standard deviation is the most common measure of spread.Remember standard deviation is the square root of the variance.
3Standardizing We standardize to eliminate units. A standardized value can be found by subtracting the mean from the value and dividing by the standard deviation.Has no unitsA z-score measures the distance of each data value from the mean in standard deviation.Negative z-score- data value below the meanPositive z-score- data value above the mean
4Benefits of Standardizing Standardized values are converted to the standard statistical unit of standard deviations from the mean. (z-score)Values that are measured on different scales or in different units can now be compared.
5Example Which performance is better? Bacher ran the 800-m in 129 seconds, which was 8 seconds faster than the mean of 137 seconds. How many standard deviations better than the mean is that? The standard deviation of all the qualifying runners was 5 seconds. So her time was ( )/5= -1.6 or 1.6 better than the mean. Prokhonova’s winning jump was 60m longer than the 6m jump. The standard deviation was 30cm, so the winning jump was (60/30)=2 standard deviations better than the mean.The long jump was better because it was a greater improvement over its mean than the winning 800m time, as measured in standard deviation.
6Shifting DataAdding or subtracting a constant amount to each value just adds or subtracts the same constant to:the mean and medianMaximum, minimum, and quartilesThe spread does not change because the distribution is simply shifting.The range, IQR, and the standard deviation remains the same.Recap: Adding a constant to every data value adds the same constant to measures of center and percentiles, but leaves measures of spread unchanged.
7Rescaling DataRescaling data is multiplying or dividing all values by the same number.Changes the measurement units.Ex. Inches to feet (multiply by 12)When we divide or multiply all the data values by any constant value, both measures of location (mean and median) and measures of spread (range, IQR and standard deviation) are divided or multiplied by that same value.
8ExampleSuppose the class took a 40 point quiz. The results show a mean score of 30. median of 32, IQR 8, SD 6, min 12 and Q (suppose YOU got a 35)What happens to each statisticI decide to weight the quiz as 50 points, and will add 50 points to each score you score is now a 45I decide to weight the score as 80 points and I double each score. Your score is now a 70I decide to count the quiz as 100 points; I’ll double each score and add 20 points . Your score is now a 90
9Table Statistic Originalx X+10 2x 2X+20 Mean 30 40 60 80 Median 32 42 6484IQR816SD612Minimum222444Q127375474Your score35457090
10What happenedMeasures of center and position are affected by addition and multiplicationMeasures of spread are only affected by multiplication
11Back to z-scoresStandardizing z-scores is shifting them by the mean and rescaling them by standard deviation.Standardizing:does not change the shape of the distribution of a variable.Changes the center by making the mean 0changes the spread by making standard deviation 1
12When is a z-score BIG?Normal models- appropriate for distributions whose shapes are unimodal and roughly symmetricparameter- a numerically value attribute of a modelex. The values of μ (mean) and σ (standard deviation) in N(μ,σ) model are parameters.summaries of data are called statisticsstandard Normal model (standard Normal distribution) - the Normal model with mean μ=0 and standard deviation σ=1
13The 68-95-99.7 Rule In a normal model: about 68% of the data fall within one standard deviation of the meanabout 95% of the data fall within two standard deviations of the meanabout 99.7% of the data fall within three standard deviations of the mean
14The First Three Rules for Working with Normal Models Make a picture.
15Working with the 68-95-99.7 Rule Step by Step SAT scores are designed to have an overall mean of 500 and standard deviation of 100.Where do u stand among other students if you earned a 600? (use the rule)Make a pictureModel the score with N(500,100)
16Continued (page 110 and 111)A score of 600 is one standard deviation away from the mean.About 32% (100%-68%) of those who took the test were more than one standard deviation away from the mean, but only half on the high side.About 16 % of the test scores were better than 600
17Finding Normal Percentiles by Hand The normal percentile corresponding to a z-score gives the percentage of values in a standard Normal distribution found at that z-score or below.Table of normal percentiles- used when a value doesn’t fall exactly 1, 2, or 3 from the convert data into z-score before using the tablelook down the left column of the table for the first two digits (of z)look across the top row for the third digitwhatever number connects the two is your percent
18Normal Percentiles Using Technology Normalcdf- finds the area between 2 z-scores2nd DISTR- normal cdf (zLeft,zRight)Example: find the area between z= -5 and z= 10.2nd DISTR- normal cdf ( -5, 10)when you want infinity as your cut point, use -99 or 99ex. What percentage of 1.8 above the 2nd DISTR- normal cdf (1.8, 99) = .0359
19From Percentiles to Scores: z in Reverse What z-score represents the first quartile in a normal model? (25th percentile)go to 2nd DISTR, invNormspecify the desired percentile invorm(.25) and ENTERthe cutpoint for the 25 % is z= -.674What z-score cuts off the highest 10% of a Normal model?Since we want the cut point for the highest 10%, we know that the 90% must be below the z-scoreinvNorm(.90) = 1.2810% of the area in a Normal model is more than 1.28 standard deviations from the mean
20Are You Normal? How Can You Tell? Draw a histogram- if the histogram is unimodal and symmetric, the Normal model is appropriate to useusually the easiest way to tell if the distribution is NormalNormal probability Plot- a display to asses whether a distribution of data is NormalNormal model is appropriate if plot is nearly straightdeviations from a straight line indicate that the distribution is not Normal
21What Can Go Wrong?Don’t use Normal models when the distribution is not unimodal and symmetric.Don’t use the mean and standard deviation when outliers are present.Both mean and standard deviation can be distorted by outliers
22Lets Try One! Page 102, # 19 a-cWhat percent of a standard Normal model is found in each region?a) z>1.5normal cdf ( 1.5,99) = 6.68%b) z< 2.25normal cdf (-99,2.25)= 98.78%c) -1 <z< 1.15normal cdf ( -1, 2.25)=71.6%
23Lets try another! Page 102,# 21 a-c In a Normal model, what values of z cut off the region described?a) highest 20%invNorm(.8)= .842b) highest 75%=invNorm (.25)= -.675c) the lowest 3%=invNorm( .03)=