Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Introduction to Statistics - Basic concepts
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Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
Topic: Frequency Distribution
Student Name: Abdul Hafeez
Class: B.Ed. (Hons) Elementary
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Understanding data type is an important concept in statistics, when you are designing an experiment, you want to know what type of data you are dealing with, that will decide what type of statistical analysis, visualizations and prediction algorithms could be used.
#data #data types #ai #machine learning #statistics #data science #data analytics #artificial intelligence
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
This presentation gives you a brief idea;
-definition of frequency distribution
- types of frequency distribution
-types of charts used in the distribution
-a problem on creating types of distribution
-advantages and limitations of the distribution
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
Topic: Frequency Distribution
Student Name: Abdul Hafeez
Class: B.Ed. (Hons) Elementary
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Understanding data type is an important concept in statistics, when you are designing an experiment, you want to know what type of data you are dealing with, that will decide what type of statistical analysis, visualizations and prediction algorithms could be used.
#data #data types #ai #machine learning #statistics #data science #data analytics #artificial intelligence
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
This presentation gives you a brief idea;
-definition of frequency distribution
- types of frequency distribution
-types of charts used in the distribution
-a problem on creating types of distribution
-advantages and limitations of the distribution
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
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Introduction to graph of class 8th students. Find a new easy way to understand graph, histogram, double-bar graph, pie-chart etc....This ppt could lead to u a better picture of maths
Don't get confused with Summary Statistics. Learn in-depth types of summary statistics from measures of central tendency, measures of dispersion and much more.
Let me know if anything is required. ping me at google #bobrupakroy
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Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Embracing GenAI - A Strategic ImperativePeter 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.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2. What is a variable?
In statistics, a variable has two defining characteristics:
A variable is an attribute that describes a person, place, thing, or idea.
The value of the variable can "vary" from one entity to another.
For example, a person's hair color is a potential variable, which could
have the value of "blond" for one person and "brunette" for another.
3. Qualitative vs. Quantitative Variables
Variables can be classified
as qualitative (aka,
categorical – Age, likert
scale, race) or quantitative
(aka, numeric).
4. Examples of types of data
Quantitative
Continuous Discrete
Blood pressure, height, weight, Number of children, Number of
age attacks of asthma per week
Categorical
Ordinal (Ordered categories) Nominal (Unordered categories)
Grade of breast cancer Sex (male/female)
Better, same, worse Alive or dead
Disagree, neutral, agree Blood group O, A, B, AB
5. Graphical presentation of data is
• better understood and appreciated by humans.
• brings out the hidden pattern and trends of the complex data
sets.
Thus the reason for displaying data graphically is
two fold:
• Investigators can have a better look at the information
collected and the distribution of data
• To communicate this information to others quickly We shall
discuss in detail some of the commonly used graphical
presentations.
6. Bar Charts : Bar charts are
used for qualitative type of
variable
Here the variable studied is
plotted in the form of bar
along the X-axis (horizontal)
and the height of the bar is
equal to the percentage or
frequencies which are plotted
along the Y-axis (vertical).
12. Pie Chart
A pie diagram is best when
the total categories are
between 2 to 6.
If there are more than 6
categories, try and reduce
them by “clubbing”,
otherwise the diagram
becomes too overcrowded.
13.
14.
15.
16.
17.
18.
19. Stem-and-leaf plots
This presentation is used for quantitative type of
data.
To construct a stem-and-leaf plot, we divide each
value into a stem component and leaf component.
The digits in the tens-place becomes stem
component and the digits in units place becomes
leaf components.
It is of much utility in quickly assessing whether
the data is following a “normal” distribution or
not, by seeing whether the stem and leaf is
showing a bell shape or not.
For example consider a sample of 10 values of age
in years : 21, 42, 05, 11, 30, 50, 28, 27, 24, 52.
20. Histogram
A histogram is used for
quantitative continuous type
of data where, on the X-axis,
we plot the quantitative
exclusive type of class intervals
and on the Y-axis we plot the
frequencies.
The difference between bar
charts and histogram is that
since histogram is the best
representation for quantitative
data measured on continuous
scale, there are no gaps
between the bars.
21. Box-and-Whisker plot
A box-and-whisker plot reveals
maximum of the information to the
audience.
A box-and whisker plot can be useful
for handling many data values.
They allow people to explore data and
to draw informal conclusions when
two or more variables are present.
It shows only certain statistics rather
than all the data.
22. Box-and-Whisker plot
Five-number summary is another name for the
visual representations of the box and whisker
plot.
Maximum
The five-number summary consists of the Q3
median, the quartiles (lower quartile and upper
quartile), and the smallest and greatest values Range IQR Median
in the distribution.
Q1
Minimum
Thus a box-and-whisker plot displays the
• center,
• the spread,
• overall range of distribution
23. Scatter Diagram
A scatter diagram gives a quick visual
display of the association between two
variables, both of which are measured on
numerical continuous or numerical
discrete scale. (Both quantitative)
Figure shows instant finding that weight
and age are associated - as age increases,
weight increases.
Be careful to record the dependent
variable along the vertical (Y) axis and the
independent variable along the
horizontal (X) axis.
24. Scatter Diagram
In this example weight is
dependent on age (as age
increases weight is likely to
increase) but age is not dependent
on weight (if weight increases, age
will not necessarily increase).
Thus, weight is the dependent
variable, and has been plotted on Y
axis while age is the independent
variable, plotted along X axis.
25.
26. Correlation coefficient
The degree of association is measured by
a correlation coefficient, denoted by r.
It is sometimes called Pearson's
correlation coefficient after its originator
and is a measure of linear association.
27. Correlation coefficient
The correlation coefficient is measured on a
scale that varies from + 1 through 0 to - 1.
Complete correlation between two variables is
expressed by either + 1 or -1.
• When one variable increases as the other increases the
correlation is positive; (coffee v/s wakefulness)
• when one decreases as the other increases it is negative.
(Old is gold!)
• Complete absence of correlation is represented by 0.
28. A perfect correlation of ± 1
occurs only when the data
points all lie exactly on a
straight line.
A correlation greater than
0.8 would be described as
strong, whereas a correlation
less than 0.5 would be
described as weak.
29. Correlation coefficient v/s Regression
analysis
Regression is used
When the objective is to extensively in making
determine association or the predictions based on
strength of relationship between
two such variables, we use finding unknown Y values
correlation coefficient (r). from known X values.
If the objective is to quantify and Multiple Regression is the
describe the existing relationship same as regression except
with a view of prediction, we use that it attempts to predict Y
regression analysis. from two or more
independent X variables.
31. Measures of Central Tendency
This gives the centrality measure of the data set i.e. where the observations are
concentrated. There are numerous measures of central tendency. These are : Mean;
Median; Mode; Geometric Mean; Harmonic Mean.
Mean (Arithmetic Mean) or Average
It is calculated as follows.
This is most appropriate measure for
data following normal distribution. It
is calculated by summing all the
observations and then dividing by
number of observations. It is
generally denoted by x.
32. Mean (Arithmetic Mean) or Average
It is the simplest of
the centrality
It depends on all
measure but is
values of the data
influenced by
set but is affected
extreme values and
by the fluctuations
hence at times may
of sampling
give fallacious
results.
33. Example : The serum cholesterol level (mg/dl) of 10 subjects
were found to be as follows:
192 242 203 212 175 284 256 218 182 228
34. Median
.
When the data is skewed, another measure of central tendency called
median is used.
Median is a locative measure which is the middle most observation
after all the values are arranged in ascending or descending order.
In case when there is odd number of observations we have a single
most middle value which is the median value.
In case when even number of observations is present there are two
middle values and the median is calculated by taking the mean of
these two middle observations
It is less affected by fluctuations of sampling than mean.
35.
36. Mode
Though mode is easy to
calculate, at times it may be
Mode is the most common
impossible to calculate
value that repeats itself in
mode if we do not have any
the data set.
value repeating itself in the
data set.
At other end it may so
happen that we come In such cases the
across two or more values distribution are said to
repeating themselves same bimodal or multimodal.
number of times.
37. Measures of Relative Position
(Quantiles)
Quantiles are the values that divide a set numerical data arranged in
increasing order into equal number of parts.
Quartiles divide the numerical data arranged in increasing order into four
equal parts of 25% each.
• Thus there are 3 quartiles Q1, Q2 and Q3 respectively.
Deciles are values which divide the arranged data into ten equal parts of 10%
each.
• Thus we have 9 deciles which divide the data in ten equal parts.
Percentiles are the values that divide the arranged data into hundred equal
parts of 1% each.
• Thus there are 99 percentiles.
• Q) Median = ___ percentile, ____ decile and ____quartile.
39. Measures of Variability
In contrast to measures of central
tendency which describes the
center of the data set, measures of
variability describes the variability
or spreadness of the observation
from the center of the data.
40. Measures of Variability
Various measures of dispersion
are as follows.
• Range
• Interquartile range
• Mean deviation
• Standard deviation
• Coefficient of variation
41. Range
One of the simplest measures
of variability is range. Range is
the difference between the two Range = maximum observation
extremes i.e. the difference – minimum observation
between the maximum and
minimum observation.
Drawback of range is that it
It gives rough idea of the
uses only extreme observations
dispersion of the data.
and ignores the rest.
42. Interquartile Range
As in the case of range difference in extreme
observations is found, similarly interquartile
range is calculated by taking difference in the
values of the two extreme quartiles.
Interquartile range = Q3 - Q1
44. Coefficient of Variation
• measures variability in relation to
Besides the measures the mean (or average) and is used
of variability discussed to compare the relative dispersion
above, we have one in one type of data with the
relative dispersion in another type
more important of data.
measure called the • The data to be compared may be
coefficient of variation in the same units, in different
which compares the units, with the same mean, or with
different means.
variability in two data
sets.