2.4-measures of variation
DESCRIPTION
2.4-MEASURES OF VARIATION. 1) Range – Difference between max & min 2) Deviation – Difference between entry & mean 3) Variance – Sum of differences between entries and mean, divided by population or sample -1. 4) Standard Deviation – Square root of variance. Range. - PowerPoint PPT PresentationTRANSCRIPT
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2.4-MEASURES OF VARIATION
• 1) Range – Difference between max & min• 2) Deviation – Difference between entry &
mean• 3) Variance – Sum of differences between
entries and mean, divided by population or sample -1.
• 4) Standard Deviation – Square root of variance
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Range
• Range = (Maximum entry) – (Minimum entry)• Find range of the starting salaries (1000 of $):41 38 39 45 47 41 44 41 37 42
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Range
• Range = (Maximum entry) – (Minimum entry)• Find range of the starting salaries (1000 of $):41 38 39 45 47 41 44 41 37 4247-37 = range of 10 or $10,000
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Range
• Range = (Maximum entry) – (Minimum entry)• Find range of the starting salaries (1000 of $):41 38 39 45 47 41 44 41 37 4247-37 = range of 10 or $10,000 • Find range of the starting salaries (1000 of $):40 23 41 50 49 32 41 29 52 58
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Range
• Range = (Maximum entry) – (Minimum entry)• Find range of the starting salaries (1000 of $):41 38 39 45 47 41 44 41 37 4247-37 = range of 10 or $10,000 • Find range of the starting salaries (1000 of $):40 23 41 50 49 32 41 29 52 58
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Range
• Range = (Maximum entry) – (Minimum entry)• Find range of the starting salaries (1000 of $):41 38 39 45 47 41 44 41 37 4247-37 = range of 10 or $10,000 • Find range of the starting salaries (1000 of $):40 23 41 50 49 32 41 29 52 5858 – 23 = 35 or $35,000
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Deviation
• Deviation = How far away entries are from mean. For each entry, entry – mean of data set. x = x - µ. May be positive or negative
• Population Variance = Mean of the SQUARE of the variance. σ² = Σ(x-µ)²÷N
• Sample Variance = Variance for a SAMPLE of a population. s² = Σ(x-x)²÷(n-1)
• Standard deviation = SQUARE ROOT of variance.σ = √ Σ(x-µ)² ÷ N s=√Σ(x-x)²÷(n-1)
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41383945474144413742Σ = Σ= Σ = SSx=sum of
Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41383945474144413742Σ = 415 Σ= Σ = SSx=sum of
Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41383945474144413742Σ = 415µ=
Σ= Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41383945474144413742Σ = 415µ= 415/10=41.5
Σ= Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 41-41.5 = -.5383945474144413742Σ = 415µ= 415/10=41.5
Σ= Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 -.538 -3.539 -2.545 3.547 5.541 -.544 2.541 -.537 -4.542 .5Σ = 415µ= 415/10=41.5
Σ= Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 -.538 -3.539 -2.545 3.547 5.541 -.544 2.541 -.537 -4.542 .5Σ = 415µ= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 -.5 (-.5)² = .2538 -3.539 -2.545 3.547 5.541 -.544 2.541 -.537 -4.542 .5Σ = 415µ= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.2541 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415µ= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.2541 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415µ= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 88.5
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.2541 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415µ= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 88.5
N = 10
σ² = SSx/N
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.2541 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415µ= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 88.5
N = 10
σ² = SSx/N
σ²= 88.5/10 = 8.85
σ= √σ²
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.25√41 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415µ= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 88.5
N = 10
σ² = SSx/N
σ²= 88.5/10 = 8.85
σ= √σ²
σ =√8.85 = 2.97
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40234150493241295258Σ = Σ= Σ = SSx=sum of
Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40234150493241295258Σ = 415 Σ= Σ = SSx=sum of
Squares =
![Page 23: 2.4-MEASURES OF VARIATION](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815ab2550346895dc85e52/html5/thumbnails/23.jpg)
Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40234150493241295258Σ = 415µ = 415/10=41.5
Σ= Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 40-41.5=-1.5234150493241295258Σ = 415µ = 415/10=41.5
Σ= Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.523 -18.541 -.550 8.549 7.532 -9.541 -.529 -12.552 10.558 16.5Σ = 415µ = 415/10=41.5
Σ= Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.523 -18.541 -.550 8.549 7.532 -9.541 -.529 -12.552 10.558 16.5Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.5 (-1.5)²=2.2523 -18.541 -.550 8.549 7.532 -9.541 -.529 -12.552 10.558 16.5Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares =
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
N=10
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
N=10
σ²=SSx/10
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
N=10
σ²=SSx/10
σ²=1102.5/10 = 110.25
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
N=10
σ²=SSx/10
σ²=1102.5/10 = 110.25
σ=√σ²
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Find mean, deviation, sum of squares, population variance & std. deviation
Salaryx
Deviationx- µ
Squares(x-µ)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415µ = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
N=10
σ²=SSx/10
σ²=1102.5/10 = 110.25
σ=√σ²
σ=√110.25 = 10.5
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.2541 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415x= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 88.5
n = 10
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.2541 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415x= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 88.5
n = 10
s²=SSx/(n-1)
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.2541 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415x= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 88.5
n = 10
s²=SSx/(n-1)
s²=88.5/(10-1) = 88.5/9 =9.83
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
41 -.5 (-.5)² = .2538 -3.5 12.2539 -2.5 6.2545 3.5 12.2547 5.5 30.2541 -.5 .2544 2.5 6.2541 -.5 .2537 -4.5 20.2542 .5 .25Σ = 415x= 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 88.5
n = 10
s²=SSx/(n-1)
s²=88.5/(10-1) = 88.5/9 =9.83
s=3.14
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415x = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
n=10
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415x = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
n=10
s²=SSx/(n-1)
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415x = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
n=10
s²=SSx/(n-1)
s²=1102.5/(10-1) = 1102.5/9 = 122.5
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415x = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
n=10
s²=SSx/(n-1)
s²=1102.5/(10-1) = 1102.5/9 = 122.5
s=√s²
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Find the Sample Variance and Sample Standard Deviation
Salaryx
Deviationx- x
Squares(x-x)²
40 -1.5 (-1.5)²=2.2523 -18.5 342.2541 -.5 .2550 8.5 72.2549 7.5 56.2532 -9.5 90.2541 -.5 .2529 -12.5 156.2552 10.5 110.2558 16.5 272.25Σ = 415x = 415/10=41.5
Σ=0 Σ = SSx=sum of Squares = 1102.5
n=10
s²=SSx/(n-1)
s²=1102.5/(10-1) = 1102.5/9 = 122.5
s=√s²
s=√122.5 = 11.07
![Page 44: 2.4-MEASURES OF VARIATION](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815ab2550346895dc85e52/html5/thumbnails/44.jpg)
Interpreting Standard Deviation
50
5
10
5
Data012345
34567
x=5s=1.2
x=5s=0
2 3 7 801234
Series 1Series 2 x=5
s=3.0
![Page 45: 2.4-MEASURES OF VARIATION](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815ab2550346895dc85e52/html5/thumbnails/45.jpg)
Estimate the Standard Deviation
405
10 Series 1
N=8µ=4σ=
3 5012345
Series 1
Series 1 N=8µ=4σ=
1 3 5 70
0.5
1
1.5
2
2.5Series 1
Series 1N=8µ=4σ=
![Page 46: 2.4-MEASURES OF VARIATION](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815ab2550346895dc85e52/html5/thumbnails/46.jpg)
Estimate the Standard Deviation
405
10 Series 1
N=8µ=4σ=0
3 5012345
Series 1
Series 1 N=8µ=4σ=
1 3 5 70
0.5
1
1.5
2
2.5Series 1
Series 1N=8µ=4σ=
![Page 47: 2.4-MEASURES OF VARIATION](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815ab2550346895dc85e52/html5/thumbnails/47.jpg)
Estimate the Standard Deviation
405
10 Series 1
N=8µ=4σ=0
3 5012345
Series 1
Series 1 N=8µ=4σ=1
1 3 5 70
0.5
1
1.5
2
2.5Series 1
Series 1N=8µ=4σ=+ 1 & 3
![Page 48: 2.4-MEASURES OF VARIATION](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815ab2550346895dc85e52/html5/thumbnails/48.jpg)
Estimate the Standard Deviation
405
10 Series 1
N=8µ=4σ=0
3 5012345
Series 1
Series 1 N=8µ=4σ=1
1 3 5 70
0.5
1
1.5
2
2.5Series 1
Series 1N=8µ=4σ=2σ²=