SlideShare une entreprise Scribd logo
1  sur  22
Objectives
The student will be able to:

•

find the variance of a data set.

•

find the standard deviation of a
data set.

SOL: A.9

2009
Variance
Variance is the average
squared deviation from the
mean of a set of data. It is
used to find the standard
deviation.
Variance
1.

Find the mean of the data.

Hint – mean is the average so add up the
values and divide by the number of items.
2. Subtract the mean from each value – the
result is called the deviation from the mean.
3. Square each deviation of the mean.
4. Find the sum of the squares.
5. Divide the total by the number of items.
Variance Formula
The variance formula includes the
Sigma Notation, ∑
, which represents
the sum of all the items to the right
2
of Sigma.
(x − µ )

∑

n

Mean is represented by
the number of items.

µ

and n is
Standard Deviation
Standard Deviation shows the
variation in data. If the data is close
together, the standard deviation will
be small. If the data is spread out, the
standard deviation will be large.
Standard Deviation is often denoted
by the lowercase Greek letter
sigma, .

σ
The bell curve which represents a
normal distribution of data shows
what standard deviation represents.

One standard deviation away from the mean ( µ ) in
either direction on the horizontal axis accounts for
around 68 percent of the data. Two standard
deviations away from the mean accounts for roughly
95 percent of the data with three standard deviations
representing about 99 percent of the data.
Standard Deviation
Find the variance.
a) Find the mean of the data.
b) Subtract the mean from each value.
c) Square each deviation of the mean.
d) Find the sum of the squares.
e) Divide the total by the number of
items.
Take the square root of the variance.
Standard Deviation Formula
The standard deviation formula can be
represented using Sigma Notation:

σ=

( x − µ )2
∑
n

Notice the standard deviation formula
is the square root of the variance.
Find the variance and
standard deviation
The math test scores of five students
are: 92,88,80,68 and 52.
1) Find the mean: (92+88+80+68+52)/5 = 76.
2) Find the deviation from the mean:
92-76=16
88-76=12
80-76=4
68-76= -8
52-76= -24
Find the variance and
standard deviation
The math test scores of five
students are: 92,88,80,68 and 52.
3) Square the deviation from the
2
mean: 256
(16) =

(12) = 144
2
(4) = 16
2
(− 8) = 64
2

( − 24) = 576
2
Find the variance and
standard deviation
The math test scores of five students
are: 92,88,80,68 and 52.
4) Find the sum of the squares of the
deviation from the mean:
256+144+16+64+576= 1056
5) Divide by the number of data
items to find the variance:
1056/5 = 211.2
Find the variance and
standard deviation
The math test scores of five students
are: 92,88,80,68 and 52.
6) Find the square root of the
variance: 211.2 = 14.53

Thus the standard deviation of
the test scores is 14.53.
Standard Deviation
A different math class took the
same test with these five test
scores: 92,92,92,52,52.
Find the standard deviation for
this class.
Hint:

1. Find the mean of the data.
2. Subtract the mean from each value
– called the deviation from the
mean.
3. Square each deviation of the mean.
4. Find the sum of the squares.
5. Divide the total by the number of
items – result is the variance.
6. Take the square root of the
variance – result is the standard
deviation.
Solve:
A different math class took the
same test with these five test
scores: 92,92,92,52,52.
Find the standard deviation for this
class.
Answer Now
The math test scores of five students
are: 92,92,92,52 and 52.
1) Find the mean: (92+92+92+52+52)/5 = 76
2) Find the deviation from the mean:
92-76=16 92-76=16 92-76=16
52-76= -24 52-76= -24
3) Square the deviation from the mean:
(16) 2 = 256

(16) 2 = 256

(−24)2 = 576

(16) 2 = 256

(−24) 2 = 576

4) Find the sum of the squares:
256+256+256+576+576= 1920
The math test scores of five
students are: 92,92,92,52 and 52.
5) Divide the sum of the squares
by the number of items :
1920/5 = 384 variance
6) Find the square root of the variance:

384 = 19.6

Thus the standard deviation of the
second set of test scores is 19.6.
Analyzing the data:

Consider both sets of scores. Both
classes have the same mean, 76.
However, each class does not have the
same scores. Thus we use the standard
deviation to show the variation in the
scores. With a standard variation of
14.53 for the first class and 19.6 for the
second class, what does this tell us?
Answer Now
Analyzing the data:
Class A: 92,88,80,68,52
Class B: 92,92,92,52,52

With a standard variation of 14.53
for the first class and 19.6 for the
second class, the scores from the
second class would be more spread
out than the scores in the second
class.
Analyzing the data:
Class A: 92,88,80,68,52
Class B: 92,92,92,52,52

Class C: 77,76,76,76,75

Estimate the standard deviation for Class C.
a) Standard deviation will be less than 14.53.
b) Standard deviation will be greater than 19.6.
c) Standard deviation will be between 14.53
and 19.6.
d) Can not make an estimate of the standard
deviation.
Answer Now
Analyzing the data:

Class A: 92,88,80,68,52
Class B: 92,92,92,52,52
Class C: 77,76,76,76,75
Estimate the standard deviation for Class C.
a) Standard deviation will be less than 14.53.
b) Standard deviation will be greater than 19.6.
c) Standard deviation will be between 14.53
and 19.6
d) Can not make an estimate if the standard
deviation.

Answer: A

The scores in class C have the same
mean of 76 as the other two classes.
However, the scores in Class C are all
much closer to the mean than the other
classes so the standard deviation will be
smaller than for the other classes.
Summary:
As we have seen, standard deviation
measures the dispersion of data.
The greater the value of the
standard deviation, the further the
data tend to be dispersed from the
mean.

Contenu connexe

Tendances

Standard Deviation and Variance
Standard Deviation and VarianceStandard Deviation and Variance
Standard Deviation and Variance
Jufil Hombria
 
9. basic concepts_of_one_way_analysis_of_variance_(anova)
9. basic concepts_of_one_way_analysis_of_variance_(anova)9. basic concepts_of_one_way_analysis_of_variance_(anova)
9. basic concepts_of_one_way_analysis_of_variance_(anova)
Irfan Hussain
 

Tendances (20)

Measures of central tendency ppt
Measures of central tendency pptMeasures of central tendency ppt
Measures of central tendency ppt
 
Measures of Variability
Measures of VariabilityMeasures of Variability
Measures of Variability
 
Measures of variability
Measures of variabilityMeasures of variability
Measures of variability
 
Standard Deviation and Variance
Standard Deviation and VarianceStandard Deviation and Variance
Standard Deviation and Variance
 
MEAN DEVIATION
MEAN DEVIATIONMEAN DEVIATION
MEAN DEVIATION
 
Measure of dispersion part II ( Standard Deviation, variance, coefficient of ...
Measure of dispersion part II ( Standard Deviation, variance, coefficient of ...Measure of dispersion part II ( Standard Deviation, variance, coefficient of ...
Measure of dispersion part II ( Standard Deviation, variance, coefficient of ...
 
9. basic concepts_of_one_way_analysis_of_variance_(anova)
9. basic concepts_of_one_way_analysis_of_variance_(anova)9. basic concepts_of_one_way_analysis_of_variance_(anova)
9. basic concepts_of_one_way_analysis_of_variance_(anova)
 
Variance And Standard Deviation
Variance And Standard DeviationVariance And Standard Deviation
Variance And Standard Deviation
 
Measures of Variability
Measures of VariabilityMeasures of Variability
Measures of Variability
 
Variance & standard deviation
Variance & standard deviationVariance & standard deviation
Variance & standard deviation
 
Standard deviation
Standard deviationStandard deviation
Standard deviation
 
Measures of central tendency and dispersion
Measures of central tendency and dispersionMeasures of central tendency and dispersion
Measures of central tendency and dispersion
 
Skewness and Kurtosis
Skewness and KurtosisSkewness and Kurtosis
Skewness and Kurtosis
 
the normal curve
the normal curvethe normal curve
the normal curve
 
VARIANCE
VARIANCEVARIANCE
VARIANCE
 
Discriminant analysis
Discriminant analysisDiscriminant analysis
Discriminant analysis
 
coefficient variation
coefficient variationcoefficient variation
coefficient variation
 
RELATION BETWEEN MEAN, MEDIAN AND MODE IN BIOSTATIC
RELATION BETWEEN MEAN, MEDIAN AND MODE IN BIOSTATICRELATION BETWEEN MEAN, MEDIAN AND MODE IN BIOSTATIC
RELATION BETWEEN MEAN, MEDIAN AND MODE IN BIOSTATIC
 
Skewness
SkewnessSkewness
Skewness
 
The Mean Deviation.pptx
The Mean Deviation.pptxThe Mean Deviation.pptx
The Mean Deviation.pptx
 

En vedette

Standard Deviation
Standard DeviationStandard Deviation
Standard Deviation
pwheeles
 
Teaching Demo
Teaching DemoTeaching Demo
Teaching Demo
smilelynn
 
Mean Median Mode
Mean Median ModeMean Median Mode
Mean Median Mode
hiratufail
 
Malimu variance and standard deviation
Malimu variance and standard deviationMalimu variance and standard deviation
Malimu variance and standard deviation
Miharbi Ignasm
 
2.3 Histogram/Frequency Polygon/Ogives
2.3 Histogram/Frequency Polygon/Ogives2.3 Histogram/Frequency Polygon/Ogives
2.3 Histogram/Frequency Polygon/Ogives
mlong24
 
Spearman Rank Correlation Presentation
Spearman Rank Correlation PresentationSpearman Rank Correlation Presentation
Spearman Rank Correlation Presentation
cae_021
 

En vedette (20)

Standard Deviation
Standard DeviationStandard Deviation
Standard Deviation
 
Grouped Mean Median Mode
Grouped Mean Median ModeGrouped Mean Median Mode
Grouped Mean Median Mode
 
Standard deviation
Standard deviationStandard deviation
Standard deviation
 
Teaching Demo
Teaching DemoTeaching Demo
Teaching Demo
 
Mean Median Mode
Mean Median ModeMean Median Mode
Mean Median Mode
 
Malimu variance and standard deviation
Malimu variance and standard deviationMalimu variance and standard deviation
Malimu variance and standard deviation
 
Spearman Rank
Spearman RankSpearman Rank
Spearman Rank
 
Partial Differential Equations, 3 simple examples
Partial Differential Equations, 3 simple examplesPartial Differential Equations, 3 simple examples
Partial Differential Equations, 3 simple examples
 
First order non-linear partial differential equation & its applications
First order non-linear partial differential equation & its applicationsFirst order non-linear partial differential equation & its applications
First order non-linear partial differential equation & its applications
 
Propteties of Standard Deviation
Propteties of Standard DeviationPropteties of Standard Deviation
Propteties of Standard Deviation
 
ARITHMETIC MEAN AND SERIES
ARITHMETIC MEAN AND SERIESARITHMETIC MEAN AND SERIES
ARITHMETIC MEAN AND SERIES
 
Skewness
SkewnessSkewness
Skewness
 
2.3 Histogram/Frequency Polygon/Ogives
2.3 Histogram/Frequency Polygon/Ogives2.3 Histogram/Frequency Polygon/Ogives
2.3 Histogram/Frequency Polygon/Ogives
 
Calculation of arithmetic mean
Calculation of arithmetic meanCalculation of arithmetic mean
Calculation of arithmetic mean
 
Correlation analysis ppt
Correlation analysis pptCorrelation analysis ppt
Correlation analysis ppt
 
Spearman Rank Correlation Presentation
Spearman Rank Correlation PresentationSpearman Rank Correlation Presentation
Spearman Rank Correlation Presentation
 
Partial differential equations
Partial differential equationsPartial differential equations
Partial differential equations
 
MATH Lesson Plan sample for demo teaching
MATH Lesson Plan sample for demo teaching MATH Lesson Plan sample for demo teaching
MATH Lesson Plan sample for demo teaching
 
Skewness
SkewnessSkewness
Skewness
 
Correlation and Simple Regression
Correlation  and Simple RegressionCorrelation  and Simple Regression
Correlation and Simple Regression
 

Similaire à Standard deviation (3)

Topic 8a Basic Statistics
Topic 8a Basic StatisticsTopic 8a Basic Statistics
Topic 8a Basic Statistics
Yee Bee Choo
 
Describing Distributions with Numbers
Describing Distributions with NumbersDescribing Distributions with Numbers
Describing Distributions with Numbers
nszakir
 
Central tendency
Central tendencyCentral tendency
Central tendency
heyyou02
 

Similaire à Standard deviation (3) (20)

Standard Deviation.ppt
Standard Deviation.pptStandard Deviation.ppt
Standard Deviation.ppt
 
Overview of variance and Standard deviation.pptx
Overview of variance and Standard deviation.pptxOverview of variance and Standard deviation.pptx
Overview of variance and Standard deviation.pptx
 
Module 3 statistics
Module 3   statisticsModule 3   statistics
Module 3 statistics
 
Standard deviation & variance
Standard deviation & varianceStandard deviation & variance
Standard deviation & variance
 
MATH DEMO.pptx
MATH DEMO.pptxMATH DEMO.pptx
MATH DEMO.pptx
 
MEASURES-OF-CENTRAL-TENDENCY-VARIABILITY-TEAM-S-PERSISTENCE.pptx
MEASURES-OF-CENTRAL-TENDENCY-VARIABILITY-TEAM-S-PERSISTENCE.pptxMEASURES-OF-CENTRAL-TENDENCY-VARIABILITY-TEAM-S-PERSISTENCE.pptx
MEASURES-OF-CENTRAL-TENDENCY-VARIABILITY-TEAM-S-PERSISTENCE.pptx
 
Standard deviation quartile deviation
Standard deviation  quartile deviationStandard deviation  quartile deviation
Standard deviation quartile deviation
 
Measures of central tendency by maria diza c. febrio
Measures of central tendency by maria diza c. febrioMeasures of central tendency by maria diza c. febrio
Measures of central tendency by maria diza c. febrio
 
Topic 8a Basic Statistics
Topic 8a Basic StatisticsTopic 8a Basic Statistics
Topic 8a Basic Statistics
 
Statistics and probability pptx lesson 303
Statistics and probability pptx  lesson 303Statistics and probability pptx  lesson 303
Statistics and probability pptx lesson 303
 
test & measuement
test & measuementtest & measuement
test & measuement
 
ME SP 11 Q3 0302 PS.pptx statistics and probability
ME SP 11 Q3 0302 PS.pptx statistics and probabilityME SP 11 Q3 0302 PS.pptx statistics and probability
ME SP 11 Q3 0302 PS.pptx statistics and probability
 
Describing Distributions with Numbers
Describing Distributions with NumbersDescribing Distributions with Numbers
Describing Distributions with Numbers
 
lesson 4 measures of central tendency copy
lesson 4 measures of central tendency   copylesson 4 measures of central tendency   copy
lesson 4 measures of central tendency copy
 
Lect 3 background mathematics
Lect 3 background mathematicsLect 3 background mathematics
Lect 3 background mathematics
 
ANSWERS
ANSWERSANSWERS
ANSWERS
 
Handling Data 2
Handling Data 2Handling Data 2
Handling Data 2
 
VARIANCE AND STANDARD DEVIATION.pptx
VARIANCE AND STANDARD DEVIATION.pptxVARIANCE AND STANDARD DEVIATION.pptx
VARIANCE AND STANDARD DEVIATION.pptx
 
Central tendency
Central tendencyCentral tendency
Central tendency
 
Measures-of-Central-Tendency.ppt
Measures-of-Central-Tendency.pptMeasures-of-Central-Tendency.ppt
Measures-of-Central-Tendency.ppt
 

Dernier

Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 

Dernier (20)

The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation StrategiesHTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 

Standard deviation (3)

  • 1. Objectives The student will be able to: • find the variance of a data set. • find the standard deviation of a data set. SOL: A.9 2009
  • 2. Variance Variance is the average squared deviation from the mean of a set of data. It is used to find the standard deviation.
  • 3. Variance 1. Find the mean of the data. Hint – mean is the average so add up the values and divide by the number of items. 2. Subtract the mean from each value – the result is called the deviation from the mean. 3. Square each deviation of the mean. 4. Find the sum of the squares. 5. Divide the total by the number of items.
  • 4. Variance Formula The variance formula includes the Sigma Notation, ∑ , which represents the sum of all the items to the right 2 of Sigma. (x − µ ) ∑ n Mean is represented by the number of items. µ and n is
  • 5. Standard Deviation Standard Deviation shows the variation in data. If the data is close together, the standard deviation will be small. If the data is spread out, the standard deviation will be large. Standard Deviation is often denoted by the lowercase Greek letter sigma, . σ
  • 6. The bell curve which represents a normal distribution of data shows what standard deviation represents. One standard deviation away from the mean ( µ ) in either direction on the horizontal axis accounts for around 68 percent of the data. Two standard deviations away from the mean accounts for roughly 95 percent of the data with three standard deviations representing about 99 percent of the data.
  • 7. Standard Deviation Find the variance. a) Find the mean of the data. b) Subtract the mean from each value. c) Square each deviation of the mean. d) Find the sum of the squares. e) Divide the total by the number of items. Take the square root of the variance.
  • 8. Standard Deviation Formula The standard deviation formula can be represented using Sigma Notation: σ= ( x − µ )2 ∑ n Notice the standard deviation formula is the square root of the variance.
  • 9. Find the variance and standard deviation The math test scores of five students are: 92,88,80,68 and 52. 1) Find the mean: (92+88+80+68+52)/5 = 76. 2) Find the deviation from the mean: 92-76=16 88-76=12 80-76=4 68-76= -8 52-76= -24
  • 10. Find the variance and standard deviation The math test scores of five students are: 92,88,80,68 and 52. 3) Square the deviation from the 2 mean: 256 (16) = (12) = 144 2 (4) = 16 2 (− 8) = 64 2 ( − 24) = 576 2
  • 11. Find the variance and standard deviation The math test scores of five students are: 92,88,80,68 and 52. 4) Find the sum of the squares of the deviation from the mean: 256+144+16+64+576= 1056 5) Divide by the number of data items to find the variance: 1056/5 = 211.2
  • 12. Find the variance and standard deviation The math test scores of five students are: 92,88,80,68 and 52. 6) Find the square root of the variance: 211.2 = 14.53 Thus the standard deviation of the test scores is 14.53.
  • 13. Standard Deviation A different math class took the same test with these five test scores: 92,92,92,52,52. Find the standard deviation for this class.
  • 14. Hint: 1. Find the mean of the data. 2. Subtract the mean from each value – called the deviation from the mean. 3. Square each deviation of the mean. 4. Find the sum of the squares. 5. Divide the total by the number of items – result is the variance. 6. Take the square root of the variance – result is the standard deviation.
  • 15. Solve: A different math class took the same test with these five test scores: 92,92,92,52,52. Find the standard deviation for this class. Answer Now
  • 16. The math test scores of five students are: 92,92,92,52 and 52. 1) Find the mean: (92+92+92+52+52)/5 = 76 2) Find the deviation from the mean: 92-76=16 92-76=16 92-76=16 52-76= -24 52-76= -24 3) Square the deviation from the mean: (16) 2 = 256 (16) 2 = 256 (−24)2 = 576 (16) 2 = 256 (−24) 2 = 576 4) Find the sum of the squares: 256+256+256+576+576= 1920
  • 17. The math test scores of five students are: 92,92,92,52 and 52. 5) Divide the sum of the squares by the number of items : 1920/5 = 384 variance 6) Find the square root of the variance: 384 = 19.6 Thus the standard deviation of the second set of test scores is 19.6.
  • 18. Analyzing the data: Consider both sets of scores. Both classes have the same mean, 76. However, each class does not have the same scores. Thus we use the standard deviation to show the variation in the scores. With a standard variation of 14.53 for the first class and 19.6 for the second class, what does this tell us? Answer Now
  • 19. Analyzing the data: Class A: 92,88,80,68,52 Class B: 92,92,92,52,52 With a standard variation of 14.53 for the first class and 19.6 for the second class, the scores from the second class would be more spread out than the scores in the second class.
  • 20. Analyzing the data: Class A: 92,88,80,68,52 Class B: 92,92,92,52,52 Class C: 77,76,76,76,75 Estimate the standard deviation for Class C. a) Standard deviation will be less than 14.53. b) Standard deviation will be greater than 19.6. c) Standard deviation will be between 14.53 and 19.6. d) Can not make an estimate of the standard deviation. Answer Now
  • 21. Analyzing the data: Class A: 92,88,80,68,52 Class B: 92,92,92,52,52 Class C: 77,76,76,76,75 Estimate the standard deviation for Class C. a) Standard deviation will be less than 14.53. b) Standard deviation will be greater than 19.6. c) Standard deviation will be between 14.53 and 19.6 d) Can not make an estimate if the standard deviation. Answer: A The scores in class C have the same mean of 76 as the other two classes. However, the scores in Class C are all much closer to the mean than the other classes so the standard deviation will be smaller than for the other classes.
  • 22. Summary: As we have seen, standard deviation measures the dispersion of data. The greater the value of the standard deviation, the further the data tend to be dispersed from the mean.