Measures of central tendency and relative position are used to summarize and compare datasets. The three main measures of central tendency are the mean, median, and mode. The mean is the average value and is calculated by summing all values and dividing by the total number of data points. The median is the middle value when data is arranged in order. The mode is the most frequent occurring value. Quantiles like quartiles, deciles, and percentiles divide a dataset into equal parts to indicate what percentage of values are below certain points.
This mini project is created by Md Halim from Haldia Insititute of Technology, Haldia WB. Disclaimer:- if any error is not the responsibility to team Halim
I am Jeremy P. I am a Statistics Assignment Expert at statisticsassignmenthelp.com. I hold a Master's in Statistics, from Loughborough University, UK. I have been helping students with their homework for the past 7 years. I solve assignments related to Statistics.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
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Probability
Random variables and Probability Distributions
The Normal Probability Distributions and Related Distributions
Sampling Distributions for Samples from a Normal Population
Classical Statistical Inferences
Properties of Estimators
Testing of Hypotheses
Relationship between Confidence Interval Procedures and Tests of Hypotheses.
According to Wikipedia point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population means).
Central tendency of data is defined as the tendency of data to concentrate around some central value. here all the measures of central tendency have been explained such as mean, arithmetic mean, geometric mean, harmonic mean, mode, and median with examples.
This 10 hours class is intended to give students the basis to empirically solve statistical problems. Talk 1 serves as an introduction to the statistical software R, and presents how to calculate basic measures such as mean, variance, correlation and gini index. Talk 2 shows how the central limit theorem and the law of the large numbers work empirically. Talk 3 presents the point estimate, the confidence interval and the hypothesis test for the most important parameters. Talk 4 introduces to the linear regression model and Talk 5 to the bootstrap world. Talk 5 also presents an easy example of a markov chains.
All the talks are supported by script codes, in R language.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This mini project is created by Md Halim from Haldia Insititute of Technology, Haldia WB. Disclaimer:- if any error is not the responsibility to team Halim
I am Jeremy P. I am a Statistics Assignment Expert at statisticsassignmenthelp.com. I hold a Master's in Statistics, from Loughborough University, UK. I have been helping students with their homework for the past 7 years. I solve assignments related to Statistics.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Statistics Assignments.
Probability
Random variables and Probability Distributions
The Normal Probability Distributions and Related Distributions
Sampling Distributions for Samples from a Normal Population
Classical Statistical Inferences
Properties of Estimators
Testing of Hypotheses
Relationship between Confidence Interval Procedures and Tests of Hypotheses.
According to Wikipedia point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population means).
Central tendency of data is defined as the tendency of data to concentrate around some central value. here all the measures of central tendency have been explained such as mean, arithmetic mean, geometric mean, harmonic mean, mode, and median with examples.
This 10 hours class is intended to give students the basis to empirically solve statistical problems. Talk 1 serves as an introduction to the statistical software R, and presents how to calculate basic measures such as mean, variance, correlation and gini index. Talk 2 shows how the central limit theorem and the law of the large numbers work empirically. Talk 3 presents the point estimate, the confidence interval and the hypothesis test for the most important parameters. Talk 4 introduces to the linear regression model and Talk 5 to the bootstrap world. Talk 5 also presents an easy example of a markov chains.
All the talks are supported by script codes, in R language.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
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Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
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The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
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students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
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• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Measures_of_Central_tendency[1].PPT
1. Measures of central tendency
* To compare the central value of a character of
two or more series of data
Average
It is a central value of an attribute around which
other observations dispersed.
Uses
* To compare any observation with the central
value of an attribute in the same series of data
3. X1+ X2+X3+. … +Xn
Mean = ----------------------------
n
Mean
If X1, X2, X3,... …. ,Xn are n observations
4. Merits
It is based on all the observations
Demerit
In case of extreme observations, it is not a
representative of a data.
e.g. 10,000, 250, 350, 400 mean=2750
5. Median
If X1, X2, X3,... …. ,Xn are n observations
arranged in ascending order of magnitude.
X(n+1)/2 If n is odd
Median = {
Xn/2 + X(n/2+1) If n is even
------------------
2
6. 74+75
Median = ---------- = 74.5
2
The following are the pulse rate per minute of
10 healthy individuals
82, 79, 60, 76, 63,81, 68, 74, 60, 75.
60, 60, 63, 68, 74,75, 76, 79, 81, 82
7. 162, 164, 166, 168, 170, 172, 174, 178,184, 188, 188
Median = 172
The following are the BP(S) of 11 hypertensive
patients of aged 50yrs
184, 170, 168, 188 162, 164, 174, 172, 178, 166, 188
8. Merits
1. It is unique for the given set of data.
2. It is not affected by presence of extreme
observations.
3. Median can be calculated even if values
of extremes are not known, if n is known.
10. Mode
It is most frequently occurring observation in
a given series of data.
Demerits
1. It is not based on all the observations.
2. It may not exist.
11. The following are the BP(S) of 11 hypertensive
patients of aged 50yrs.
184,170,168,188,162,164,174,172,178,166,188
Mode = 188
12. The following are the BP(S) of 10
hypertensive patients of aged 50yrs.
184,170,168,188,162,164,174,172,178,166
All the values have got the same frequency.
hence,
Mode does not exits.
13. The following are the BP(S) of 10
hypertensive patients of aged 50yrs.
188,170,166,188,162,164,174,172,178,166
Two values have got the same maximum
frequency.
hence,
Mode =188 and 166
14. Geometric Mean(G.M.)
If x1, x2, …………xn are n observations then G.M. is defined as
G.M. = (x1x2…..xn )1/n
Merits :
1. It is unique for the given set of data.
2. It is based on all the observations.
3. It less affected by extreme values.
Demerits:
1. It can not be used if any observation is either negative or
zero.
15. Harmonic Mean(H.M.)
If x1, x2, …………xn are n observations then
H.M. is defined as
1
H.M. = --------------------------------
(1/x1+ 1/x2…….+ 1/xn )/n
H.M. it is reciprocal of mean of reciprocals.
16. Merits :
1. It is unique for the given set of data.
2. It is based on all the observations.
3. It is least affected by the fluctuation of the
sampling.
Demerits:
1. It can not be used if any observation is zero.
2. It gives less weightage to largest observation and
more weightage to lowest observation.
17. Measures of relative position
Quantiles : Quantiles divides the total data into equal
parts after arranging the data in ascending order of
magnitude.
Quartiles divides the total data into 4 equal parts.
Deciles divides the total data into 10 equal parts.
Percentiles divides the total data into 100 equal parts.
18. Quartiles:
Q1, Q2 and Q3 denote 1st, 2nd and 3rd quartiles resp.
If X1, X2, …………Xn are n obs arranged in ascending
order of magnitude then quartiles are defined as
Qj = Xj( n+1)/4 , j = 1, 2, 3
Q1 indicates that 25% of observations are ≤ Q1.
Q2 indicates that 50% of observations are ≤ Q2.
Q3 indicates that 75% of observations are ≤ Q3.
Note: If n is even then Q2 is average of middle two obs.
19. Deciles: Let Dj denotes the jth decile and it is
defined as
Dj = Xj( n+1)/10 j= 1 to 10
For example D2 is given by D2 = X2( n+1)/10
It indicates that 10% of observations are less than
or equal to D2.
20. Percentiles:
Let Pj denotes the jth percentile.
It indicates that j% of observations are less than or
equal to Pj .
It is defined as
Pj = Xj( n+1)/100
For example 30th percentile is given by
P30 = X30( n+1)/100