SlideShare a Scribd company logo
DESCRIPTIVE STATISTICS
K MURUGESAN
DescriptiveStatistics
Central Tendency
Mean
Median
Mode
Dispersion
Range
Variance
Standard
Deviation
Descriptive statistics
Central Tendency
• Mean is the average or arithmetic mean of the data.
• Median is the middle value of the data set, when the
data set is arranged in the ascending or descending order.
• Mode is the most frequently observed value(s).
• Range is the difference between highest and lowest observations in the data set
• Variance is a measure of spread of a data set
It is calculated as the average squared deviation of each number from mean of a data set.
• Standard Deviation is the Square Root of the Variance.
Dispersion
Mean
Variance
Standard
Deviation
X
n
x
n
i
i


1
The marks obtained by 10 students in a subject are as
given below
9,31,35,34,37,32,100,33,30,100
Average Marks:
=(9+31+35+34+37+32+100+33+30+100)/10
=44
Effect of outliers on Mean
The marks obtained by 8 students in a subject are as
given below
31,35,34,37,32,38,33,30
Average Marks:
=(31+35+34+37+32+38+33+30)/8
=33.75
Mean
The marks obtained by 10 students in a subject are as
given below
9,31,35,34,37,32,100,33,30,100
Average Marks:
=(9+31+35+34+37+32+100+33+30+100)/10
=44.1
Effect of outliers on Mean
• The middle value when a variable’s values are ranked in
ascending/ descending order
If n is odd;
Median is ((n-1)/2)+1th value
If n is even;
Median is Average of {(n/2), (n/2)+1} values
Median
Median = 60
(six cases above, six below)
Marks scored by 13 students in an exam is as give below.
Find the median marks
35,90,40,62,54,95,38,60,73,92,51,32,74
Median
32
35
38
40
51
54
60
62
73
74
90
92
95
When “n” is odd
Median = (60+64) /2
= 62
(Average of 5th and 6th values)
Marks scored by 10 students in an exam is as give below.
Find the median marks
38,84,42,64,55,96,39,60,73,92
Median
38
39
42
55
60
64
73
84
92
96
When “n” is even
The marks obtained by 10 students in a subject are as
given below
9,31,35,34,37,32,100,33,30,100
Median: 9,30,31,32,33,34,35,37,100,100
Effect of outliers on Median
= (33+34)/2
=33.5
Median is not influenced by outliers
The mode for a data set is the element that occurs the most often.
More than one mode is possible for a data set.
It is also possible that a data set may not have a mode at all.
Example-1:
Data set: 2,5,4,7,8,9,4,3,6,4,7,8,6,4,9,2
Mode: 4
Example-2:
Data set: 10,25,15,30,25,45,50,15,40,35
Modes: 25, 15
Mode
Range
Range = Largest value – Smallest value
The spread, or the difference, between the lowest and highest values of a variable.
31,35,34,37,32,38,33,30
Data set
Range = 38 - 30
= 8
Variance
A measure of the spread of the recorded values on a variable.
The larger the variance, the further the individual values are from the mean.
The smaller the variance, the closer the individual values are to the mean.
Mean
Mean
The square root of the variance (Standard deviation) reveals the
average deviation of the observations from the mean.
Standard deviation
 
1
1
2




n
xx
s
n
i
i
• The larger the standard deviation, the more
spread out the data is.
Interpretation of standard deviation
THANK YOU

More Related Content

What's hot

Descriptive Statistics
Descriptive StatisticsDescriptive Statistics
Descriptive Statistics
Neny Isharyanti
 
Measures Of Central Tendencies
Measures Of Central TendenciesMeasures Of Central Tendencies
Measures Of Central Tendencies
Adams City High School
 
T distribution | Statistics
T distribution | StatisticsT distribution | Statistics
T distribution | Statistics
Transweb Global Inc
 
Central limit theorem
Central limit theoremCentral limit theorem
Central limit theoremVijeesh Soman
 
Frequency distribution
Frequency distributionFrequency distribution
Frequency distribution
metnashikiom2011-13
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statisticsAileen Balbido
 
Measures of Central Tendency
Measures of Central TendencyMeasures of Central Tendency
Measures of Central Tendency
Nida Nafees
 
Basic Statistics & Data Analysis
Basic Statistics & Data AnalysisBasic Statistics & Data Analysis
Basic Statistics & Data Analysis
Ajendra Sharma
 
Range
RangeRange
The sampling distribution
The sampling distributionThe sampling distribution
The sampling distributionHarve Abella
 
Sampling distribution
Sampling distributionSampling distribution
Sampling distribution
Nilanjan Bhaumik
 
Measure of central tendency
Measure of central tendencyMeasure of central tendency
Measure of central tendencymauitaylor007
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
Mmedsc Hahm
 
Basic Descriptive statistics
Basic Descriptive statisticsBasic Descriptive statistics
Basic Descriptive statistics
Ajendra Sharma
 
Inferential statistics
Inferential statisticsInferential statistics
Inferential statistics
Ashok Kulkarni
 
Basics of Educational Statistics (Descriptive statistics)
Basics of Educational Statistics (Descriptive statistics)Basics of Educational Statistics (Descriptive statistics)
Basics of Educational Statistics (Descriptive statistics)
HennaAnsari
 
Z-Scores
Z-ScoresZ-Scores
Z-Scores
Gordon Weber
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
Dr Resu Neha Reddy
 

What's hot (20)

Descriptive Statistics
Descriptive StatisticsDescriptive Statistics
Descriptive Statistics
 
Measures Of Central Tendencies
Measures Of Central TendenciesMeasures Of Central Tendencies
Measures Of Central Tendencies
 
T distribution | Statistics
T distribution | StatisticsT distribution | Statistics
T distribution | Statistics
 
Central limit theorem
Central limit theoremCentral limit theorem
Central limit theorem
 
Frequency distribution
Frequency distributionFrequency distribution
Frequency distribution
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Measures of Central Tendency
Measures of Central TendencyMeasures of Central Tendency
Measures of Central Tendency
 
Basic Statistics & Data Analysis
Basic Statistics & Data AnalysisBasic Statistics & Data Analysis
Basic Statistics & Data Analysis
 
Z test
Z testZ test
Z test
 
Range
RangeRange
Range
 
The sampling distribution
The sampling distributionThe sampling distribution
The sampling distribution
 
Sampling distribution
Sampling distributionSampling distribution
Sampling distribution
 
Measure of central tendency
Measure of central tendencyMeasure of central tendency
Measure of central tendency
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Basic Descriptive statistics
Basic Descriptive statisticsBasic Descriptive statistics
Basic Descriptive statistics
 
Inferential statistics
Inferential statisticsInferential statistics
Inferential statistics
 
Basics of Educational Statistics (Descriptive statistics)
Basics of Educational Statistics (Descriptive statistics)Basics of Educational Statistics (Descriptive statistics)
Basics of Educational Statistics (Descriptive statistics)
 
Spss tutorial 1
Spss tutorial 1Spss tutorial 1
Spss tutorial 1
 
Z-Scores
Z-ScoresZ-Scores
Z-Scores
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 

Similar to Descriptive statistics

5.DATA SUMMERISATION.ppt
5.DATA SUMMERISATION.ppt5.DATA SUMMERISATION.ppt
5.DATA SUMMERISATION.ppt
chusematelephone
 
STATISTICS AND PROBABILITY.pptx
STATISTICS AND PROBABILITY.pptxSTATISTICS AND PROBABILITY.pptx
STATISTICS AND PROBABILITY.pptx
Bharathiar University
 
Descriptive Statistics: Mean, Median Mode and Standard Deviation.
Descriptive Statistics: Mean, Median Mode and Standard Deviation.Descriptive Statistics: Mean, Median Mode and Standard Deviation.
Descriptive Statistics: Mean, Median Mode and Standard Deviation.
Megha Sharma
 
SMDM Presentation.pptx
SMDM Presentation.pptxSMDM Presentation.pptx
SMDM Presentation.pptx
ananyadas124159
 
CABT Math 8 measures of central tendency and dispersion
CABT Math 8   measures of central tendency and dispersionCABT Math 8   measures of central tendency and dispersion
CABT Math 8 measures of central tendency and dispersionGilbert Joseph Abueg
 
Lesson 23 planning data analyses using statistics
Lesson 23 planning data analyses using statisticsLesson 23 planning data analyses using statistics
Lesson 23 planning data analyses using statistics
mjlobetos
 
Topic 8a Basic Statistics
Topic 8a Basic StatisticsTopic 8a Basic Statistics
Topic 8a Basic StatisticsYee Bee Choo
 
Mean_Median_Mode.ppthhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...
Mean_Median_Mode.ppthhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...Mean_Median_Mode.ppthhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...
Mean_Median_Mode.ppthhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...
JuliusRomano3
 
Module 3 statistics
Module 3   statisticsModule 3   statistics
Module 3 statistics
dionesioable
 
Descriptive Statistics.pptx
Descriptive Statistics.pptxDescriptive Statistics.pptx
Descriptive Statistics.pptx
fatinhidayah14
 
Analysis of students’ performance
Analysis of students’ performanceAnalysis of students’ performance
Analysis of students’ performance
Gautam Kumar
 
Mod mean quartile
Mod mean quartileMod mean quartile
Mod mean quartile
Maher Faisal Razi
 
Lecture 3 & 4 Measure of Central Tendency.pdf
Lecture 3 & 4 Measure of Central Tendency.pdfLecture 3 & 4 Measure of Central Tendency.pdf
Lecture 3 & 4 Measure of Central Tendency.pdf
kelashraisal
 
analytical representation of data
 analytical representation of data analytical representation of data
analytical representation of data
Unsa Shakir
 
Group 3 measures of central tendency and variation - (mean, median, mode, ra...
Group 3  measures of central tendency and variation - (mean, median, mode, ra...Group 3  measures of central tendency and variation - (mean, median, mode, ra...
Group 3 measures of central tendency and variation - (mean, median, mode, ra...
reymartyvette_0611
 
Measures of Central Tendency.ppt
Measures of Central Tendency.pptMeasures of Central Tendency.ppt
Measures of Central Tendency.ppt
AdamRayManlunas1
 
Measures of central tendency and dispersion
Measures of central tendency and dispersionMeasures of central tendency and dispersion
Measures of central tendency and dispersion
Abhinav yadav
 
3Measurements of health and disease_MCTD.pdf
3Measurements of health and disease_MCTD.pdf3Measurements of health and disease_MCTD.pdf
3Measurements of health and disease_MCTD.pdf
AmanuelDina
 
Measures of Central Tendency - Biostatstics
Measures of Central Tendency - BiostatsticsMeasures of Central Tendency - Biostatstics
Measures of Central Tendency - Biostatstics
Harshit Jadav
 
Basics of Stats (2).pptx
Basics of Stats (2).pptxBasics of Stats (2).pptx
Basics of Stats (2).pptx
madihamaqbool6
 

Similar to Descriptive statistics (20)

5.DATA SUMMERISATION.ppt
5.DATA SUMMERISATION.ppt5.DATA SUMMERISATION.ppt
5.DATA SUMMERISATION.ppt
 
STATISTICS AND PROBABILITY.pptx
STATISTICS AND PROBABILITY.pptxSTATISTICS AND PROBABILITY.pptx
STATISTICS AND PROBABILITY.pptx
 
Descriptive Statistics: Mean, Median Mode and Standard Deviation.
Descriptive Statistics: Mean, Median Mode and Standard Deviation.Descriptive Statistics: Mean, Median Mode and Standard Deviation.
Descriptive Statistics: Mean, Median Mode and Standard Deviation.
 
SMDM Presentation.pptx
SMDM Presentation.pptxSMDM Presentation.pptx
SMDM Presentation.pptx
 
CABT Math 8 measures of central tendency and dispersion
CABT Math 8   measures of central tendency and dispersionCABT Math 8   measures of central tendency and dispersion
CABT Math 8 measures of central tendency and dispersion
 
Lesson 23 planning data analyses using statistics
Lesson 23 planning data analyses using statisticsLesson 23 planning data analyses using statistics
Lesson 23 planning data analyses using statistics
 
Topic 8a Basic Statistics
Topic 8a Basic StatisticsTopic 8a Basic Statistics
Topic 8a Basic Statistics
 
Mean_Median_Mode.ppthhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...
Mean_Median_Mode.ppthhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...Mean_Median_Mode.ppthhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...
Mean_Median_Mode.ppthhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...
 
Module 3 statistics
Module 3   statisticsModule 3   statistics
Module 3 statistics
 
Descriptive Statistics.pptx
Descriptive Statistics.pptxDescriptive Statistics.pptx
Descriptive Statistics.pptx
 
Analysis of students’ performance
Analysis of students’ performanceAnalysis of students’ performance
Analysis of students’ performance
 
Mod mean quartile
Mod mean quartileMod mean quartile
Mod mean quartile
 
Lecture 3 & 4 Measure of Central Tendency.pdf
Lecture 3 & 4 Measure of Central Tendency.pdfLecture 3 & 4 Measure of Central Tendency.pdf
Lecture 3 & 4 Measure of Central Tendency.pdf
 
analytical representation of data
 analytical representation of data analytical representation of data
analytical representation of data
 
Group 3 measures of central tendency and variation - (mean, median, mode, ra...
Group 3  measures of central tendency and variation - (mean, median, mode, ra...Group 3  measures of central tendency and variation - (mean, median, mode, ra...
Group 3 measures of central tendency and variation - (mean, median, mode, ra...
 
Measures of Central Tendency.ppt
Measures of Central Tendency.pptMeasures of Central Tendency.ppt
Measures of Central Tendency.ppt
 
Measures of central tendency and dispersion
Measures of central tendency and dispersionMeasures of central tendency and dispersion
Measures of central tendency and dispersion
 
3Measurements of health and disease_MCTD.pdf
3Measurements of health and disease_MCTD.pdf3Measurements of health and disease_MCTD.pdf
3Measurements of health and disease_MCTD.pdf
 
Measures of Central Tendency - Biostatstics
Measures of Central Tendency - BiostatsticsMeasures of Central Tendency - Biostatstics
Measures of Central Tendency - Biostatstics
 
Basics of Stats (2).pptx
Basics of Stats (2).pptxBasics of Stats (2).pptx
Basics of Stats (2).pptx
 

Recently uploaded

Tutorial for 16S rRNA Gene Analysis with QIIME2.pdf
Tutorial for 16S rRNA Gene Analysis with QIIME2.pdfTutorial for 16S rRNA Gene Analysis with QIIME2.pdf
Tutorial for 16S rRNA Gene Analysis with QIIME2.pdf
aqil azizi
 
HYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generationHYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generation
Robbie Edward Sayers
 
Building Electrical System Design & Installation
Building Electrical System Design & InstallationBuilding Electrical System Design & Installation
Building Electrical System Design & Installation
symbo111
 
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
ssuser7dcef0
 
DESIGN A COTTON SEED SEPARATION MACHINE.docx
DESIGN A COTTON SEED SEPARATION MACHINE.docxDESIGN A COTTON SEED SEPARATION MACHINE.docx
DESIGN A COTTON SEED SEPARATION MACHINE.docx
FluxPrime1
 
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
MdTanvirMahtab2
 
space technology lecture notes on satellite
space technology lecture notes on satellitespace technology lecture notes on satellite
space technology lecture notes on satellite
ongomchris
 
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
bakpo1
 
DfMAy 2024 - key insights and contributions
DfMAy 2024 - key insights and contributionsDfMAy 2024 - key insights and contributions
DfMAy 2024 - key insights and contributions
gestioneergodomus
 
Immunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary AttacksImmunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary Attacks
gerogepatton
 
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdfTop 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
Teleport Manpower Consultant
 
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
ClaraZara1
 
AP LAB PPT.pdf ap lab ppt no title specific
AP LAB PPT.pdf ap lab ppt no title specificAP LAB PPT.pdf ap lab ppt no title specific
AP LAB PPT.pdf ap lab ppt no title specific
BrazilAccount1
 
Investor-Presentation-Q1FY2024 investor presentation document.pptx
Investor-Presentation-Q1FY2024 investor presentation document.pptxInvestor-Presentation-Q1FY2024 investor presentation document.pptx
Investor-Presentation-Q1FY2024 investor presentation document.pptx
AmarGB2
 
Standard Reomte Control Interface - Neometrix
Standard Reomte Control Interface - NeometrixStandard Reomte Control Interface - Neometrix
Standard Reomte Control Interface - Neometrix
Neometrix_Engineering_Pvt_Ltd
 
Hierarchical Digital Twin of a Naval Power System
Hierarchical Digital Twin of a Naval Power SystemHierarchical Digital Twin of a Naval Power System
Hierarchical Digital Twin of a Naval Power System
Kerry Sado
 
block diagram and signal flow graph representation
block diagram and signal flow graph representationblock diagram and signal flow graph representation
block diagram and signal flow graph representation
Divya Somashekar
 
Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024
Massimo Talia
 
Forklift Classes Overview by Intella Parts
Forklift Classes Overview by Intella PartsForklift Classes Overview by Intella Parts
Forklift Classes Overview by Intella Parts
Intella Parts
 
14 Template Contractual Notice - EOT Application
14 Template Contractual Notice - EOT Application14 Template Contractual Notice - EOT Application
14 Template Contractual Notice - EOT Application
SyedAbiiAzazi1
 

Recently uploaded (20)

Tutorial for 16S rRNA Gene Analysis with QIIME2.pdf
Tutorial for 16S rRNA Gene Analysis with QIIME2.pdfTutorial for 16S rRNA Gene Analysis with QIIME2.pdf
Tutorial for 16S rRNA Gene Analysis with QIIME2.pdf
 
HYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generationHYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generation
 
Building Electrical System Design & Installation
Building Electrical System Design & InstallationBuilding Electrical System Design & Installation
Building Electrical System Design & Installation
 
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
 
DESIGN A COTTON SEED SEPARATION MACHINE.docx
DESIGN A COTTON SEED SEPARATION MACHINE.docxDESIGN A COTTON SEED SEPARATION MACHINE.docx
DESIGN A COTTON SEED SEPARATION MACHINE.docx
 
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
 
space technology lecture notes on satellite
space technology lecture notes on satellitespace technology lecture notes on satellite
space technology lecture notes on satellite
 
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
 
DfMAy 2024 - key insights and contributions
DfMAy 2024 - key insights and contributionsDfMAy 2024 - key insights and contributions
DfMAy 2024 - key insights and contributions
 
Immunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary AttacksImmunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary Attacks
 
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdfTop 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
 
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
 
AP LAB PPT.pdf ap lab ppt no title specific
AP LAB PPT.pdf ap lab ppt no title specificAP LAB PPT.pdf ap lab ppt no title specific
AP LAB PPT.pdf ap lab ppt no title specific
 
Investor-Presentation-Q1FY2024 investor presentation document.pptx
Investor-Presentation-Q1FY2024 investor presentation document.pptxInvestor-Presentation-Q1FY2024 investor presentation document.pptx
Investor-Presentation-Q1FY2024 investor presentation document.pptx
 
Standard Reomte Control Interface - Neometrix
Standard Reomte Control Interface - NeometrixStandard Reomte Control Interface - Neometrix
Standard Reomte Control Interface - Neometrix
 
Hierarchical Digital Twin of a Naval Power System
Hierarchical Digital Twin of a Naval Power SystemHierarchical Digital Twin of a Naval Power System
Hierarchical Digital Twin of a Naval Power System
 
block diagram and signal flow graph representation
block diagram and signal flow graph representationblock diagram and signal flow graph representation
block diagram and signal flow graph representation
 
Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024
 
Forklift Classes Overview by Intella Parts
Forklift Classes Overview by Intella PartsForklift Classes Overview by Intella Parts
Forklift Classes Overview by Intella Parts
 
14 Template Contractual Notice - EOT Application
14 Template Contractual Notice - EOT Application14 Template Contractual Notice - EOT Application
14 Template Contractual Notice - EOT Application
 

Descriptive statistics

  • 3. Central Tendency • Mean is the average or arithmetic mean of the data. • Median is the middle value of the data set, when the data set is arranged in the ascending or descending order. • Mode is the most frequently observed value(s).
  • 4. • Range is the difference between highest and lowest observations in the data set • Variance is a measure of spread of a data set It is calculated as the average squared deviation of each number from mean of a data set. • Standard Deviation is the Square Root of the Variance. Dispersion
  • 6. The marks obtained by 10 students in a subject are as given below 9,31,35,34,37,32,100,33,30,100 Average Marks: =(9+31+35+34+37+32+100+33+30+100)/10 =44 Effect of outliers on Mean
  • 7. The marks obtained by 8 students in a subject are as given below 31,35,34,37,32,38,33,30 Average Marks: =(31+35+34+37+32+38+33+30)/8 =33.75 Mean
  • 8. The marks obtained by 10 students in a subject are as given below 9,31,35,34,37,32,100,33,30,100 Average Marks: =(9+31+35+34+37+32+100+33+30+100)/10 =44.1 Effect of outliers on Mean
  • 9. • The middle value when a variable’s values are ranked in ascending/ descending order If n is odd; Median is ((n-1)/2)+1th value If n is even; Median is Average of {(n/2), (n/2)+1} values Median
  • 10. Median = 60 (six cases above, six below) Marks scored by 13 students in an exam is as give below. Find the median marks 35,90,40,62,54,95,38,60,73,92,51,32,74 Median 32 35 38 40 51 54 60 62 73 74 90 92 95 When “n” is odd
  • 11. Median = (60+64) /2 = 62 (Average of 5th and 6th values) Marks scored by 10 students in an exam is as give below. Find the median marks 38,84,42,64,55,96,39,60,73,92 Median 38 39 42 55 60 64 73 84 92 96 When “n” is even
  • 12. The marks obtained by 10 students in a subject are as given below 9,31,35,34,37,32,100,33,30,100 Median: 9,30,31,32,33,34,35,37,100,100 Effect of outliers on Median = (33+34)/2 =33.5 Median is not influenced by outliers
  • 13. The mode for a data set is the element that occurs the most often. More than one mode is possible for a data set. It is also possible that a data set may not have a mode at all. Example-1: Data set: 2,5,4,7,8,9,4,3,6,4,7,8,6,4,9,2 Mode: 4 Example-2: Data set: 10,25,15,30,25,45,50,15,40,35 Modes: 25, 15 Mode
  • 14. Range Range = Largest value – Smallest value The spread, or the difference, between the lowest and highest values of a variable. 31,35,34,37,32,38,33,30 Data set Range = 38 - 30 = 8
  • 15. Variance A measure of the spread of the recorded values on a variable. The larger the variance, the further the individual values are from the mean. The smaller the variance, the closer the individual values are to the mean. Mean Mean
  • 16. The square root of the variance (Standard deviation) reveals the average deviation of the observations from the mean. Standard deviation   1 1 2     n xx s n i i
  • 17. • The larger the standard deviation, the more spread out the data is. Interpretation of standard deviation