“No human mind is capable of
grasping in its entirety the meaning of
any considerable quantity of numerical
data. We want to be able to express all
the relevant information contained in the
mass by means of comparatively few
numerical values. This is a purely
practical need which the science of
statistics is able to some extent to
meet” (Fisher, 1950 p 7).
One of the three points that divide a data set into four equal parts. Or the values that divide data into quarters. Each group contains equal number of observations or data. Median acts as base for calculation of quartile.
Investment Multiplier and Super multiplierKhemraj Subedi
Investment Multiplier and Super Multiplier are very important concept of Macroeconomics to understand the effect of autonomous investment and induced investment in final increase in national income.
One of the three points that divide a data set into four equal parts. Or the values that divide data into quarters. Each group contains equal number of observations or data. Median acts as base for calculation of quartile.
Investment Multiplier and Super multiplierKhemraj Subedi
Investment Multiplier and Super Multiplier are very important concept of Macroeconomics to understand the effect of autonomous investment and induced investment in final increase in national income.
Types of Probability Distributions - Statistics IIRupak Roy
Get to know in detail the definitions of the types of probability distributions from binomial, poison, hypergeometric, negative binomial to continuous distribution like t-distribution and much more.
Let me know if anything is required. Ping me at google #bobrupakroy
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
This slide show is related to measures of dispersion or variability in Statistics. This slideshow will be useful to all the students and persons interested in Statistics, Bio statistics, Management, Education, Data Science, etc.
The single numerical value that indicates the orientation
of data towards the calculated central value of distribution. This value is sometimes called as nuclear value of the data.
Types of Probability Distributions - Statistics IIRupak Roy
Get to know in detail the definitions of the types of probability distributions from binomial, poison, hypergeometric, negative binomial to continuous distribution like t-distribution and much more.
Let me know if anything is required. Ping me at google #bobrupakroy
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
This slide show is related to measures of dispersion or variability in Statistics. This slideshow will be useful to all the students and persons interested in Statistics, Bio statistics, Management, Education, Data Science, etc.
The single numerical value that indicates the orientation
of data towards the calculated central value of distribution. This value is sometimes called as nuclear value of the data.
Measures of Central Tendency-Mean, Median , Mode- Dr. Vikramjit SinghVikramjit Singh
This presentation discusses in details about different measures of central tendency like- mean, median, mode, Geometric Mean, Harmonic Mean and Weighted Mean.
Anxiety based disorders. This ppt has been specifically designed for the Post Graduate Diploma in Guidance and Couselling students of GJUS&T, Hisar and affiliated College. FGM Govt. College Adampur have such diploma. the total seats are 20. We have well furnished lab. The students have exposure to various activities during their session at college. For more information and Psychology videos click on the following handle Dr. Rajesh Verma
@Psychologywala
2_Substance related and addictive disorders.pptxDr Rajesh Verma
Substance Abuse Related Disorders. this ppt is specially for students who are pursuing Post Graduate Diploma in Guidance and Counseling from GJUS&T Hisar and affiliated colleges.
Students can contact me for further discussion and doubts.
व्यावहारिक मनोविज्ञान का अर्थ इतिहास (Meaning and History of Applied Pschology)Dr Rajesh Verma
हेनरी इलियट के अनुसार “यह मनोविज्ञान की ऐसी शाखा है जिसमें शुद्ध और विशेषकर प्रायोगिक मनोविज्ञान की विधियों एवं परिणामों को व्यहारिक समस्याओं और व्यवहारिक जीवन पर प्रयोग करने का प्रयास किया जाता है”
Maulana Sayyid Abul Kalam Ghulam Muhiyuddin Ahmed bin Khairuddin Al-Hussaini Azad. मौलाना सैय्यद अबुल कलाम गुलाम मुहियुद्दीन अहमद बिन खैरुद्दीन अल-हुसैनी आज़ाद।
पतंजलि के अनुसार, "पर्यावरण के साथ पूर्वव्यस्तता के बिना आत्म से सामंजस्य बनाये रखने के लिए शारीरिक, बौद्धिक और संवेगात्मक संसाधनों के इष्टतम उपयोग को स्वास्थ्य कहा जाता है" (वर्मा, 1979)। According to Patanjali, “health is the optimal utilisation of one’s physical, intellectual and emotional faculties to maintain harmony with self without undue preoccupation with the environment’ (Verma, 1979)
सामान्यता की अवधारणा व्यक्तिपरक घटना होती है। जो व्यक्ति उचित व्यवहार करते हैं, उपयुक्त कार्य करते हैं और अपना जीवन सही तरीके से जीते हैं, कमोबेश स्वयं से संतुष्ट होते हैं और जीवन यापन के लिए आवश्यक दैनिक गतिविधियों को करने में किसी भी प्रकार की कठिनाई का सामना नहीं
करते हैं उन्हें आमतौर पर ‘सामान्य’ माना जाता है।
If we look at word Normal it is derived from Latin word ‘Norma’
meaning Rule. It means following or confirming to social norms or standards. “Normal means abiding by conduct and explicit or
implicit norms of the
society”
Overview of Quantitative research by Prof Rajbir Singh.Dr Rajesh Verma
In sciences we conduct research in order to determine the acceptability of hypotheses derived from theories. Having selected a certain hypothesis which seems important in a certain theory, we collect empirical data which should yield direct information on the acceptability of that hypothesis. Our decision about the meaning of the data may lead us to retain, revise, or reject the hypothesis and even the theory which was its source
मानक विचलन स्कोर्स के विस्तार की डिग्री का सूचकांक और उस जनसंख्या का जिसमे में से नमूना लिया गया है की विचलनशीलता का एक अनुमान होता है (Guilford & Fruchter, 1976)।
Standard deviation is and index of degree of dispersion and an estimate of the variability in the population from which the sample is drawn (Guilford & Fruchter, 1976).
चतुर्थक उन तीन बिंदुओं में से एक होता है जो किसी डेटा सेट को चार बराबर भागों में विभाजित करता है। या वो संख्याएँ जो डेटा को चार चतुर्थांशों में विभाजित करती हैं। प्रत्येक चतुर्थांश में आंकड़ों या डेटा की संख्या
समान होती है। चतुर्थक की गणना का आधार माध्य (Median) होता है।
दो मनोविज्ञान के प्रोफेसर छात्रों के असाइनमेंट चेक करते हैं और 50 में से जो नंबर देते हैं उनका औसत 38 अंक आता है। इसे देखकर क्या हमें ये मान लेना चाहिए की दोनों शिक्षक एक जैसा करते मूल्यांकन हैं? (ऐसा मानना खतरनाक हो सकता है!)। मान लीजिये: -
(i) एक शिक्षक 34 से 40 के बीच अंक देता है,
(ii) और दूसरा 20 से 48 के बीच।
यदि आप अपने असाइनमेंट को चेक
करवाना चाहते हैं तो आप किस शिक्षक
को चुनेंगे?
Two psychology professors assesses students’ assignment with average 38 marks (out of 50). Does this indicate that both teachers have same evaluation temperament (assuming such may be disastrous!). Let us consider
(i) teacher A awards within 34 to 40 marks,
(ii) while teacher B awards within 20 to 48 marks.
If you are a student and seeking
to get your assignment assessed which
teacher you will prefer??
Importance of social science research 17.09.2020Dr Rajesh Verma
Quantitative research based on measurement of quantity or amount
Applies to variables that can be measured
Asks questions such as what, how much etc.
Qualitative research applies to qualitative phenomena
Asks questions as why, seeks opinions, tries to find reasons for particular behaviour or event
केंद्रीय प्रवृत्ति’ शब्द 1920 के दशक के उत्तरार्ध की देन है (wikipedia)। सांख्यिकी, विशेष रूप से सामाजिक अनुसंधान में केंद्रीय प्रवृत्ति एक प्रकार का औसत (Average) होता है। आमतौर पर औसत तीन प्रकार के होते हैं अर्थात मध्यमान, माध्य एवं बहुलक (Mean, Median, Mode)। औसत ऐसी संख्या होती है जो स्कोर या व्यक्तियों के एक समूह के केंद्रीय मूल्य को दर्शाती है (Guilford & Fruchter, 1978)।
Maze was invented at the Lab of Edmund Sanford in Clark University in 1898-1899. They (Sanford and his students) started ‘rats-in-mazes’ tradition (Goodwin, 2012).
1898-1899 में क्लार्क विश्वविद्यालय में एडमंड सैनफोर्ड की लैब में भूलभुलैया का आविष्कार किया गया था। उन्होंने (सैनफोर्ड और उनके छात्रों ने) भूलभुलैया-में-चूहे नामक परंपरा की (गुडविन, 2012) शुरुआत की
1905 में दर्पण चित्रण की तकनीक डब्ल्यू एफ डियरबॉर्न ने प्रयास एवं त्रुटि द्वारा सीखने को दिखाने के लिए विशेष रूप से विकसित की गई थी (कारमाइकल, 2012) ।
In 1905 a technique of mirror drawing was developed by W. F. Dearborn specifically as a demonstration of trial and error learning (Carmichael, 2012).
निरपेक्ष सीमा या निरपेक्ष देहली एवं भेद सीमा या भेद देहली साइकोफिजिक्स की मूलभूत अवधारणाएं हैं जो 1860 में गुस्ताव थियोडोर फेचनर द्वारा प्रस्तावित की गई थीं ताकि शरीर और मन के बीच के संबंध का वर्णन गणित के आधार पर किया जा सके।
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.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
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 Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
4. “No human mind is capable of
grasping in its entirety the meaning of
any considerable quantity of numerical
data. We want to be able to express all
the relevant information contained in the
mass by means of comparatively few
numerical values. This is a purely
practical need which the science of
statistics is able to some extent to
meet” (Fisher, 1950 p 7).
5. Definition
The tendency of data to move towards the center.
The single
numerical value that
indicates the orientation
of data towards the
calculated central value
of distribution. This
value is sometimes
called as nuclear
value of the data.
6. Introduction
In statistics, especially social research, central
tendency is a kind of average. Averages are generally
three types
mean,
median and
mode.
In other words central tendency is a single value
generally located
towards the middle or center
of a distribution where
most of the data to be
concentrated (Levin &
Fox, 2006).
7. Purpose of Calculating Averages
1. It is shorthand description of a mass of
quantitative data.
2. Average makes data
meaningful and represents
it.
3. Describes the
population from which
the sample is drawn.
4. To compare two or
more groups in terms of
typical performance.
9. Measures of Central Tendency
The central tendency is measured by: -
1. Mean or arithmetic mean
2. Median
3. Mode
10. 1. Arithmetic Mean – Most commonly used measure of central
tendency and denoted by 𝑿commonly known as X-bar. It is the
sum of the separate scores or measures divided by their number.
Mean = 𝑿 =
∑𝑿
𝑵
(For ungrouped data)
Where 𝑿= mean
∑ = the sum of
x = score or measurement
N = number of scores or measurements
For example we have some hypothetical data such as 21, 33, 29,
31, 20, 18, 32, 23, 27, 36.
∑X = 21+33+29+31+20+18+32+23+27+36=270
N = 10
therefore 𝑿 =
𝟐𝟕𝟎
𝟏𝟎
= 27
11. Mean = 𝑿 =
∑𝒇𝑿
𝑵
(For grouped data)
Where 𝑿= mean
∑ = the sum of
f = Frequency
x = midpoint of class interval
fx = Product of frequency and midpoint
N = number of scores or measurements
As per table 1.1
∑fx = 1480
N = 50
therefore 𝑿 =
𝟏𝟒𝟖𝟎
𝟓𝟎
= 29.60
Note: Mean is a point on the distribution not a
score
12. Other Types of Mean
1. Weighted mean.
2. Harmonic mean.
3. Geometric mean.
4. Arithmetic-Geometric mean.
5. Root-Mean Square mean.
6. Heronian mean.
13. 2. Median – The median is defined as that point on
the scale of measurement above which are exactly half
the cases and below which are other half (Guilford &
Fruchter, 1978). In other words median is middle most
point in a distribution. (Levin & Fox, 2006). It divides
the distribution in two parts as a white strip (median
strip) divides the highway into two parts.
(i) Calculation of Median of ungrouped data: -
Step I – Arrange the scores in ascending order
Step II – Then count the total scores
(a) If scores are odd then the middle score is
median
For example: 18, 20, 21, 23, 27, 29, 31, 32, 33
14. (b) If scores are even then follow the following rule.
𝑴𝒆𝒅𝒊𝒂𝒏 =
(𝑵+𝟏)
𝟐
𝐭𝐡 score
For example: 18, 20, 21, 23, 27, 29, 31, 32, 33, 36
𝑴𝒆𝒅𝒊𝒂𝒏 =
𝟏𝟎+𝟏
𝟐
th score
= 11/2 = 5.5th score counting from the either end of
series.
So, Median is above 27
(5th score) and below 29
(6th score) i.e. 28
15. (ii) Calculation of Median of grouped data: -
Step I – Find the N/2 (50/2 = 25)
Step II – Start at the lowest score end of class interval
column and count off the
scores in order up to the
exact lower limit (l) of the
interval which contains the
median. The sum of these
scores is F. Or just check
the cumulative frequency
of class interval previous to
the class interval in which
median falls.
therefore F or CF = 16
Class
Interval
Frequency
(f)
cf
55-59 1 50
50-54 1 49
45-49 3 48
40-44 4 45
35-39 6 41
30-34 7 35
25-29 12 28
20-24 6 16
15-19 8 10
10-14 2 2
16. Step III – Compute the number of scores necessary to fill out
N/2, i.e. compute N/2-F. Divide the quantity by the frequency
(fm) on the interval which contains the median; and multiply
the result by the size of the
class interval (i)
N/2-F = 25-16 = 9
=
𝟗
𝟏𝟐
𝑿𝟓 = 3.75
Step IV – Add the amount
obtained by the calculation in
(3) to the exact lower limit (l) of
the interval which contains the
median. 25.5+3.75 = 29.25
Note: Median is a point on the
distribution not a score
Class
Interval
Frequency
(f)
cf
55-59 1 50
50-54 1 49
45-49 3 48
40-44 4 45
35-39 6 41
30-34 7 35
25-29 12 28
20-24 6 16
15-19 8 10
10-14 2 2
17. Formula to Calculate Median of grouped data
Median = 𝒍 +
𝑵
𝟐
−𝑪𝑭
𝒇𝒎
𝒊
Where, l = Exact lower limit of the class in which median lies
N = Number of scores
CF = Cumulative frequency of the previous class interval
to the class interval in which median falls.
fm = frequency within the interval upon which the median
falls
i = size of class interval
l = 25.5, N/2 = 50/2 = 25, F or CF = 16,fm= 12, i = 5
Substituting the values = 25.5+
𝟐𝟓−𝟏𝟔
𝟏𝟐
5
= 25.5 + (9/12) 5 = 25.5 +(.75) 5
= 25.5 + 3.75 = 29.25
18. 3. Mode – The measure of central tendency which is
most frequent occurring value of a distribution. The
mode is the only measure of central tendency available
for nominal level variables (Levin & Fox, 2006). In an
ungrouped data the single score which occurs most
frequently is the mode.
19. (a) For ungrouped data
For example in this distribution 11, 23, 25, 25, 30, 32, 36,
36, 36, 45, 48, 51 the most frequently occurring score is 36
which is mode [Crude].
(b) For grouped data
The mode is usually
taken to be the midpoint of
that interval which contains
the largest frequency. In this
table the midpoint of class
interval of 25-29 is 27 which is
mode [crude].
Formula for Mode [True] = 3 Median – 2 Mean
Class Interval Mid point of
Class interval
Frequency
(f)
55-59 57 1
50-54 52 1
45-49 47 3
40-44 42 4
35-39 37 6
30-34 32 7
25-29 27 12
20-24 22 6
15-19 17 8
10-14 12 2
20. References:
1. Guilford, J. P. & Fruchter, B. (1978).
Fundamental Statistics in Psychology and Education.
Tokyo: McGraw Hill.
2. Garrett, H. E. (2014). Statistics in Psychology and
Education. New Delhi: Pragon International.
3. Levin, J. & Fox, J. A. (2006). Elementary
Statistics. New Delhi: Pearson.
4. Upton, G. & Cook,
I. (2008). Oxford
Dictionary of Statistics,
OUP ISBN
978-0-19-954145-4.