Creamy: 56 44 62 36 39 53 50 65 45 40 56 68 41 30 40 50 56 30 22

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# Creamy: 56 44 62 36 39 53 50 65 45 40 56 68 41 30 40 50 56 30 22 - PowerPoint PPT Presentation

An article on peanut butter reported the following scores (quality ratings on a scale of 0 to 100) for various brands. Construct a comparative stem-and-leaf plot and compare the graphs. Creamy: 56 44 62 36 39 53 50 65 45 40 56 68 41 30 40 50 56 30 22

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An article on peanut butter reported the following scores (quality ratings on a scale of 0 to 100) for various brands. Construct a comparative stem-and-leaf plot and compare the graphs.

• Creamy: 56 44 62 36 39 53 50 65 45 40 56 68 41 30 40 50 56 30 22
• Crunchy: 62 53 75 42 47 40 34 62 52 50 34 42 36 75 80 47 56 62

Creamy: 56 44 62 36 39 53 50 65 45 40 56 68 41 30 40 50 56 30 22 Creamy: 56 44 62 36 39 53 50 65 45 40 56 68 41 30 40 50 56 30 22

• Crunchy: 62 53 75 42 47 40 34 62 52 50 34 42 36 75 80 47 56 62

Center:The center of the creamy is roughly 45 whereas the center for crunchy is higher at 51.

Shape: Both are unimodal but crunchy is skewed to the right while creamy is more symmetric.

Spread: The range for creamy and crunchy are equal at. There doesn’t seem to be any gaps in the distribution.

### Variation

Standard Deviation
• It’s a measure of the typical or average deviation (difference) from the mean.
Variance
• This is the average of the squared distance from the mean.
Does the Average Help?
• Paint A: Avg = 210/6 = 35 months
• Paint B:Avg = 210/6 = 35 months
• They both last 35 months before fading. No help in deciding which to buy.
• Paint A:Spread = 60 – 10 = 50 months
• Paint B:Spread = 45 – 25 = 20 months
• Paint B has a smaller variancewhich means that it performs more consistently. Choose paint B.

Formula for Population

Variance =

Standard Deviation =

Formula for Sample

Variance =

Standard Deviation =

Formulas for Variance and St. Deviation

Population

Sample

Variance

Variance

Standard Deviation

Standard Deviation

Standard Deviation
• A more powerful approach to determining how much individual data values vary.
• This is a measure of the average distance of the observations from their mean.
• Like the mean, the standard deviation is appropriate only for symmetric data!
• The use of squared deviations makes the standard deviation even more sensitive than the mean to outliers!
Standard Deviation
• One way to think about spread is to examine how far each data value is from the mean.
• This difference is called a deviation.
• We could just average the deviations, but the positive and negative differences always cancel each other out! So, the average deviation is always 0  not very helpful!
Finding Variance
• To keep them from canceling out, we squareeach deviation.
• Squaring always gives a positive value, so the sum will not be zero!
• Squaring also emphasizes larger differences – a feature that turns out to be good and bad.
• When we add up these squared deviations and find their average (almost), we call the result the variance.
Finding Standard Deviation
• This is the average of the squareddistance from the mean.
• Variance will play an important role later – but it has a problem as a measure of spread.
• Whatever the units of the original data are, the variance is in squared units – we want measures of spread to have the same units as the data, so to get back to the original units, we take the square root of .
• The result is, s, is the standard deviation.
Let’s look at the data again on the number of pets owned by a group of 9 children.

Recall that the mean was 5 pets.

Let’s take a graphical look at the “deviations” from the mean:

1 – 5 = -4

3 – 5 = -2

4 – 5 = -1

4 – 5 = -1

4 – 5 = -1

5 – 5 = 0

7 – 5 = 2

8 – 5 = 3

9 – 5 = 4

Find Variance:

This is the “average” squared deviation.

Find the Standard Deviation:

This 2.55 is roughly the average distance of the values in the data set from the mean.

Homework
• Worksheet