The document provides information about descriptive statistics, summarizing qualitative and quantitative data, and various methods for presenting data visually. It discusses frequency distributions, relative and percent frequency distributions, bar graphs, pie charts, dot plots, histograms, and cumulative distributions as ways to summarize data in a clear manner. Guidelines are given for selecting class widths and numbers when creating frequency distributions. Examples using data on hotel ratings and auto repair part costs are presented to illustrate the various statistical and graphical techniques.
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
Elements of Inference covers the following concepts and takes off right from where we left off in the previous slide https://www.slideshare.net/GiridharChandrasekar1/statistics1-the-basics-of-statistics.
Population Vs Sample (Measures)
Probability
Random Variables
Probability Distributions
Statistical Inference – The Concept
This presentation covers important topics such as
Multiple Independent Random Variables or i.i.d samples.
Expectations or Expected values
T-Distribution
Central Limit Theorem
Asymptotics & Law of Large Numbers
Confidence Intervals
In this lesson, students will be shown that it is not enough to get measures of central tendency in a data set by scrutinizing two different data sets with the same measures of central tendency. We illustrate this using data on the returns on stocks where it is not only the mean, median and mode which are the same, it is also true for other measures of location like its minimum and maximum. However, the spread of observations are different which means that to further describe the data sets we need additional measures like a measure about the dispersion of the data, i.e. range, interquartile range, variance, standard deviation, and coefficient of variation. Also, the standard deviation, as a measure of dispersion can be viewed as a measure of risk, specifically in the case of making investments in stock market. The smaller the value of the standard deviation, the smaller is the risk.
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
Abstract: This PDSG workshop introduces basic concepts of statistics. Concepts covered are mean (average), median, mode, standard deviation discrete vs. continuous, normal distribution, sampling distribution, Z-scores and boxplots.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Elements of Inference covers the following concepts and takes off right from where we left off in the previous slide https://www.slideshare.net/GiridharChandrasekar1/statistics1-the-basics-of-statistics.
Population Vs Sample (Measures)
Probability
Random Variables
Probability Distributions
Statistical Inference – The Concept
This presentation covers important topics such as
Multiple Independent Random Variables or i.i.d samples.
Expectations or Expected values
T-Distribution
Central Limit Theorem
Asymptotics & Law of Large Numbers
Confidence Intervals
In this lesson, students will be shown that it is not enough to get measures of central tendency in a data set by scrutinizing two different data sets with the same measures of central tendency. We illustrate this using data on the returns on stocks where it is not only the mean, median and mode which are the same, it is also true for other measures of location like its minimum and maximum. However, the spread of observations are different which means that to further describe the data sets we need additional measures like a measure about the dispersion of the data, i.e. range, interquartile range, variance, standard deviation, and coefficient of variation. Also, the standard deviation, as a measure of dispersion can be viewed as a measure of risk, specifically in the case of making investments in stock market. The smaller the value of the standard deviation, the smaller is the risk.
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
Abstract: This PDSG workshop introduces basic concepts of statistics. Concepts covered are mean (average), median, mode, standard deviation discrete vs. continuous, normal distribution, sampling distribution, Z-scores and boxplots.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Diapositiva del libro de Anderson de estadística aplicada a los negocios y la economía, muestra los conceptos de estadística descriptiva..Diapositiva del libro de Anderson de estadística aplicada a los negocios y la economía, muestra los conceptos de estadística descriptiva
As mentioned earlier, the mid-term will have conceptual and quanti.docxfredharris32
As mentioned earlier, the mid-term will have conceptual and quantitative multiple-choice questions. You need to read all 4 chapters and you need to be able to solve problems in all 4 chapters in order to do well in this test.
The following are for review and learning purposes only. I am not indicating that identical or similar problems will be in the test. As I have indicated in the class syllabus, all the exams in this course will have multiple-choice questions and problems.
Suggestion: treat this review set as you would an actual test. Sit down with your one page of notes and your calculator, and give it a try. That way you will know what areas you still need to study.
ADMN 210
Answers to Review for Midterm #1
1) Classify each of the following as nominal, ordinal, interval, or ratio data.
a. The time required to produce each tire on an assembly line – ratio since it is numeric with a valid 0 point meaning “lack of”
b. The number of quarts of milk a family drinks in a month - ratio since it is numeric with a valid 0 point meaning “lack of”
c. The ranking of four machines in your plant after they have been designated as excellent, good, satisfactory, and poor – ordinal since it is ranking data only
d. The telephone area code of clients in the United States – nominal since it is a label
e. The age of each of your employees - ratio since it is numeric with a valid 0 point meaning “lack of”
f. The dollar sales at the local pizza house each month - ratio since it is numeric with a valid 0 point meaning “lack of”
g. An employee’s identification number – nominal since it is a label
h. The response time of an emergency unit - ratio since it is numeric with a valid 0 point meaning “lack of”
2) True or False: The highest level of data measurement is the ratio-level measurement.
True (you can do the most powerful analysis with this kind of data)
3) True or False: Interval- and ratio-level data are also referred to as categorical data.
False (Interval and ratio level data are numeric and therefore quantitative, NOT qualitative….Nominal is qualitative)
4) A small portion or a subset of the population on which data is collected for conducting statistical analysis is called __________.
A sample! A population is the total group, a census IS the population, and a data set can be either a sample or a population.
5) One of the advantages for taking a sample instead of conducting a census is this:
a sample is more accurate than census
a sample is difficult to take
a sample cannot be trusted
a sample can save money when data collection process is destructive
6) Selection of the winning numbers is a lottery is an example of __________.
convenience sampling
random sampling
nonrandom sampling
regulatory sampling
7) A type of random sampling in which the population is divided into non-overlapping subpopulations is called __________.
stratified random sampling
cluster sampling
systematic random sampling
regulatory sampling
8) A ...
Frequency distribution, types of frequency distribution.
Ungrouped frequency distribution
Grouped frequency distribution
Cumulative frequency distribution
Relative frequency distribution
Relative cumulative frequency distribution
Graphical representation of frequency distribution
I. Representation of Grouped data
1.Line graphs
2.Bar diagrams
a) Simple bar diagram
b)Multiple/Grouped bar diagram
c)Sub-divided bar diagram.
d) % bar diagram
3. Pie charts
4.Pictogram
II. Graphical representation of ungrouped data
1, Histogram
2.Frequency polygon
3.Cumulative change diagram
4. Proportional change diagram
5. Ratio diagram
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
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
4. Summarizing Qualitative Data
Frequency Distribution (shows how
many)
Relative Frequency Distribution (shows
what fraction)
Percent Frequency Distribution (shows
what percentage)
Bar Graph
Pie Chart
Both these are graphical means for
displaying any of above.
5. Data – any set of information
that describes a given identity
• It an be
• GROUPED DATA is a data that has been
organized into classes. This data is no longer
“raw”
• UNGROUPED DATA is simply an arrangement
of data from lowest to highest.
A data class is a group of data which is related by
some user defined property
Each of those classes is of a certain width and
this is referred to as class width or class size.
7. Calculating Class interval or
Class Size
• Class interval = Higest Value – Lowest
Value
Number of classes
you want to have
• or
• Class interval =
HV - LV
= Range
•
k
k
• Where k is equal to 1 + 3.3 log n
8. Frequency Distribution
A frequency distribution is a tabular summary of
A frequency distribution is a tabular summary of
data showing the frequency (or number) of items
data showing the frequency (or number) of items
in each of several nonoverlapping classes.
in each of several nonoverlapping classes.
The objective is to provide insights about the data
The objective is to provide insights about the data
that cannot be quickly obtained by looking only at
that cannot be quickly obtained by looking only at
the original data.
the original data.
9. Example: Miranda Inn
•
•
•
•
•
Guests staying at Miranda Inn were
asked to rate the quality of their
accommodations as being excellent,
above average, average, below average, or
poor. The ratings provided by a sample of 20 guests are:
Below Average
Above Average
Above Average
Average
Above Average
Average
Above Average
Average
Above Average
Below Average
Poor
Excellent
Above Average
Average
Above Average
Above Average
Below Average
Poor
Above Average
Average
Average
11. Relative Frequency Distribution
The relative frequency of a class is the fraction or
The relative frequency of a class is the fraction or
proportion of the total number of data items
proportion of the total number of data items
belonging to the class.
belonging to the class.
A relative frequency distribution is a tabular
A relative frequency distribution is a tabular
summary of a set of data showing the relative
summary of a set of data showing the relative
frequency for each class.
frequency for each class.
12. Percent Frequency
Distribution
The percent frequency of a class is the relative
The percent frequency of a class is the relative
frequency multiplied by 100.
frequency multiplied by 100.
A percent frequency distribution is a tabular
A percent frequency distribution is a tabular
summary of a set of data showing the percent
summary of a set of data showing the percent
frequency for each class.
frequency for each class.
13. Relative Frequency and
Percent Frequency Distributions
Relative
Frequency
Rating
.10
Poor
.15
Below Average
.25
Average
.45
Above Average
.05
Excellent
Total
1.00
Percent
Frequency
10
15
25 .10(100) = 10
45
5
100
1/20 = .05
14. Bar Graph
A bar graph is a graphical device for depicting
qualitative data.
On one axis (usually the horizontal axis), we specify
the labels that are used for each of the classes.
A frequency, relative frequency, or percent frequency
scale can be used for the other axis (usually the
vertical axis).
Using a bar of fixed width drawn above each class
label, we extend the height appropriately.
The bars are separated to emphasize the fact that each
class is a separate category.
15. Bar Graph
Good?
Bad?
Miranda Inn Quality Ratings
10
9
Frequency
8
7
6
5
4
3
2
1
Poor
Below Average Above Excellent
Average
Average
Rating
16. Pie Chart
The pie chart is a commonly used graphical device
for presenting relative frequency distributions for
qualitative data.
First draw a circle; then use the relative
frequencies to subdivide the circle
into sectors that correspond to the
relative frequency for each class.
Since there are 360 degrees in a circle,
a class with a relative frequency of .25 would
consume .25(360) = 90 degrees of the circle.
17. Pie Chart
Miranda Inn Quality Ratings
Excellent
5%
Poor
10%
Above
Average
45%
Below
Average
15%
Average
25%
18. Example: Miranda Inn
Insights Gained from the Preceding Pie Chart
• One-half of the customers surveyed gave Miranda
a quality rating of “above average” or “excellent”
(looking at the left side of the pie). This might
please the manager.
• For each customer who gave an “excellent” rating,
there were two customers who gave a “poor”
rating (looking at the top of the pie). This should
displease the manager.
20. Example: Juson Auto Repair
The manager of Juson Auto
would like to have a better
understanding of the cost
of parts used in the engine
tune-ups performed in the
shop. She examines 50
customer invoices for tune-ups. The costs of parts,
rounded to the nearest dollar, are listed on the next
slide.
21. Example: Juson Auto Repair
Sample of Parts Cost for 50 Tune-ups
91
71
104
85
62
78
69
74
97
82
93
72
62
88
98
57
89
68
68
101
75
66
97
83
79
52
75
105
68
105
99
79
77
71
79
Including a line in the table for every
possible cost is not a good idea.
Need to categorize.
80
75
65
69
69
97
72
80
67
62
62
76
109
74
73
22. Frequency Distribution
Guidelines for Selecting Number of
Classes
• Use between 5 and 20 classes.
• Data sets with a larger number of elements
usually require a larger number of classes.
• Smaller data sets usually require fewer classes
23. Frequency Distribution
Guidelines for Selecting Width of
Classes
•Use classes of equal width.
•Approximate Class Width =
Largest Data Value − Smallest Data Value
Number of Classes
24. Frequency Distribution
•
For Juson Auto Repair, if we choose six
classes:
Approximate Class Width = (109 - 52)/6 = 9.5 ≅ 10
Parts Cost ($) Frequency
50-59
2
60-69
13
70-79
16
80-89
7
90-99
7
100-109
5
Total
50
25. Preview cumulative frequencies here.
Relative Frequency and
Percent Frequency Distributions
Parts
Relative
Percent
Cost ($) Frequency
Frequency
50-59
.04
4
60-69
.26
2/50 26 .04(100)
70-79
.32
32
80-89
.14
14
90-99
.14
14
100-109
.10
10
Total 1.00
100
26. Relative Frequency and
Percent Frequency Distributions
Insights Gained from the Percent Frequency
Distribution
• Only 4% of the parts costs are in the $50-59 class.
• 30% of the parts costs are under $70.
• The greatest percentage (32% or almost one-third)
of the parts costs are in the $70-79 class.
• 10% of the parts costs are $100 or more.
27. Dot Plot
One of the simplest graphical
summaries of data is a dot plot.
A horizontal axis shows the range of
data values.
Then each data value is represented by
a dot placed above the axis.
28. Dot Plot
Tune-up Parts Cost
.
50
.
. .. . .
.
. .. .. .. ..
.
.
. . ..... .......... .. . .. . . ... . .. .
60
70
80
90
Cost ($)
Not used much anymore. Common when
graphical drawing tools were primitive.
100
110
29. Histogram
Another common graphical presentation of
quantitative data is a histogram.
The variable of interest is placed on the horizontal
axis.
A rectangle is drawn above each class interval with
its height corresponding to the interval’s frequency,
relative frequency, or percent frequency.
Unlike a bar graph, a histogram has no natural
separation between rectangles of adjacent classes.
In informal discussions bar graphs and histograms are
often equated. In this class you should be careful to
keep them straight.
34. Histogram
Highly Skewed Right
−
−
A very long tail to the right
Example: executive salaries
.35
Relative Frequency
.30
.25
.20
.15
.10
.05
0
35. Cumulative Distributions
Cumulative frequency distribution − shows the
Cumulative frequency distribution − shows the
number of items with values less than or equal to
number of items with values less than or equal to
the upper limit of each class..
the upper limit of each class..
Cumulative relative frequency distribution – shows
Cumulative relative frequency distribution – shows
the proportion of items with values less than or
the proportion of items with values less than or
equal to the upper limit of each class.
equal to the upper limit of each class.
Cumulative percent frequency distribution – shows
Cumulative percent frequency distribution – shows
the percentage of items with values less than or
the percentage of items with values less than or
equal to the upper limit of each class.
equal to the upper limit of each class.
36. Cumulative Distributions
Hudson Auto Repair
Cost ($)
< 59
< 69
< 79
< 89
< 99
< 109
Cumulative Cumulative
Cumulative
Relative
Percent
Frequency
Frequency
Frequency
2
.04
4
15
.30
30
31 2 + 13 .62 15/50 62 .30(100)
38
.76
76
45
.90
90
50
1.00
100
Cumulative frequency distribution − shows the
Cumulative frequency distribution − shows the
number of items with values less than or equal to
number of items with values less than or equal to
the upper limit of each class..
the upper limit of each class..
37. Ogive
An ogive is a graph of a cumulative distribution.
The data values are shown on the horizontal axis.
Shown on the vertical axis are the:
• cumulative frequencies, or
• cumulative relative frequencies, or
• cumulative percent frequencies
The frequency (one of the above) of each class is
plotted as a point.
The plotted points are connected by straight lines.
38. Ogive
Hudson Auto Repair
• Because the class limits for the parts-cost data are
50-59, 60-69, and so on, there appear to be one-unit
gaps from 59 to 60, 69 to 70, and so on.
• These gaps are eliminated by plotting points
halfway between the class limits.
• Thus, 59.5 is used for the 50-59 class, 69.5 is used
for the 60-69 class, and so on.
39. Ogive with
Cumulative Percent Frequencies
Cumulative Percent Frequency
Tune-up Parts Cost
Tune-up Parts Cost
100
80
60
(89.5, 76)
40
20
50
60
70
80
90
100
110
Parts
Cost ($)