SlideShare a Scribd company logo
Copyright © 2012 Statisticshelpdesk.com, All rights reserved
Statistics Homework Help
Statistics Assignment Help | Help with Statistics Assignment
1/16/2015
Statisticshelpdesk
Alex Gerg
Contact Us:
Statistics Help Desk
Email: info@statisticshelpdesk.com
Web: http://www.statisticshelpdesk.com
Tel: +44-793-744-3379
Follow Us:
https://www.facebook.com/Statshelpdesk
http://twitter.com/statshelpdesk
http://statistics-help-homework.blogspot.com/
Copyright © 2012 Statisticshelpdesk.com, All rights reserved
Copyright © 2012 Statisticshelpdesk.com, All rights reserved
About Statistics:
Statisticshelpdesk provides solution to all kind of problems related to
statistics, as it is a dedicated site of statistics so it provides all sorts of
help i.e Assignment help, Homework help, Project help, Dissertation
help, Live online tutoring, Statistical analysis, All sorts of software help
that are being used in statistics.
Statistics homework help will be viewed in numerous ways that.
Normally it's thought-about to subsume graphs, charts, percentages,
and averages. It consists of rules and strategies of collecting and presenting numerical data. It
conjointly consists of constructing inferences from a given knowledge. The statistical data will be
used to elucidate unexplained things, to form and justify a claim, to form comparisons, to seek
out unknown quantities, to predict data regarding future and to ascertain relationship between
quantities. Thus, it's a subject matter that consists of quite numbers.
Statistics Sample Questions and Solutions
ILLUSTRATION 1.
From the following data determine by Sturge’s rule the number of classes to be formed and the
interval of each such classes :
Number of observation = 60
Value of largest observation = 95
Value of the smallest observation = 5
SOLUTION:
According to struges rule the number of classes to be formed is given by
K(s) =1+3.322log N
= 1+3.322 log 60
= 1+1.322(1.7782) = 1+5.9072.
Further, according to the said rule, the size of classes interval for each of the classes is given by
i(s) =
L−S
K(s)
95−5
7
=
90
7
= 13 approx
Thus, the number of classes to be formed is 7 and the classes interval of each such class should
be 13.
Copyright © 2012 Statisticshelpdesk.com, All rights reserved
(iv) Class boundary:- Class boundaries or class walls mean the two extreme values of a class
to which the data belonging to the said class cannot cross or exceeds either way. He lower of
the two is called the lower boundary and the higher of the two id called the upper boundary of
the class. In class of exclusive classification, all the values of the class remain below the upper
boundary but in case of inclusive classification, all the values of a class remain within the two
boundaries ending with the upper one .the following example will indicate the class boundaries
under each of the two types of classification.
Example: Class Boundaries
In case of exclusive classes In case of exclusive classes
Lower boundary Upper boundary Lower boundary Upper boundary
0-10
10-20
20-30
30-40
40-50
0-9
10-19
20-29
30-39
40-49
Copyright © 2012 Statisticshelpdesk.com, All rights reserved
(v) Class limits. Class limits mean the two extreme values of a class within which all the values
of the class remain. The lower value of the class is called the lower limit and the upper value
below which all the data remain is called the upper limit of the class. the lower limit is designed
by L1 while the upper limit by L2 in case of exclusive classification the class limit are equal to
the class boundaries explained above. But in case of inclusive classification, the class limits cross
the class boundaries in both the directions. in such a case, the lower limit of a class is
determined by subtracting from the lower boundary of the class half of the difference between
the last significant digit of the upper boundary of the class and the last significant digit of the
lower boundary of the next class. Similarly, the upper limit of the class is determined by adding.
to the upper boundary of the class , half the difference between the last integer of the upper
boundary of the class and last integer of the lower boundary of the next class.
Symbolically, the lower limit and the upper limit of the inclusive class can be computed as
under:
Where L1 = lower limit of a class , L2 = upper limit of a class , B1=lower boundary of a class ,
B2 = upper boundary of a class , and d = difference between the last significant digit of the
upper boundary of the class and the last significant digit of the lower boundary of the next class.
In the above manner, in inclusive series is converted into an exclusive one for determining the
values of certain measures viz, median, quartiles, percentiles, mode etc . That depend upon the
exact class limits of the respective classes.
The following examples will show the class limits of the different classes formed under both the
exclusive and inclusive methods:
Example1:
L1=B1-
1
2
𝑑
and
L2 = B2+-
1
2
𝑑
Exclusive Classes Inclusive Classes
Classes Class-Limits Classes Class-Limits
B1 B2 L1 L2 B1 B2 L1 L2
0-10
10-20
20-25.5
25.5-30.5
30.5-35.5
0
10
20
25.5
30.5
10
20
25.5
30.5
35.5
0-9
10-19
20-29
30-39
40-49
-.5
9.5
19.5
29.5
39.5
9.5
19.5
29.5
39.5
49.5
Copyright © 2012 Statisticshelpdesk.com, All rights reserved
Example 2:
Inclusive Series I Inclusive Classes II
Classes Class-Limits Classes Classes-Limits
B1 B2 L1 L2 B1 B2 L1 L2
10-19.9
20-29.9
30-39.9
40-49.9
50.59.9
9.95
19.95
29.95
39.95
49.95
19.95
29.95
39.95
49.95
59.95
10-14.95
15-19.95
20-24.95
25-29.95
30-34.95
9.975
14.975
19.975
24.975
29.975
14.975
19.975
24.975
29.975
34.975
(vi) Mid-Value or Class Mark: The value that lies at the center of a class is called the mid –
value , mid-point or class mark of the said class. It is computed
M =
L1+L2
2
=
B1+B2
2
Where M= mid-value,
L1 and L2=Lower and upper limits of the class respectively,
And B1 and B2=lower and upper boundary of the class respectively.
Thus the mid-value of the class (10-20) =
10+20
2
=15 and that of (10-19) =
10+19
2
=14.5

More Related Content

Similar to Statistics Homework Help

Basic statistics for marketing management
Basic statistics for marketing managementBasic statistics for marketing management
Basic statistics for marketing management
mukeremm25
 
Algorithms
AlgorithmsAlgorithms
Algorithms
DrHiyamHatem
 
Quantitative Method.pptx
Quantitative Method.pptxQuantitative Method.pptx
Quantitative Method.pptx
CHANJAYVASQUEZ
 
Topic 2 tabular presentation
Topic 2 tabular presentationTopic 2 tabular presentation
Topic 2 tabular presentation
Alicia Goh
 
Estimation and confidence interval
Estimation and confidence intervalEstimation and confidence interval
Estimation and confidence interval
Homework Guru
 
Dee 2034 chapter 1 number and code system (Baia)
Dee 2034 chapter 1 number and code system (Baia)Dee 2034 chapter 1 number and code system (Baia)
Dee 2034 chapter 1 number and code system (Baia)
SITI SABARIAH SALIHIN
 
measure of dispersion
measure of dispersion measure of dispersion
measure of dispersion
som allul
 
Statistics and probability lesson6&7
Statistics and probability lesson6&7Statistics and probability lesson6&7
Statistics and probability lesson6&7
MARIA CHRISTITA POLINAG
 
Contextual ontology alignment may 2011
Contextual ontology alignment may 2011Contextual ontology alignment may 2011
Contextual ontology alignment may 2011
Mariana Damova, Ph.D
 
Test Bank Statistics for Business and Economics 8th edition by Carlson & Thor...
Test Bank Statistics for Business and Economics 8th edition by Carlson & Thor...Test Bank Statistics for Business and Economics 8th edition by Carlson & Thor...
Test Bank Statistics for Business and Economics 8th edition by Carlson & Thor...
bussinessw959
 
Linear discriminant analysis: an overview
Linear discriminant analysis: an overviewLinear discriminant analysis: an overview
Linear discriminant analysis: an overview
Alaa Tharwat
 
Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...
Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...
Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...
Chaitali Dongaonkar
 
Software Measurement: Lecture 1. Measures and Metrics
Software Measurement: Lecture 1. Measures and MetricsSoftware Measurement: Lecture 1. Measures and Metrics
Software Measurement: Lecture 1. Measures and Metrics
Programeter
 
Frequency Distribution Table Handout
Frequency Distribution Table HandoutFrequency Distribution Table Handout
Frequency Distribution Table Handout
sheisirenebkm
 
Measures and Strengths of AssociationRemember that while w.docx
Measures and Strengths of AssociationRemember that while w.docxMeasures and Strengths of AssociationRemember that while w.docx
Measures and Strengths of AssociationRemember that while w.docx
ARIV4
 
Day 4 normal curve and standard scores
Day 4 normal curve and standard scoresDay 4 normal curve and standard scores
Day 4 normal curve and standard scores
Elih Sutisna Yanto
 
STATISTICS AND PROBABILITY.pptx
STATISTICS AND PROBABILITY.pptxSTATISTICS AND PROBABILITY.pptx
STATISTICS AND PROBABILITY.pptx
Bharathiar University
 
Scaling Z-scores T-scores C-scores
Scaling Z-scores T-scores C-scoresScaling Z-scores T-scores C-scores
Scaling Z-scores T-scores C-scores
Surbhi Sharma
 
Ordinal logistic regression
Ordinal logistic regression Ordinal logistic regression
Ordinal logistic regression
Dr Athar Khan
 
Tabulation of Data, Frequency Distribution, Contingency table
Tabulation of Data, Frequency Distribution, Contingency tableTabulation of Data, Frequency Distribution, Contingency table
Tabulation of Data, Frequency Distribution, Contingency table
Jagdish Powar
 

Similar to Statistics Homework Help (20)

Basic statistics for marketing management
Basic statistics for marketing managementBasic statistics for marketing management
Basic statistics for marketing management
 
Algorithms
AlgorithmsAlgorithms
Algorithms
 
Quantitative Method.pptx
Quantitative Method.pptxQuantitative Method.pptx
Quantitative Method.pptx
 
Topic 2 tabular presentation
Topic 2 tabular presentationTopic 2 tabular presentation
Topic 2 tabular presentation
 
Estimation and confidence interval
Estimation and confidence intervalEstimation and confidence interval
Estimation and confidence interval
 
Dee 2034 chapter 1 number and code system (Baia)
Dee 2034 chapter 1 number and code system (Baia)Dee 2034 chapter 1 number and code system (Baia)
Dee 2034 chapter 1 number and code system (Baia)
 
measure of dispersion
measure of dispersion measure of dispersion
measure of dispersion
 
Statistics and probability lesson6&7
Statistics and probability lesson6&7Statistics and probability lesson6&7
Statistics and probability lesson6&7
 
Contextual ontology alignment may 2011
Contextual ontology alignment may 2011Contextual ontology alignment may 2011
Contextual ontology alignment may 2011
 
Test Bank Statistics for Business and Economics 8th edition by Carlson & Thor...
Test Bank Statistics for Business and Economics 8th edition by Carlson & Thor...Test Bank Statistics for Business and Economics 8th edition by Carlson & Thor...
Test Bank Statistics for Business and Economics 8th edition by Carlson & Thor...
 
Linear discriminant analysis: an overview
Linear discriminant analysis: an overviewLinear discriminant analysis: an overview
Linear discriminant analysis: an overview
 
Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...
Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...
Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...
 
Software Measurement: Lecture 1. Measures and Metrics
Software Measurement: Lecture 1. Measures and MetricsSoftware Measurement: Lecture 1. Measures and Metrics
Software Measurement: Lecture 1. Measures and Metrics
 
Frequency Distribution Table Handout
Frequency Distribution Table HandoutFrequency Distribution Table Handout
Frequency Distribution Table Handout
 
Measures and Strengths of AssociationRemember that while w.docx
Measures and Strengths of AssociationRemember that while w.docxMeasures and Strengths of AssociationRemember that while w.docx
Measures and Strengths of AssociationRemember that while w.docx
 
Day 4 normal curve and standard scores
Day 4 normal curve and standard scoresDay 4 normal curve and standard scores
Day 4 normal curve and standard scores
 
STATISTICS AND PROBABILITY.pptx
STATISTICS AND PROBABILITY.pptxSTATISTICS AND PROBABILITY.pptx
STATISTICS AND PROBABILITY.pptx
 
Scaling Z-scores T-scores C-scores
Scaling Z-scores T-scores C-scoresScaling Z-scores T-scores C-scores
Scaling Z-scores T-scores C-scores
 
Ordinal logistic regression
Ordinal logistic regression Ordinal logistic regression
Ordinal logistic regression
 
Tabulation of Data, Frequency Distribution, Contingency table
Tabulation of Data, Frequency Distribution, Contingency tableTabulation of Data, Frequency Distribution, Contingency table
Tabulation of Data, Frequency Distribution, Contingency table
 

More from Statistics Help Desk

Econometrics assignment help
Econometrics assignment helpEconometrics assignment help
Econometrics assignment help
Statistics Help Desk
 
Linear programming assignment help
Linear programming assignment helpLinear programming assignment help
Linear programming assignment help
Statistics Help Desk
 
Business statistics homework help service
Business statistics homework help serviceBusiness statistics homework help service
Business statistics homework help service
Statistics Help Desk
 
Business statistics homework help
Business statistics homework helpBusiness statistics homework help
Business statistics homework help
Statistics Help Desk
 
Do My Statistics Homework
Do My Statistics HomeworkDo My Statistics Homework
Do My Statistics Homework
Statistics Help Desk
 
Statistics assignment help
Statistics assignment helpStatistics assignment help
Statistics assignment help
Statistics Help Desk
 

More from Statistics Help Desk (6)

Econometrics assignment help
Econometrics assignment helpEconometrics assignment help
Econometrics assignment help
 
Linear programming assignment help
Linear programming assignment helpLinear programming assignment help
Linear programming assignment help
 
Business statistics homework help service
Business statistics homework help serviceBusiness statistics homework help service
Business statistics homework help service
 
Business statistics homework help
Business statistics homework helpBusiness statistics homework help
Business statistics homework help
 
Do My Statistics Homework
Do My Statistics HomeworkDo My Statistics Homework
Do My Statistics Homework
 
Statistics assignment help
Statistics assignment helpStatistics assignment help
Statistics assignment help
 

Recently uploaded

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
 
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
 
Standardized tool for Intelligence test.
Standardized tool for Intelligence test.Standardized tool for Intelligence test.
Standardized tool for Intelligence test.
deepaannamalai16
 
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
 
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit InnovationLeveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
TechSoup
 
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف القراءات العشر   أعد أحرف الخلاف سمير بسيوني.pdfمصحف القراءات العشر   أعد أحرف الخلاف سمير بسيوني.pdf
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
سمير بسيوني
 
skeleton System.pdf (skeleton system wow)
skeleton System.pdf (skeleton system wow)skeleton System.pdf (skeleton system wow)
skeleton System.pdf (skeleton system wow)
Mohammad Al-Dhahabi
 
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
 
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 InventoryHow to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
Celine George
 
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
 
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdfREASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
giancarloi8888
 
Nutrition Inc FY 2024, 4 - Hour Training
Nutrition Inc FY 2024, 4 - Hour TrainingNutrition Inc FY 2024, 4 - Hour Training
Nutrition Inc FY 2024, 4 - Hour Training
melliereed
 
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
PsychoTech Services
 
Bonku-Babus-Friend by Sathyajith Ray (9)
Bonku-Babus-Friend by Sathyajith Ray  (9)Bonku-Babus-Friend by Sathyajith Ray  (9)
Bonku-Babus-Friend by Sathyajith Ray (9)
nitinpv4ai
 
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDFLifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
Vivekanand Anglo Vedic Academy
 
Benner "Expanding Pathways to Publishing Careers"
Benner "Expanding Pathways to Publishing Careers"Benner "Expanding Pathways to Publishing Careers"
Benner "Expanding Pathways to Publishing Careers"
National Information Standards Organization (NISO)
 
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptxBIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
RidwanHassanYusuf
 
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
 
Stack Memory Organization of 8086 Microprocessor
Stack Memory Organization of 8086 MicroprocessorStack Memory Organization of 8086 Microprocessor
Stack Memory Organization of 8086 Microprocessor
JomonJoseph58
 
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptxNEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
iammrhaywood
 

Recently uploaded (20)

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
 
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
 
Standardized tool for Intelligence test.
Standardized tool for Intelligence test.Standardized tool for Intelligence test.
Standardized tool for Intelligence test.
 
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 ...
 
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit InnovationLeveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
 
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف القراءات العشر   أعد أحرف الخلاف سمير بسيوني.pdfمصحف القراءات العشر   أعد أحرف الخلاف سمير بسيوني.pdf
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
 
skeleton System.pdf (skeleton system wow)
skeleton System.pdf (skeleton system wow)skeleton System.pdf (skeleton system wow)
skeleton System.pdf (skeleton system wow)
 
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 - ...
 
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 InventoryHow to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
 
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
 
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdfREASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
 
Nutrition Inc FY 2024, 4 - Hour Training
Nutrition Inc FY 2024, 4 - Hour TrainingNutrition Inc FY 2024, 4 - Hour Training
Nutrition Inc FY 2024, 4 - Hour Training
 
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
 
Bonku-Babus-Friend by Sathyajith Ray (9)
Bonku-Babus-Friend by Sathyajith Ray  (9)Bonku-Babus-Friend by Sathyajith Ray  (9)
Bonku-Babus-Friend by Sathyajith Ray (9)
 
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDFLifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
 
Benner "Expanding Pathways to Publishing Careers"
Benner "Expanding Pathways to Publishing Careers"Benner "Expanding Pathways to Publishing Careers"
Benner "Expanding Pathways to Publishing Careers"
 
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptxBIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
 
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
 
Stack Memory Organization of 8086 Microprocessor
Stack Memory Organization of 8086 MicroprocessorStack Memory Organization of 8086 Microprocessor
Stack Memory Organization of 8086 Microprocessor
 
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptxNEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
 

Statistics Homework Help

  • 1. Copyright © 2012 Statisticshelpdesk.com, All rights reserved Statistics Homework Help Statistics Assignment Help | Help with Statistics Assignment 1/16/2015 Statisticshelpdesk Alex Gerg Contact Us: Statistics Help Desk Email: info@statisticshelpdesk.com Web: http://www.statisticshelpdesk.com Tel: +44-793-744-3379 Follow Us: https://www.facebook.com/Statshelpdesk http://twitter.com/statshelpdesk http://statistics-help-homework.blogspot.com/ Copyright © 2012 Statisticshelpdesk.com, All rights reserved
  • 2. Copyright © 2012 Statisticshelpdesk.com, All rights reserved About Statistics: Statisticshelpdesk provides solution to all kind of problems related to statistics, as it is a dedicated site of statistics so it provides all sorts of help i.e Assignment help, Homework help, Project help, Dissertation help, Live online tutoring, Statistical analysis, All sorts of software help that are being used in statistics. Statistics homework help will be viewed in numerous ways that. Normally it's thought-about to subsume graphs, charts, percentages, and averages. It consists of rules and strategies of collecting and presenting numerical data. It conjointly consists of constructing inferences from a given knowledge. The statistical data will be used to elucidate unexplained things, to form and justify a claim, to form comparisons, to seek out unknown quantities, to predict data regarding future and to ascertain relationship between quantities. Thus, it's a subject matter that consists of quite numbers. Statistics Sample Questions and Solutions ILLUSTRATION 1. From the following data determine by Sturge’s rule the number of classes to be formed and the interval of each such classes : Number of observation = 60 Value of largest observation = 95 Value of the smallest observation = 5 SOLUTION: According to struges rule the number of classes to be formed is given by K(s) =1+3.322log N = 1+3.322 log 60 = 1+1.322(1.7782) = 1+5.9072. Further, according to the said rule, the size of classes interval for each of the classes is given by i(s) = L−S K(s) 95−5 7 = 90 7 = 13 approx Thus, the number of classes to be formed is 7 and the classes interval of each such class should be 13.
  • 3. Copyright © 2012 Statisticshelpdesk.com, All rights reserved (iv) Class boundary:- Class boundaries or class walls mean the two extreme values of a class to which the data belonging to the said class cannot cross or exceeds either way. He lower of the two is called the lower boundary and the higher of the two id called the upper boundary of the class. In class of exclusive classification, all the values of the class remain below the upper boundary but in case of inclusive classification, all the values of a class remain within the two boundaries ending with the upper one .the following example will indicate the class boundaries under each of the two types of classification. Example: Class Boundaries In case of exclusive classes In case of exclusive classes Lower boundary Upper boundary Lower boundary Upper boundary 0-10 10-20 20-30 30-40 40-50 0-9 10-19 20-29 30-39 40-49
  • 4. Copyright © 2012 Statisticshelpdesk.com, All rights reserved (v) Class limits. Class limits mean the two extreme values of a class within which all the values of the class remain. The lower value of the class is called the lower limit and the upper value below which all the data remain is called the upper limit of the class. the lower limit is designed by L1 while the upper limit by L2 in case of exclusive classification the class limit are equal to the class boundaries explained above. But in case of inclusive classification, the class limits cross the class boundaries in both the directions. in such a case, the lower limit of a class is determined by subtracting from the lower boundary of the class half of the difference between the last significant digit of the upper boundary of the class and the last significant digit of the lower boundary of the next class. Similarly, the upper limit of the class is determined by adding. to the upper boundary of the class , half the difference between the last integer of the upper boundary of the class and last integer of the lower boundary of the next class. Symbolically, the lower limit and the upper limit of the inclusive class can be computed as under: Where L1 = lower limit of a class , L2 = upper limit of a class , B1=lower boundary of a class , B2 = upper boundary of a class , and d = difference between the last significant digit of the upper boundary of the class and the last significant digit of the lower boundary of the next class. In the above manner, in inclusive series is converted into an exclusive one for determining the values of certain measures viz, median, quartiles, percentiles, mode etc . That depend upon the exact class limits of the respective classes. The following examples will show the class limits of the different classes formed under both the exclusive and inclusive methods: Example1: L1=B1- 1 2 𝑑 and L2 = B2+- 1 2 𝑑 Exclusive Classes Inclusive Classes Classes Class-Limits Classes Class-Limits B1 B2 L1 L2 B1 B2 L1 L2 0-10 10-20 20-25.5 25.5-30.5 30.5-35.5 0 10 20 25.5 30.5 10 20 25.5 30.5 35.5 0-9 10-19 20-29 30-39 40-49 -.5 9.5 19.5 29.5 39.5 9.5 19.5 29.5 39.5 49.5
  • 5. Copyright © 2012 Statisticshelpdesk.com, All rights reserved Example 2: Inclusive Series I Inclusive Classes II Classes Class-Limits Classes Classes-Limits B1 B2 L1 L2 B1 B2 L1 L2 10-19.9 20-29.9 30-39.9 40-49.9 50.59.9 9.95 19.95 29.95 39.95 49.95 19.95 29.95 39.95 49.95 59.95 10-14.95 15-19.95 20-24.95 25-29.95 30-34.95 9.975 14.975 19.975 24.975 29.975 14.975 19.975 24.975 29.975 34.975 (vi) Mid-Value or Class Mark: The value that lies at the center of a class is called the mid – value , mid-point or class mark of the said class. It is computed M = L1+L2 2 = B1+B2 2 Where M= mid-value, L1 and L2=Lower and upper limits of the class respectively, And B1 and B2=lower and upper boundary of the class respectively. Thus the mid-value of the class (10-20) = 10+20 2 =15 and that of (10-19) = 10+19 2 =14.5