SlideShare a Scribd company logo
 Mode:
The most repeated or most common or the
most frequent Value which occurs in a set of
values.
The mode is the value in a data set that
occur most frequently.
A set of data can have more than one mode
if two or more values tie for the most
frequently occurring value.
 Collect the sample data.
a sample of 20 group was selected at
random.
2,4,1,2,3,2,4,2,3,6,8,4,2,1,7,4,2,4,4,3.
“most repeated value or values” Mode = 2,4
X Frequency
1 2
2 6
3 3
4 6
5 0
6 1
7 1
8 1
 For group data
Mode = l + (fm – f1) * h
(fm- f1) + (fm –f2)
here
l = lower limit class boundary of the mode
group
fm= maximum frequency
f1 = frequency preceding the fm
f2 = frequency following the fm
h = class interval
Marks F Class boundary
30 - 39 2 29.5 – 39.5
40 – 49 3 39.5 – 49.5
50 – 59 11 49.5 – 59.5
60 – 69 20 f1 59.5 – 69.5
70 – 79 32 fm l 69.5 – 79.5 mode
group
80- 89 25 f2 79.5 – 89.5
90 - 99 7 90.5 – 99.5
Mode = l + (fm – f1) * h
(fm- f1) + (fm –f2)
= 69.5 + ( 32 – 20) * 10
(32 – 20) + (32 – 25)
= 69.5 + 120
12+7
mode = 69.5 + 6.32 = 75.82
 Advantages:
1. It is easy and quick to calculate.
2. It is easy to understand.
3. Extreme values do not effect its values.
4. It can be determined from open end distribution.
5. It can be found by inspection from ungroup data.
6. It can be used for qualitative data.
 Disadvantage:
1. It is not well defined.
2. It is not based on all the observation of a set of
data.
3. It can not be used for further mathematical
processing.
4. There may be more then one value of the mode
in the set of data.
5. There may be no mode, if there is no common
value in the data.
 Weighted Mean:
weighted mean is a special case of arithmetic mean.
The mean value of data values that have been weighted
according to their relative importance.
 when the value are not equal importance, we assign
them certain numerical values to express their relative
importance. These numerical values are called
weights.
 Weighted mean =  WX / W
 The marks obtained by a students in English, Urdu
and Statistics were 70, 76 and 82 respectively.
Find the suitable average if weights of 5, 4 and 3
are assigned to these subjects.
 Weighted mean =  WX / W
= 5*70+4*76+3*82
5+4+3
= 900/12 = 75
 Empirical Relationship among Mean, Median and
Mode.
1. If Mean = Median = Mode then distribution is
symmetrical.
2. If Mean > Median > Mode then distribution is
positively skewed.
3. If Mean < Median < Mode then distribution is
negatively Skewed.

More Related Content

Similar to Lecture Slides (1).pptx

2.3
2.32.3
3. Mean__Median__Mode__Range.ppt
3. Mean__Median__Mode__Range.ppt3. Mean__Median__Mode__Range.ppt
3. Mean__Median__Mode__Range.ppt
ABDULRAUF411
 
stat.ppt
stat.pptstat.ppt
Mean__Median__Mode__Range.ppt
Mean__Median__Mode__Range.pptMean__Median__Mode__Range.ppt
Mean__Median__Mode__Range.ppt
trader33
 
Measures of central tendency
Measures of central tendencyMeasures of central tendency
Measures of central tendency
Srinivasan Padmanaban
 
Mean, Median, Mode and Range Central Tendency.pptx
Mean, Median, Mode and Range Central Tendency.pptxMean, Median, Mode and Range Central Tendency.pptx
Mean, Median, Mode and Range Central Tendency.pptx
YanieSilao
 
Measures in Statistics. kjc.pptx
Measures in Statistics. kjc.pptxMeasures in Statistics. kjc.pptx
Measures in Statistics. kjc.pptx
Neerupa Chauhan
 
Stattistic ii - mode, median, mean
Stattistic ii - mode, median, meanStattistic ii - mode, median, mean
Stattistic ii - mode, median, mean
amsy1224
 
Problems on Mean,Mode,Median Standard Deviation
Problems on Mean,Mode,Median Standard DeviationProblems on Mean,Mode,Median Standard Deviation
Chapter 4
Chapter 4Chapter 4
Chapter 4
Lem Lem
 
Module 3 statistics
Module 3   statisticsModule 3   statistics
Module 3 statistics
dionesioable
 
Measures of central tendency median mode
Measures of central tendency median modeMeasures of central tendency median mode
Measures of central tendency median mode
RekhaChoudhary24
 
Chapter 3 260110 044503
Chapter 3 260110 044503Chapter 3 260110 044503
Chapter 3 260110 044503
guest25d353
 
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
Gilbert Joseph Abueg
 
3. measures of central tendency
3. measures of central tendency3. measures of central tendency
3. measures of central tendency
renz50
 
Data Processing and Statistical Treatment.pptx
Data Processing and Statistical Treatment.pptxData Processing and Statistical Treatment.pptx
Data Processing and Statistical Treatment.pptx
VamPagauraAlvarado
 
Mod mean quartile
Mod mean quartileMod mean quartile
Mod mean quartile
Maher Faisal Razi
 
Measures of Central tendency
Measures of Central tendencyMeasures of Central tendency
Measures of Central tendency
Sir Parashurambhau College, Pune
 
3 descritive statistics measure of central tendency variatio
3 descritive statistics measure of   central   tendency variatio3 descritive statistics measure of   central   tendency variatio
3 descritive statistics measure of central tendency variatio
Lama K Banna
 
Mode
ModeMode

Similar to Lecture Slides (1).pptx (20)

2.3
2.32.3
2.3
 
3. Mean__Median__Mode__Range.ppt
3. Mean__Median__Mode__Range.ppt3. Mean__Median__Mode__Range.ppt
3. Mean__Median__Mode__Range.ppt
 
stat.ppt
stat.pptstat.ppt
stat.ppt
 
Mean__Median__Mode__Range.ppt
Mean__Median__Mode__Range.pptMean__Median__Mode__Range.ppt
Mean__Median__Mode__Range.ppt
 
Measures of central tendency
Measures of central tendencyMeasures of central tendency
Measures of central tendency
 
Mean, Median, Mode and Range Central Tendency.pptx
Mean, Median, Mode and Range Central Tendency.pptxMean, Median, Mode and Range Central Tendency.pptx
Mean, Median, Mode and Range Central Tendency.pptx
 
Measures in Statistics. kjc.pptx
Measures in Statistics. kjc.pptxMeasures in Statistics. kjc.pptx
Measures in Statistics. kjc.pptx
 
Stattistic ii - mode, median, mean
Stattistic ii - mode, median, meanStattistic ii - mode, median, mean
Stattistic ii - mode, median, mean
 
Problems on Mean,Mode,Median Standard Deviation
Problems on Mean,Mode,Median Standard DeviationProblems on Mean,Mode,Median Standard Deviation
Problems on Mean,Mode,Median Standard Deviation
 
Chapter 4
Chapter 4Chapter 4
Chapter 4
 
Module 3 statistics
Module 3   statisticsModule 3   statistics
Module 3 statistics
 
Measures of central tendency median mode
Measures of central tendency median modeMeasures of central tendency median mode
Measures of central tendency median mode
 
Chapter 3 260110 044503
Chapter 3 260110 044503Chapter 3 260110 044503
Chapter 3 260110 044503
 
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
 
3. measures of central tendency
3. measures of central tendency3. measures of central tendency
3. measures of central tendency
 
Data Processing and Statistical Treatment.pptx
Data Processing and Statistical Treatment.pptxData Processing and Statistical Treatment.pptx
Data Processing and Statistical Treatment.pptx
 
Mod mean quartile
Mod mean quartileMod mean quartile
Mod mean quartile
 
Measures of Central tendency
Measures of Central tendencyMeasures of Central tendency
Measures of Central tendency
 
3 descritive statistics measure of central tendency variatio
3 descritive statistics measure of   central   tendency variatio3 descritive statistics measure of   central   tendency variatio
3 descritive statistics measure of central tendency variatio
 
Mode
ModeMode
Mode
 

Recently uploaded

LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPLAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
RAHUL
 
Hindi varnamala | hindi alphabet PPT.pdf
Hindi varnamala | hindi alphabet PPT.pdfHindi varnamala | hindi alphabet PPT.pdf
Hindi varnamala | hindi alphabet PPT.pdf
Dr. Mulla Adam Ali
 
Wound healing PPT
Wound healing PPTWound healing PPT
Wound healing PPT
Jyoti Chand
 
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptxChapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Denish Jangid
 
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
GeorgeMilliken2
 
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
Nguyen Thanh Tu Collection
 
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumPhilippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
MJDuyan
 
How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17
Celine George
 
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
PECB
 
ZK on Polkadot zero knowledge proofs - sub0.pptx
ZK on Polkadot zero knowledge proofs - sub0.pptxZK on Polkadot zero knowledge proofs - sub0.pptx
ZK on Polkadot zero knowledge proofs - sub0.pptx
dot55audits
 
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem studentsRHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Himanshu Rai
 
IGCSE Biology Chapter 14- Reproduction in Plants.pdf
IGCSE Biology Chapter 14- Reproduction in Plants.pdfIGCSE Biology Chapter 14- Reproduction in Plants.pdf
IGCSE Biology Chapter 14- Reproduction in Plants.pdf
Amin Marwan
 
writing about opinions about Australia the movie
writing about opinions about Australia the moviewriting about opinions about Australia the movie
writing about opinions about Australia the movie
Nicholas Montgomery
 
Bed Making ( Introduction, Purpose, Types, Articles, Scientific principles, N...
Bed Making ( Introduction, Purpose, Types, Articles, Scientific principles, N...Bed Making ( Introduction, Purpose, Types, Articles, Scientific principles, N...
Bed Making ( Introduction, Purpose, Types, Articles, Scientific principles, N...
Leena Ghag-Sakpal
 
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
Nguyen Thanh Tu Collection
 
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingYour Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Excellence Foundation for South Sudan
 
BBR 2024 Summer Sessions Interview Training
BBR  2024 Summer Sessions Interview TrainingBBR  2024 Summer Sessions Interview Training
BBR 2024 Summer Sessions Interview Training
Katrina Pritchard
 
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
MysoreMuleSoftMeetup
 
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptxPrésentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
siemaillard
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
Nguyen Thanh Tu Collection
 

Recently uploaded (20)

LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPLAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
 
Hindi varnamala | hindi alphabet PPT.pdf
Hindi varnamala | hindi alphabet PPT.pdfHindi varnamala | hindi alphabet PPT.pdf
Hindi varnamala | hindi alphabet PPT.pdf
 
Wound healing PPT
Wound healing PPTWound healing PPT
Wound healing PPT
 
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptxChapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptx
 
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
 
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
 
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumPhilippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
 
How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17
 
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
 
ZK on Polkadot zero knowledge proofs - sub0.pptx
ZK on Polkadot zero knowledge proofs - sub0.pptxZK on Polkadot zero knowledge proofs - sub0.pptx
ZK on Polkadot zero knowledge proofs - sub0.pptx
 
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem studentsRHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
 
IGCSE Biology Chapter 14- Reproduction in Plants.pdf
IGCSE Biology Chapter 14- Reproduction in Plants.pdfIGCSE Biology Chapter 14- Reproduction in Plants.pdf
IGCSE Biology Chapter 14- Reproduction in Plants.pdf
 
writing about opinions about Australia the movie
writing about opinions about Australia the moviewriting about opinions about Australia the movie
writing about opinions about Australia the movie
 
Bed Making ( Introduction, Purpose, Types, Articles, Scientific principles, N...
Bed Making ( Introduction, Purpose, Types, Articles, Scientific principles, N...Bed Making ( Introduction, Purpose, Types, Articles, Scientific principles, N...
Bed Making ( Introduction, Purpose, Types, Articles, Scientific principles, N...
 
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
 
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingYour Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
 
BBR 2024 Summer Sessions Interview Training
BBR  2024 Summer Sessions Interview TrainingBBR  2024 Summer Sessions Interview Training
BBR 2024 Summer Sessions Interview Training
 
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
 
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptxPrésentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
 

Lecture Slides (1).pptx

  • 1.  Mode: The most repeated or most common or the most frequent Value which occurs in a set of values. The mode is the value in a data set that occur most frequently. A set of data can have more than one mode if two or more values tie for the most frequently occurring value.
  • 2.  Collect the sample data. a sample of 20 group was selected at random. 2,4,1,2,3,2,4,2,3,6,8,4,2,1,7,4,2,4,4,3. “most repeated value or values” Mode = 2,4 X Frequency 1 2 2 6 3 3 4 6 5 0 6 1 7 1 8 1
  • 3.  For group data Mode = l + (fm – f1) * h (fm- f1) + (fm –f2) here l = lower limit class boundary of the mode group fm= maximum frequency f1 = frequency preceding the fm f2 = frequency following the fm h = class interval
  • 4. Marks F Class boundary 30 - 39 2 29.5 – 39.5 40 – 49 3 39.5 – 49.5 50 – 59 11 49.5 – 59.5 60 – 69 20 f1 59.5 – 69.5 70 – 79 32 fm l 69.5 – 79.5 mode group 80- 89 25 f2 79.5 – 89.5 90 - 99 7 90.5 – 99.5
  • 5. Mode = l + (fm – f1) * h (fm- f1) + (fm –f2) = 69.5 + ( 32 – 20) * 10 (32 – 20) + (32 – 25) = 69.5 + 120 12+7 mode = 69.5 + 6.32 = 75.82
  • 6.  Advantages: 1. It is easy and quick to calculate. 2. It is easy to understand. 3. Extreme values do not effect its values. 4. It can be determined from open end distribution. 5. It can be found by inspection from ungroup data. 6. It can be used for qualitative data.
  • 7.  Disadvantage: 1. It is not well defined. 2. It is not based on all the observation of a set of data. 3. It can not be used for further mathematical processing. 4. There may be more then one value of the mode in the set of data. 5. There may be no mode, if there is no common value in the data.
  • 8.  Weighted Mean: weighted mean is a special case of arithmetic mean. The mean value of data values that have been weighted according to their relative importance.  when the value are not equal importance, we assign them certain numerical values to express their relative importance. These numerical values are called weights.  Weighted mean =  WX / W
  • 9.  The marks obtained by a students in English, Urdu and Statistics were 70, 76 and 82 respectively. Find the suitable average if weights of 5, 4 and 3 are assigned to these subjects.  Weighted mean =  WX / W = 5*70+4*76+3*82 5+4+3 = 900/12 = 75
  • 10.  Empirical Relationship among Mean, Median and Mode. 1. If Mean = Median = Mode then distribution is symmetrical. 2. If Mean > Median > Mode then distribution is positively skewed. 3. If Mean < Median < Mode then distribution is negatively Skewed.