Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docxpooleavelina
Homework #1
SOCY 3115
Spring 20
Read the Syllabus and FAQ on how to do your homework before beginning the assignment!
To get consideration for full credit, you must:
· Follow directions;
· Show all work required to arrive at answer (statistical calculations often require multiple steps, so you need to write these down, not just skip to the final answer)
· Use appropriate statistical notation at all times (e.g. if you are calculating a population mean, begin with the equation for population mean)
· Use units in your answer, where appropriate (e.g. a mean time would be “6.5 hours” rather than just “6.5”)
Understanding the Structure of Data
1. For the following rectangular dataset:
Id
Highest degree
Works full-time
Annual income cat
1
Did not grad HS
Yes
Low
2
HS dip
Yes
Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
7
HS dip
Yes
Med
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For eachvariable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement?
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
2. For the following rectangular dataset:
Id
num_bdrms
num_bthrms
sqft
Ranch
1
4
3
3200
Yes
2
2
1.5
2800
Yes
3
2
1
1200
Yes
4
3
2
1500
No
5
2
2
1100
No
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For each variable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
Statistics Informed Decisions Using Data 5th edition by Michael Sullivan solu...ssuserf63bd7
https://qidiantiku.com/solution-manual-for-statistics-informed-decisions-using-data-5th-edition-by-michael-sullivan.shtml
Statistics Informed Decisions Using Data 5th edition by Michael Sullivan solution manual.pdf
name:Solution manual for Statistics: Informed Decisions Using Data 5th edition by Michael Sullivan
Edition:5th edition
author:by Michael Sullivan III
ISBN:ISBN-10 : 0134135377
ISBN-13 : 9780134135373
type:solution manual
format:word/zip
All chapter include
The changes of our world Free Essay Example. IS THE WORLD CHANGING FOR THE BETTER ESSAY – meciwepyl. Essays that changed the world. Is The World Changing For The Better Sat Essay Rubric. 002 Essay Example How To Make The World Better Place Science Can Help .... If I Could Change the World Essay: Examples & Writing Guide. Our Changing World - 1477 Words | Free Essay Example on GraduateWay. (DOC) Essay Educators for a changing world | Eveline Rose - Academia.edu.
Chapter 17 (Salkind)What To Do When You’re Not Normal.docxketurahhazelhurst
Chapter 17 (Salkind)
What To Do When You’re Not Normal
Overview of this ChapterThe Good News and the Bad News
First up, the Bad News. Once again, we will look at statistics. Here, that means the Chi Square, a type of statistics we rely on when our scales are nominal or ordinal
The other Bad News is that this there are formulas and tables associated with this chapter. I know, ugh
The Good News? Some of this might be a review! But you will need some of the new information here as you work on one statistical calculation for your research paper: The Chi Square
Overview of this ChapterIn this chapter, we will focus on …
Part One: Introduction To Non-Parametric Statistics
Part Two (A): Introduction To The One-Sample Chi-Square
Part Two (B): Chi Square Test Of Independence
Part Three: Computing The Chi-Square Statistic
Part Four: Using The Computer To Perform A Chi-Square Test
Part Five: Other Non-Parametric Tests You Should Know
Part Six: An Eye Toward The Future
Part One
Introduction To Non-Parametric Statistics
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Last semester in Research Methods and Design One (and last week in Chapter 9, Smith and Davis), we talked about normal curves and why we need normality in order to run ANOVAs, t-Tests, and other “parametric” tests.
“Parametric tests” infer that the results obtained from a sample in the study easily applies to a population from which that sample was drawn. But such “normal” tests are based on a series of assumptions …
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Four parametric test assumptions:
Assumption #1: Variances in each group are homogenous (that is, the two or more groups are similar in variability)
Assumption #2: The sample is large enough to adequately represent the population (e.g. it isn’t a biased sample)
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Four parametric test assumptions:
Assumption #3: The statistical test uses interval or ratio scales of measurement (the I and R in NOIR)
Assumption #4: The characteristic under consideration is normally distributed (i.e. has a normal curve)
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
So what happens when/if a test violates these assumptions?
In some cases, t-Tests, ANOVAs, and other parametric tests are robust (e.g. strong enough) that the assumptions can be violated without too much hassle.
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
So what happens when/if a test violates these assumptions?
Non-parametric tests may be used when assumptions are violated
“Non-parametric” statistics are essentially distribution-free, meaning they don’t follow the same rules as the parametric tests
They don’t require homogeneity of variance and they can examine more than just interval and ratio data
Introduction - Non-Parametric Stat ...
Chapter 17 (Salkind)What To Do When You’re Not Normal.docxwalterl4
Chapter 17 (Salkind)
What To Do When You’re Not Normal
Overview of this ChapterThe Good News and the Bad News
First up, the Bad News. Once again, we will look at statistics. Here, that means the Chi Square, a type of statistics we rely on when our scales are nominal or ordinal
The other Bad News is that this there are formulas and tables associated with this chapter. I know, ugh
The Good News? Some of this might be a review! But you will need some of the new information here as you work on one statistical calculation for your research paper: The Chi Square
Overview of this ChapterIn this chapter, we will focus on …
Part One: Introduction To Non-Parametric Statistics
Part Two (A): Introduction To The One-Sample Chi-Square
Part Two (B): Chi Square Test Of Independence
Part Three: Computing The Chi-Square Statistic
Part Four: Using The Computer To Perform A Chi-Square Test
Part Five: Other Non-Parametric Tests You Should Know
Part Six: An Eye Toward The Future
Part One
Introduction To Non-Parametric Statistics
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Last semester in Research Methods and Design One (and last week in Chapter 9, Smith and Davis), we talked about normal curves and why we need normality in order to run ANOVAs, t-Tests, and other “parametric” tests.
“Parametric tests” infer that the results obtained from a sample in the study easily applies to a population from which that sample was drawn. But such “normal” tests are based on a series of assumptions …
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Four parametric test assumptions:
Assumption #1: Variances in each group are homogenous (that is, the two or more groups are similar in variability)
Assumption #2: The sample is large enough to adequately represent the population (e.g. it isn’t a biased sample)
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Four parametric test assumptions:
Assumption #3: The statistical test uses interval or ratio scales of measurement (the I and R in NOIR)
Assumption #4: The characteristic under consideration is normally distributed (i.e. has a normal curve)
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
So what happens when/if a test violates these assumptions?
In some cases, t-Tests, ANOVAs, and other parametric tests are robust (e.g. strong enough) that the assumptions can be violated without too much hassle.
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
So what happens when/if a test violates these assumptions?
Non-parametric tests may be used when assumptions are violated
“Non-parametric” statistics are essentially distribution-free, meaning they don’t follow the same rules as the parametric tests
They don’t require homogeneity of variance and they can examine more than just interval and ratio data
Introduction - Non-Parametric Stat.
2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docxeugeniadean34240
2. This exercise uses the dataset “WholeFoods.”
(a) Use Excel to construct a relative histogram for store size. Does the distribution of store size appear to be skewed? If so, does it appear to be skewed to the right or to the left? Explain.
(b) Use Excel to calculate the following four measures of central tendency for store size: mean, median, midrange, and 5% trimmed mean (using the trimmed mean definition from the textbook). Do any of these measures of central tendency appear to not be appropriate for this particular dataset? Explain.
(c) Use Excel to calculate the following four measures of dispersion for store size: variance, standard deviation, mean absolute deviation, and coefficient of variation. Please provide brief and “to-the-point” comments on your results.
(d) According to Chebyshev’s Theorem, at least what percentage of the observations within a sample is supposed to lie within 1.5 sample standard deviations of the sample mean? Next, using Excel, please take the observations for store size in the Whole Foods dataset and confirm that this prediction holds within the Whole Foods sample dataset.
(e) Use Excel to calculate the first quartile, the third quartile, the midhinge, and interquartile range for store size. Next, use Excel to create a box plot graph for store size. (Note: Excel does not have a built-in function for creating a box plot. Your group will need to “figure out” how to do it. For example, the internet has many examples of how to create a box plot in Excel using column/bar charts. You may do either a “horizontal” box plot (i.e., a box plot with the “whiskers” pointing to the right and to the left) or a “vertical” box plot (i.e., a box plot with the “whiskers” pointing to the top and to the bottom).)
(f) Use Excel to calculate both inner fences (left and right) for store size, and then both outer fences (left and right) for store size. Based on these calculated values, are there any “outlier” stores in the data? Any “extreme outlier” stores in the data? If so, which stores are they? (Note: In answering this question, please use the definition of “outlier” and “extreme outlier” provided on page 144 of the textbook; please do not use the definition of “outlier” provided on pages 135-137 of the textbook.) (g) Use Excel to calculate skewness for the variable store size. Is store size skewed right or left? Does your answer corroborate the answer you provided in part 2(a) above?
18 Chapter 1 Exploring Life and Science
• reproduce; and experience growth, and in many cases
development;
• maintain homeostasis to maintain the conditions of an internal
environment;
• respond to stimuli; and
• have an evolutionary history and are adapted to a way oflife.
1.2 Humans Are Related to Other Animals
The classification ofliving organisms mirrors their evolutionary
relationships. Humans are mammals, a type of vertebrate in the
animal kingdom ofthe domain Eukarya. Humans differ from other
mammals, including apes, .
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Elementary Statistics Practice Test 1
Module 1: Chapters 1-3
Chapter 1: Introduction to Statistics.
Chapter 2: Exploring Data with Tables and Graphs.
Chapter 3: Describing, Exploring, and Comparing Data.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docxpooleavelina
Homework #1
SOCY 3115
Spring 20
Read the Syllabus and FAQ on how to do your homework before beginning the assignment!
To get consideration for full credit, you must:
· Follow directions;
· Show all work required to arrive at answer (statistical calculations often require multiple steps, so you need to write these down, not just skip to the final answer)
· Use appropriate statistical notation at all times (e.g. if you are calculating a population mean, begin with the equation for population mean)
· Use units in your answer, where appropriate (e.g. a mean time would be “6.5 hours” rather than just “6.5”)
Understanding the Structure of Data
1. For the following rectangular dataset:
Id
Highest degree
Works full-time
Annual income cat
1
Did not grad HS
Yes
Low
2
HS dip
Yes
Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
7
HS dip
Yes
Med
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For eachvariable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement?
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
2. For the following rectangular dataset:
Id
num_bdrms
num_bthrms
sqft
Ranch
1
4
3
3200
Yes
2
2
1.5
2800
Yes
3
2
1
1200
Yes
4
3
2
1500
No
5
2
2
1100
No
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For each variable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
Statistics Informed Decisions Using Data 5th edition by Michael Sullivan solu...ssuserf63bd7
https://qidiantiku.com/solution-manual-for-statistics-informed-decisions-using-data-5th-edition-by-michael-sullivan.shtml
Statistics Informed Decisions Using Data 5th edition by Michael Sullivan solution manual.pdf
name:Solution manual for Statistics: Informed Decisions Using Data 5th edition by Michael Sullivan
Edition:5th edition
author:by Michael Sullivan III
ISBN:ISBN-10 : 0134135377
ISBN-13 : 9780134135373
type:solution manual
format:word/zip
All chapter include
The changes of our world Free Essay Example. IS THE WORLD CHANGING FOR THE BETTER ESSAY – meciwepyl. Essays that changed the world. Is The World Changing For The Better Sat Essay Rubric. 002 Essay Example How To Make The World Better Place Science Can Help .... If I Could Change the World Essay: Examples & Writing Guide. Our Changing World - 1477 Words | Free Essay Example on GraduateWay. (DOC) Essay Educators for a changing world | Eveline Rose - Academia.edu.
Chapter 17 (Salkind)What To Do When You’re Not Normal.docxketurahhazelhurst
Chapter 17 (Salkind)
What To Do When You’re Not Normal
Overview of this ChapterThe Good News and the Bad News
First up, the Bad News. Once again, we will look at statistics. Here, that means the Chi Square, a type of statistics we rely on when our scales are nominal or ordinal
The other Bad News is that this there are formulas and tables associated with this chapter. I know, ugh
The Good News? Some of this might be a review! But you will need some of the new information here as you work on one statistical calculation for your research paper: The Chi Square
Overview of this ChapterIn this chapter, we will focus on …
Part One: Introduction To Non-Parametric Statistics
Part Two (A): Introduction To The One-Sample Chi-Square
Part Two (B): Chi Square Test Of Independence
Part Three: Computing The Chi-Square Statistic
Part Four: Using The Computer To Perform A Chi-Square Test
Part Five: Other Non-Parametric Tests You Should Know
Part Six: An Eye Toward The Future
Part One
Introduction To Non-Parametric Statistics
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Last semester in Research Methods and Design One (and last week in Chapter 9, Smith and Davis), we talked about normal curves and why we need normality in order to run ANOVAs, t-Tests, and other “parametric” tests.
“Parametric tests” infer that the results obtained from a sample in the study easily applies to a population from which that sample was drawn. But such “normal” tests are based on a series of assumptions …
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Four parametric test assumptions:
Assumption #1: Variances in each group are homogenous (that is, the two or more groups are similar in variability)
Assumption #2: The sample is large enough to adequately represent the population (e.g. it isn’t a biased sample)
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Four parametric test assumptions:
Assumption #3: The statistical test uses interval or ratio scales of measurement (the I and R in NOIR)
Assumption #4: The characteristic under consideration is normally distributed (i.e. has a normal curve)
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
So what happens when/if a test violates these assumptions?
In some cases, t-Tests, ANOVAs, and other parametric tests are robust (e.g. strong enough) that the assumptions can be violated without too much hassle.
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
So what happens when/if a test violates these assumptions?
Non-parametric tests may be used when assumptions are violated
“Non-parametric” statistics are essentially distribution-free, meaning they don’t follow the same rules as the parametric tests
They don’t require homogeneity of variance and they can examine more than just interval and ratio data
Introduction - Non-Parametric Stat ...
Chapter 17 (Salkind)What To Do When You’re Not Normal.docxwalterl4
Chapter 17 (Salkind)
What To Do When You’re Not Normal
Overview of this ChapterThe Good News and the Bad News
First up, the Bad News. Once again, we will look at statistics. Here, that means the Chi Square, a type of statistics we rely on when our scales are nominal or ordinal
The other Bad News is that this there are formulas and tables associated with this chapter. I know, ugh
The Good News? Some of this might be a review! But you will need some of the new information here as you work on one statistical calculation for your research paper: The Chi Square
Overview of this ChapterIn this chapter, we will focus on …
Part One: Introduction To Non-Parametric Statistics
Part Two (A): Introduction To The One-Sample Chi-Square
Part Two (B): Chi Square Test Of Independence
Part Three: Computing The Chi-Square Statistic
Part Four: Using The Computer To Perform A Chi-Square Test
Part Five: Other Non-Parametric Tests You Should Know
Part Six: An Eye Toward The Future
Part One
Introduction To Non-Parametric Statistics
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Last semester in Research Methods and Design One (and last week in Chapter 9, Smith and Davis), we talked about normal curves and why we need normality in order to run ANOVAs, t-Tests, and other “parametric” tests.
“Parametric tests” infer that the results obtained from a sample in the study easily applies to a population from which that sample was drawn. But such “normal” tests are based on a series of assumptions …
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Four parametric test assumptions:
Assumption #1: Variances in each group are homogenous (that is, the two or more groups are similar in variability)
Assumption #2: The sample is large enough to adequately represent the population (e.g. it isn’t a biased sample)
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
Four parametric test assumptions:
Assumption #3: The statistical test uses interval or ratio scales of measurement (the I and R in NOIR)
Assumption #4: The characteristic under consideration is normally distributed (i.e. has a normal curve)
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
So what happens when/if a test violates these assumptions?
In some cases, t-Tests, ANOVAs, and other parametric tests are robust (e.g. strong enough) that the assumptions can be violated without too much hassle.
Introduction - Non-Parametric StatisticsIntroduction To Non-Parametric Statistics
So what happens when/if a test violates these assumptions?
Non-parametric tests may be used when assumptions are violated
“Non-parametric” statistics are essentially distribution-free, meaning they don’t follow the same rules as the parametric tests
They don’t require homogeneity of variance and they can examine more than just interval and ratio data
Introduction - Non-Parametric Stat.
2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docxeugeniadean34240
2. This exercise uses the dataset “WholeFoods.”
(a) Use Excel to construct a relative histogram for store size. Does the distribution of store size appear to be skewed? If so, does it appear to be skewed to the right or to the left? Explain.
(b) Use Excel to calculate the following four measures of central tendency for store size: mean, median, midrange, and 5% trimmed mean (using the trimmed mean definition from the textbook). Do any of these measures of central tendency appear to not be appropriate for this particular dataset? Explain.
(c) Use Excel to calculate the following four measures of dispersion for store size: variance, standard deviation, mean absolute deviation, and coefficient of variation. Please provide brief and “to-the-point” comments on your results.
(d) According to Chebyshev’s Theorem, at least what percentage of the observations within a sample is supposed to lie within 1.5 sample standard deviations of the sample mean? Next, using Excel, please take the observations for store size in the Whole Foods dataset and confirm that this prediction holds within the Whole Foods sample dataset.
(e) Use Excel to calculate the first quartile, the third quartile, the midhinge, and interquartile range for store size. Next, use Excel to create a box plot graph for store size. (Note: Excel does not have a built-in function for creating a box plot. Your group will need to “figure out” how to do it. For example, the internet has many examples of how to create a box plot in Excel using column/bar charts. You may do either a “horizontal” box plot (i.e., a box plot with the “whiskers” pointing to the right and to the left) or a “vertical” box plot (i.e., a box plot with the “whiskers” pointing to the top and to the bottom).)
(f) Use Excel to calculate both inner fences (left and right) for store size, and then both outer fences (left and right) for store size. Based on these calculated values, are there any “outlier” stores in the data? Any “extreme outlier” stores in the data? If so, which stores are they? (Note: In answering this question, please use the definition of “outlier” and “extreme outlier” provided on page 144 of the textbook; please do not use the definition of “outlier” provided on pages 135-137 of the textbook.) (g) Use Excel to calculate skewness for the variable store size. Is store size skewed right or left? Does your answer corroborate the answer you provided in part 2(a) above?
18 Chapter 1 Exploring Life and Science
• reproduce; and experience growth, and in many cases
development;
• maintain homeostasis to maintain the conditions of an internal
environment;
• respond to stimuli; and
• have an evolutionary history and are adapted to a way oflife.
1.2 Humans Are Related to Other Animals
The classification ofliving organisms mirrors their evolutionary
relationships. Humans are mammals, a type of vertebrate in the
animal kingdom ofthe domain Eukarya. Humans differ from other
mammals, including apes, .
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Elementary Statistics Practice Test 1
Module 1: Chapters 1-3
Chapter 1: Introduction to Statistics.
Chapter 2: Exploring Data with Tables and Graphs.
Chapter 3: Describing, Exploring, and Comparing Data.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
Speakers:
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2. Statistics include numerical facts and
figures such as:
The largest earthquake measured 9.2 on the Richter
scale.
Men are at least 10 times more likely than women to
commit murder.
One in every 8 South Africans is HIV positive.
By the year 2020, there will be 15 people aged 65
and over for every new baby born.
3. Why should we study statistics?
We want to make sense of the world.
We don’t want to be lied to.
We want to be able to spot trends that help us make
good decisions.
4. 4 Main Themes:
I. Exploring data (compare graphs and numbers)
II. Sampling & Experimentation (collect data)
III. Anticipating Patterns (Probability and Simulation)
IV. Statistical Inference (Make conclusions)
5. Individuals are the objects described by a set of data.
Variable is any characteristic of an individual.
EXAMPLE: An AP Statistics class list contains information
about the students enrolled. The students enrolled are the
individuals, and for each individual, there are variables
such as student number, gender and home phone number.
6. Check For Understanding
You want to compare the “size of several AP statistics
textbooks. Give at least three possible numerical variables
that describe the “size” of a book. In what units would you
measure each variable?
7. Possible Answers (units)
Number of pages (pages)
Number of words (words)
Volume (cubic inches, cubic centimeters)
Height and/or width (pounds, ounces)
Number of chapters (chapters)
Weight (pounds, ounces)
8. Types of variables:
A categorical variable places an individual into one of
several groups or categories.
Examples: gender, letter grade, zip code
A quantitative variable takes numerical values for which
arithmetic operations such as adding and averaging make
sense.
Examples: scores on a test, age, GPA
9. Check for Understanding
Data from a medical study contain values of many variables
for each of the people who were the subjects of the study.
Which of the following variables are categorical and which are
quantitative?
(a) gender (female or male)
(b) Age (years)
(c) Race (Asian, black, white,or other)
(d) Smoker (yes or no)
(e) Systolic blood pressure (millimeters of mercury)
(f) Level of calcium in the blood (micrograms per milliliter)
11. The distribution of a variable is a list (chart, picture,
etc.) that tells us what values the variable takes and
how often it takes these values.
Most variables take on values that vary. Sometimes
those values are clustered close together, and other
times they are spread far apart. When we look at
how those values vary, we are looking at the
distribution of the variable.
12. Important features of a distribution:
Shape
Is the distribution symmetric or skewed?
Outliers
Are there individual observation that falls outside the overall
pattern of the graph?
Center
Is there a typical or most common value of the variable? Is
there a mode? Bimodal?
Spread
What is the range of possible values? How wide is the
distribution?
13. Check For Understanding
The histogram below shows the
distribution of total returns for all
1528 stocks listed on the New
York State Exchange in one year.
(a) Describe the overall shape of the
distribution of total returns.
(b) What is the approximate center
of this distribution? (For now,
take the center to be the value
with roughly half the stocks
having lower returns and half
having higher returns.)
(c) Approximately what were the
smallest and largest total
returns? (This describes the
spread of the distribution)
14. Answers
(a) Roughly symmetric, though it might be viewed as SLIGHTLY
skewed to the right.
(b) About 15%. (39% of the stocks had a total return less than
10%, while 60% had a return less than 20%. This places the
center of the distribution somewhere between 10% and
20%.)
(c) The smallest return was between -70% and -60%, while the
largest was between 100% and 110%.
15. When analyzing data,
ask the following:
Who are the individuals being described?
What are the variables?
Why were the data gathered?
When, where, how, and by whom were the data
produced?
16. Check For Understanding
In October 2005 Discover published an article on the colonies of ants. They
reported some basic information about many species of ants and the results of
some discoveries found by myrmecologist Walter Tschinkel of the University of
Florida. Information included the scientific name of the ant species, the
geographic location, the depth of the nest (in feet), the number of chambers in
the nest, and the number of ants in the colony. The article documented how new
ant colonies begin, the ant-nest design, and how nests differ in shape, number,
size of chambers, and how they are connected, depending on the species. It
reported that nest designs include vertical, horizontal, or inclined tunnels for
movement and transport of food and ants.
Describe the W's, if the information is given. If the information is not given, state
that it is not specified.
Who:
What:
When:
Where:
How:
Why:
17. Answers
Who: Colonies of ants. "Many species of ants," but no
indication of exactly how many.
What: scientific name, geographic location, average nest
depth, average number of chambers, average colony size,
how new ant colonies begin, the ant-nest design, and how
nests differ in architecture.
When: October 2005
Where: not specified
How: The results of some discoveries found by myrmecologist
Walter Tschinkel of the University of Florida
Why: Information of interest to readers of the magazine