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![Page 1: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/1.jpg)
Variability
![Page 2: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/2.jpg)
Variability How tightly clustered or how widely dispersed
the values are in a data set. Example
Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52] Both have a mean of 50, but data set 1 clearly
has greater Variability than data set 2.
Variability
![Page 3: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/3.jpg)
Variability: The Range
The Range is one measure of variability The range is the difference between the maximum
and minimum values in a set
Example Data set 1: [1,25,50,75,100]; R: 100-0 +1 = 100 Data set 2: [48,49,50,51,52]; R: 52-48 + 1= 5 The range ignores how data are distributed and
only takes the extreme scores into account
RANGE = (Xlargest – Xsmallest) + 1
![Page 4: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/4.jpg)
Quartiles
Split Ordered Data into 4 Quarters
= first quartile
= second quartile= Median
= third quartile
25% 25% 25% 25%
1Q 2Q 3Q
1Q
3Q
2Q
![Page 5: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/5.jpg)
Quartiles
MdQ1 Q3
75%25%
![Page 6: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/6.jpg)
Variability: Interquartile Range
Difference between third & first quartiles Interquartile Range = Q3 - Q1
Spread in middle 50% Not affected by extreme values
![Page 7: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/7.jpg)
Standard Deviation and Variance How much do scores deviate from the mean?
deviation =
Why not just add these all up and take the mean?
X
X X-1
0
6
1
= 2 )-(X
![Page 8: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/8.jpg)
Standard Deviation and Variance Solve the problem by squaring the deviations!
X
X- (X-)2
1 -1 1
0 -2 4
6 +4 16
1 -1 1 = 2
Variance =
N
uX
22 )(
![Page 9: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/9.jpg)
Standard Deviation and Variance Higher value means greater variability around Critical for inferential statistics! But, not as useful as a purely descriptive statistic
hard to interpret “squared” scores!
Solution un-square the variance!
Standard Deviation =N
uX
2)(
![Page 10: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/10.jpg)
Variability: Standard Deviation
The Standard Deviation tells us approximately how far the scores vary from the mean on average
estimate of average deviation/distance from small value means scores clustered close to large value means scores spread farther from Overall, most common and important measure extremely useful as a descriptive statistic extremely useful in inferential statistics
The typical deviation in a given distribution
![Page 11: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/11.jpg)
Standard Deviation can be calculated with the sum of squares (SS) divided by n
Variability: Standard Deviation
N
SS
N
X
2)(
![Page 12: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/12.jpg)
Sample variance and standard deviation
Sample will tend to have less variability than popl’n
if we use the population fomula, our sample statistic will be biased
will tend to underestimate popl’n variance
![Page 13: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/13.jpg)
Sample variance and standard deviation Correct for problem by adjusting formula
Different symbol: s2 vs. 2 Different denominator: n-1 vs. N n-1 = “degrees of freedom” Everything else is the same Interpretation is the same
1
)( 22
n
MXs
![Page 14: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/14.jpg)
Definitional Formula:
deviation squared-deviation ‘Sum of Squares’ = SS degrees of freedom
1n
ss
df
SS
1
)( 22
n
XXs
1n
ss
df
SS
1
)( 2
n
XXs
Variance:
Standard Deviation:
![Page 15: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/15.jpg)
Variability: Standard Deviation
let X = [3, 4, 5 ,6, 7] M = 5 (X - M) = [-2, -1, 0, 1, 2]
subtract M from each number in X (X - M)2 = [4, 1, 0, 1, 4]
squared deviations from the mean (X - M)2 = 10
sum of squared deviations from the mean (SS)
(X - M)2 /n-1 = 10/5 = 2.5 average squared deviation from the mean
(X - M)2 /n-1 = 2.5 = 1.58 square root of averaged squared deviation
1
)( 2
n
XXs
![Page 16: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/16.jpg)
Variability: Standard Deviation
let X = [1, 3, 5, 7, 9] M = 5 (X - M) = [-4, -2, 0, 2, 4 ]
subtract M from each number in X (X - M)2 = [16, 4, 0, 4, 16]
squared deviations from the mean (X - M)2 = 40
sum of squared deviations from the mean (SS) (X - M)2 /n-1 = 40/4 = 10
average squared deviation from the mean (X - M)2 /n-1 = 10 = 3.16
square root of averaged squared deviation
1
)( 2
n
XXs
![Page 17: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/17.jpg)
In class example
Work on handout
![Page 18: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/18.jpg)
Standard Deviation & Standard Scores Z scores are expressed in the following way
Z scores express how far a particular score is from the mean in units of standard deviation
X
Z
![Page 19: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/19.jpg)
Standard Deviation & Standard Scores Z scores provide a common scale to express
deviations from a group mean
ZX
X
Z
![Page 20: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/20.jpg)
Let’s say someone has an IQ of 145 and is 52 inches tall IQ in a population has a mean of 100 and a
standard deviation of 15 Height in a population has a mean of 64” with a
standard deviation of 4 How many standard deviations is this person
away from the average IQ? How many standard deviations is this person
away from the average height?
Standard Deviation and Standard Scores
![Page 21: Variability. How tightly clustered or how widely dispersed the values are in a data set. Example Data set 1: [0,25,50,75,100] Data set 2: [48,49,50,51,52]](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f2b5503460f94c45bef/html5/thumbnails/21.jpg)
Homework
Chapter 4 8, 9, 11, 12, 16, 17