1. Quantitative data can be summarized using measures of center (mean, median), spread (range, IQR, standard deviation), and position (quartiles, percentiles, z-scores).
2. The mean is more affected by outliers than the median. The median is more resistant to outliers and a better measure of center for skewed data.
3. Additional summaries like the five-number summary and boxplots provide a graphical view of the distribution and identify potential outliers.
Basic knowledge and practical applicability of Mean median mode is given which is useful for MSc Microbiology ,BCom II, BBA I,BA students.References has been taken from economics books and websites
Basic knowledge and practical applicability of Mean median mode is given which is useful for MSc Microbiology ,BCom II, BBA I,BA students.References has been taken from economics books and websites
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
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👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
2. Summarizing distributions of
univariate data
1. Measuring center: median, mean
2. Measuring spread: range, interquartile
range, standard deviation
3. Measuring position: quartiles, percentiles,
standardized scores (z-scores)
4. Using boxplots
5. The effect of changing units on summary
measures
3. Measuring Center
When describing the “center” of a set of
data, we can use the mean or the median.
Mean: “Average” value
Median: “Center” value (Q2)
4. Where is the Center of the
Distribution?
If you had to pick a single number to describe
all the data what would you pick?
It’s easy to find the center when a histogram is
unimodal and symmetric—it’s right in the
middle.
On the other hand, it’s not so easy to find the
center of a skewed histogram or a histogram
with more than one mode.
5. Mean
To find the mean
of a set of
observations, add
their values and
divide by the
number of
observations.
x =
xi∑
n
6. Find the mean of:
2 3 4 6 8 12
6
1286432 +++++
833.5=x
7. Although the mean is the most popular
measure of center, it is not always the most
appropriate.
The mean is very sensitive to extreme
observations (outliers).
Because outliers affect the mean, we say
that the mean is NOT a resistant measure of
center.
So if the mean is not a resistant measure of
center, what is? Median
8. Median
The median is the value with
exactly half the data values
below it and half above it.
It is the middle data value
once the data values have
been ordered) that divides
the histogram into two
equal areas
It has the same units as
the data
The median is not
influenced by extreme
observations, so we say
that the median is a
resistant measure of
center.
9. Finding the Median
First sort the values (arrange them in order),
then follow one of these:
1. If the number of data values is even, the
median is found by computing the mean of
the two middle numbers.
2. If the number of data values is odd, the
median is the number located in the exact
middle of the list.
10. 5.40 1.10 0.42 0.73 0.48 1.10
0.42 0.48 0.73 1.10 1.10 5.40
(in order - even number of values – no exact middle shared by two numbers)
0.73 + 1.1 MEDIAN is 0.915
2
5.40 1.10 0.42 0.73 0.48 1.10 0.66
0.42 0.48 0.66 0.73 1.10 1.10 5.40
(in order - odd number of values)
exact middle MEDIAN is 0.73
11. Mean vs Median
Mean Median
Average value of variable Typical value of variable
Not resistant to outliers Resistant to outliers
A good measure when the data
is symmetric
A reliable measure regardless
of the shape of the distribution
Farther out in the long tail than
the median when data is
skewed
Close to the center even when
the data is skewed
Easy to find Less prone to mistakes
15. Range
Distance between largest and smallest values.
Range = Maximum – Minimum
Range is useful if there are no outliers.
16. Interquartile Range
How to find the IQR:
1. Find median
2. Find the median of both halves of data
the lower median is 1st
Quartile
the upper median is 3rd
Quartile
3. Subtract the two quartile scores
17. Outliers
One general rule of thumb for identifying
outliers is finding any data points that lie:
Lower than 1.5 * IQR below Q1
OR
Higher than 1.5 * IQR above Q3
18. Check For Understanding
• The “Descriptive Statistics” of test grades for a certain
class are listed below.
Mean = 74.71
Median = 76
Standard Deviation = 12.61
Minimum = 35
Maximum = 94
Q1 = 68
Q3 = 84
• (a) Determine the IQR for this data.
• (b) Using the answer from part (a), determine whether
the lowest and highest values in the data are outliers.
19. Standard Deviation
A standard deviation is a measure of the average
deviation from the mean.
sx =
1
n −1
(xi − x)2
∑
20. If the data is uniform or symmetric use:
If the data is skewed, use:
MeanCenter:
Spread:standard deviation
MedianCenter:
Spread:Five-number summary, Range, IQR
21. Distributions with Outliers
Since outliers affect mean and standard
deviation, it is usually better to use median
and IQR
However, if the distribution is unimodal—use
mean and median and just report outliers
separately
However, if you find a simple reason for
outlier, eliminate it and use mean and
standard devation—if symmetric
22. Measuring Position
Quartiles
Percentiles
Z-scores
• We can either use z-
Scores or percentiles to
declare the location of
an observation in a
distribution.
• z-Scores use the mean
and standard deviation.
• Percentiles use a
position relative to the
starting point.
23. Percentiles/Quartiles
• is the notation for
the kth percentile
• is the notation for
the nth quartile
P Q25 1=
P Q50 2= = median
P Q75 3=
24. Finding Percentiles
If you are trying to find the percentile
corresponding to a certain score x:
number of scores <
100
total number of scores
x
Percentile = ×
• Percentiles are used often when reporting academic
scores such as SAT scores. Let’s say you get a 620 on
the math portion of the SAT. It might also indicate
that you are in the “78th percentile”. That means
that you scored better than 78% of all students
taking that particular SAT.
25. Measuring Relative Standing With
Standardized Values (z-Scores)
• One way to compare an individual to the whole
distribution is to describe it’s location in the
distribution relative to the mean.
• Let’s do this by describing how many standard
deviations an individual is away from the mean value.
• We call this the “standardized value,” or, the “z-
Score.”
26. Here is how to interpret z-scores:
A z-score less than 0 represents an element less than
the mean.
A z-score greater than 0 represents an element
greater than the mean.
A z-score equal to 0 represents an element equal to
the mean.
A z-score equal to 1 represents an element that is 1
standard deviation greater than the mean; a z-score
equal to 2, 2 standard deviations greater than the
mean; etc.
A z-score equal to -1 represents an element that is 1
standard deviation less than the mean; a z-score equal
to -2, 2 standard deviations less than the mean; etc.
27. Five-Number Summary
The five-number summary of a distribution
consists of the smallest observation, the first
quartile, the median, the third quartile, and the
largest observation, written in order from
smallest to largest.
Minimum Q1 Median Q3 Maximum
28. Boxplots
The five-number summary divides the
distribution roughly into quarters. This leads
to a new way to display quantitative data, the
boxplot.
29. How to make a boxplot:
1. Draw and label a number line that includes
the range of the distribution.
2. Draw a central box from Q1 to Q3.
3. Note the median M inside the box.
4. Extend lines (whiskers) from the box out to
the minimum and maximum values that are
not outliers.
32. Effect of Changing Units
If you add a constant to every
value, the mean and median
increase by the same
constant.
Example:
Suppose you have a set of
scores with a mean equal to 5
and a median equal to 6. If
you add 10 to every score,
the new mean will be 5 + 10 =
15; and the new median will
be 6 + 10 = 16.
If you multiply every value
by a constant. Then, the
mean and the median will
also be multiplied by that
constant.
Example:
Assume that a set of scores
has a mean of 5 and a
median of 6. If you multiply
each of these scores by 10,
the new mean will be 5 * 10
= 50; and the new median
will be 6 * 10 = 60.
Sometimes, researchers change units (minutes to hours,
feet to meters, etc.). Here is how measures of central
tendency are affected when we change units:
33. Check For Understanding
The average score on a test is 150 with a
standard deviation of 15. Each score is then
increased by 25. What are the new mean and
standard deviation?
34. Check For Understanding
The test grades from a college statistics class are shown
below.
85 72 64 65 98 78 75 76 82 80 61 92 72 58 65 74 92 85 74 76 77 77
62 68 68 54 62 76 73 85 88 91 99 82 80 74 76 77 70 60
(a) Construct two different graphs of these data
(b) Calculate the five-number summary and the mean and
standard deviation of the data.
(c) Describe the distribution of the data, citing both the
plots
and the summary statistics found in questions (a) and (b).