- 200 students applied to the University of Manitoba, with 25% (50 students) having not been to the university before.
- The mean number of students who had not been is 50, with a standard deviation of 5.3.
- A 95% confidence interval for the number of students who had not been is between 40 and 60 students.
Confidence Interval ModuleOne of the key concepts of statist.docxmaxinesmith73660
Confidence Interval Module
One of the key concepts of statistics enabling statisticians to make incredibly accurate predictions is called the Central Limit Theorem. The Central Limit Theorem is defined in this way:
· For samples of a sufficiently large size, the real distribution of means is almost always approximately normal.
· The distribution of means gets closer and closer to normal as the sample size gets larger and larger, regardless of what the original variable looks like (positively or negatively skewed).
· In other words, the original variable does not have to be normally distributed.
· This is because, if we as eccentric researchers, drew an almost infinite number of random samples from a single population (such as the student body of NMSU), the means calculated from the many samples of that population will be normally distributed and the mean calculated from all of those samples would be a very close approximation to the true population mean. It is this very characteristic that makes it possible for us, using sound probability based sampling techniques, to make highly accurate statements about characteristics of a population based upon the statistics calculated on a sample drawn from that population.
· Furthermore, we can calculate a statistic known as the standard error of the mean (abbreviated s.e.) that describes the variability of the distribution of all possible sample means in the same way that we used the standard deviation to describe the variability of a single sample. We will use the standard error of the mean (s.e.) to calculate the statistic that is the topic of this module, the confidence interval.
The formula that we use to calculate the standard error of the mean is:
s.e. = s / √N – 1
where s = the standard deviation calculated from the sample; and
N = the sample size.
So the formula tells us that the standard error of the mean is equal to the
standard deviation divided by the square root of the sample size minus 1.
This is the preferred formula for practicing professionals as it accounts for errors that may be a function of the particular sample we have selected.
THE CONFIDENCE INTERVAL (CI)
The formula for the CI is a function of the sample size (N).
For samples sizes ≥ 100, the formula for the CI is:
CI = (the sample mean) + & - Z(s.e.).
Let’s look at an example to see how this formula works.
* Please use a pdf doc. “how to solve the problem”, I have provided for you under the “notes” link.
Example 1
Suppose that we conducted interviews with 140 randomly selected individuals (N = 140) in a large metropolitan area. We assured these individuals that their answers would remain confidential, and we asked them about their law-breaking behavior. Among other questions the individuals were asked to self-report the number of times per month they exceeded the speed limit. One of the objectives of the study was to estimate (make an inference about) the average nu.
- Aow
-Aow
f--,
d--Tto o4*prnbAu
SDUJ?_
pya
I
LjrrtripA)c€ IttTS4nOt Mooute
U : YA€aru
! ZG.)=
t,
= rneat-!
Z GltFu)
l
Exa^lrle-4,' N
_
# 44o
bon a*^
wwan=4r.L{'mes
<tabk
S= s.zhnes = 4-96
,,.7,
.XSoo
ll =.Q\So
W=rne&,\s,
Ywe Knot{lWeacL,siob --:4.96s
-
42 ,t
t 4o 5t/. (. arr"[hb,r/olrr.r*WItttt*v
'./\
, sorc/z=.025o=2e/
s5/.C7=
42,4tqlte&r/f,*-ot) to/
35/.eJ=.12.+ g;GZlfi=ee) '
Il :5e/{
gsfT=42,+t4sGGz/fm)
w.Q=,1A4!46(.t+)
gS/U=42+L-53 : 44-tT g5/ cT =
-l
-l-^-
gtJ.CJ=41,4+ .53 = 42.?3 44.{T p42.?3
lt
&.a,Wux: =.4 5oo
67)* , ?w/q
,45oo=z+il)k=Yu(-q*/ r'--=
,O5oit
'f O5oo
@ia/.
eo/8=42.+t4.432/ftu)
1o/.cI=/1.4! tt.o{ztlfrd 3o/.cT=42tt-.qq=
ry
tjc7./2.+t4.658'z/tr(--J31 + -1't=ry
%/,c]=42,q
90/q= 42.1tl,os[,t+1 '/
9r1,cl,424t .rirt
9o/ cf = l,l,g6<+lx,8q
�
eT-flowlb .n1rea/<06!e,rvr-7wz
_
N=flo,(
rneei=/1.4hnes
lt
.i = 3.2 nrn?s
cT_
=42,1J 4as(st/m)
[G)
c n4t L.65(s.21 -{"6ss aq
-421t l-As(z,a/zo) I,6s
= (2 qt 4".65 CIa)
/2'58
t.48 fuo/.c-T=/2-22'+
= /Q.1
:42.4--'rg-'12'2U='12,4+-/[email protected]
=,u<se
Saft&(4OO
t{=47
-Cf
:Wean! +G.")
= tgrFr) .hd00
ftp.a,rLL
oSoo
,/,2,,1
T+o"Ib=Ly
sv/,q=4e.\t1.++6Ss
"/F)
= t1.1! 4.?t^(s./fnt)
= 19..4/ ,t,7//6/3.2/, 9o/ Cf-44,003+>/3.+97
=t27r t.++e70.8)
= ,14,4!4"g97
12.+-/.397=4(.003
4A.4+ /.39/-fi. ?*
�
Confidence Interval Module
One of the key concepts of statistics enabling statisticians to make incredibly accurate predictions is called the Central Limit Theorem. The Central Limit Theorem is defined in this way:
· For samples of a sufficiently large size, the real distribution of means is almost always approximately normal.
· The distribution of means gets closer and closer to normal as the sample size gets larger and larger, regardless of what the original variable looks like (positively or negatively skewed).
· In other words, the original variable does not have to be normally distributed.
· This is because, if we as eccentric researchers, drew an almost infinite number of random samples from a single population (such as the student body of NMSU), the means calculated from the many samples of that population will be normally distributed and the mean calculated from all of those samples would be a very close approximation to the true population mean. It is this very characteristic that makes it possible for us, using sound probability based sampling techniques, to make highly accurate statements about characteristics of a population based upon the statistics calculated on a sample drawn from that population.
· Furthermore, we can calculate a statistic known as the standard error of the mean (abbreviated s.e.) that describes the variability of the distribution of all possible sample means in the sa ...
Confidence Interval ModuleOne of the key concepts of statist.docxmaxinesmith73660
Confidence Interval Module
One of the key concepts of statistics enabling statisticians to make incredibly accurate predictions is called the Central Limit Theorem. The Central Limit Theorem is defined in this way:
· For samples of a sufficiently large size, the real distribution of means is almost always approximately normal.
· The distribution of means gets closer and closer to normal as the sample size gets larger and larger, regardless of what the original variable looks like (positively or negatively skewed).
· In other words, the original variable does not have to be normally distributed.
· This is because, if we as eccentric researchers, drew an almost infinite number of random samples from a single population (such as the student body of NMSU), the means calculated from the many samples of that population will be normally distributed and the mean calculated from all of those samples would be a very close approximation to the true population mean. It is this very characteristic that makes it possible for us, using sound probability based sampling techniques, to make highly accurate statements about characteristics of a population based upon the statistics calculated on a sample drawn from that population.
· Furthermore, we can calculate a statistic known as the standard error of the mean (abbreviated s.e.) that describes the variability of the distribution of all possible sample means in the same way that we used the standard deviation to describe the variability of a single sample. We will use the standard error of the mean (s.e.) to calculate the statistic that is the topic of this module, the confidence interval.
The formula that we use to calculate the standard error of the mean is:
s.e. = s / √N – 1
where s = the standard deviation calculated from the sample; and
N = the sample size.
So the formula tells us that the standard error of the mean is equal to the
standard deviation divided by the square root of the sample size minus 1.
This is the preferred formula for practicing professionals as it accounts for errors that may be a function of the particular sample we have selected.
THE CONFIDENCE INTERVAL (CI)
The formula for the CI is a function of the sample size (N).
For samples sizes ≥ 100, the formula for the CI is:
CI = (the sample mean) + & - Z(s.e.).
Let’s look at an example to see how this formula works.
* Please use a pdf doc. “how to solve the problem”, I have provided for you under the “notes” link.
Example 1
Suppose that we conducted interviews with 140 randomly selected individuals (N = 140) in a large metropolitan area. We assured these individuals that their answers would remain confidential, and we asked them about their law-breaking behavior. Among other questions the individuals were asked to self-report the number of times per month they exceeded the speed limit. One of the objectives of the study was to estimate (make an inference about) the average nu.
- Aow
-Aow
f--,
d--Tto o4*prnbAu
SDUJ?_
pya
I
LjrrtripA)c€ IttTS4nOt Mooute
U : YA€aru
! ZG.)=
t,
= rneat-!
Z GltFu)
l
Exa^lrle-4,' N
_
# 44o
bon a*^
wwan=4r.L{'mes
<tabk
S= s.zhnes = 4-96
,,.7,
.XSoo
ll =.Q\So
W=rne&,\s,
Ywe Knot{lWeacL,siob --:4.96s
-
42 ,t
t 4o 5t/. (. arr"[hb,r/olrr.r*WItttt*v
'./\
, sorc/z=.025o=2e/
s5/.C7=
42,4tqlte&r/f,*-ot) to/
35/.eJ=.12.+ g;GZlfi=ee) '
Il :5e/{
gsfT=42,+t4sGGz/fm)
w.Q=,1A4!46(.t+)
gS/U=42+L-53 : 44-tT g5/ cT =
-l
-l-^-
gtJ.CJ=41,4+ .53 = 42.?3 44.{T p42.?3
lt
&.a,Wux: =.4 5oo
67)* , ?w/q
,45oo=z+il)k=Yu(-q*/ r'--=
,O5oit
'f O5oo
@ia/.
eo/8=42.+t4.432/ftu)
1o/.cI=/1.4! tt.o{ztlfrd 3o/.cT=42tt-.qq=
ry
tjc7./2.+t4.658'z/tr(--J31 + -1't=ry
%/,c]=42,q
90/q= 42.1tl,os[,t+1 '/
9r1,cl,424t .rirt
9o/ cf = l,l,g6<+lx,8q
�
eT-flowlb .n1rea/<06!e,rvr-7wz
_
N=flo,(
rneei=/1.4hnes
lt
.i = 3.2 nrn?s
cT_
=42,1J 4as(st/m)
[G)
c n4t L.65(s.21 -{"6ss aq
-421t l-As(z,a/zo) I,6s
= (2 qt 4".65 CIa)
/2'58
t.48 fuo/.c-T=/2-22'+
= /Q.1
:42.4--'rg-'12'2U='12,4+-/[email protected]
=,u<se
Saft&(4OO
t{=47
-Cf
:Wean! +G.")
= tgrFr) .hd00
ftp.a,rLL
oSoo
,/,2,,1
T+o"Ib=Ly
sv/,q=4e.\t1.++6Ss
"/F)
= t1.1! 4.?t^(s./fnt)
= 19..4/ ,t,7//6/3.2/, 9o/ Cf-44,003+>/3.+97
=t27r t.++e70.8)
= ,14,4!4"g97
12.+-/.397=4(.003
4A.4+ /.39/-fi. ?*
�
Confidence Interval Module
One of the key concepts of statistics enabling statisticians to make incredibly accurate predictions is called the Central Limit Theorem. The Central Limit Theorem is defined in this way:
· For samples of a sufficiently large size, the real distribution of means is almost always approximately normal.
· The distribution of means gets closer and closer to normal as the sample size gets larger and larger, regardless of what the original variable looks like (positively or negatively skewed).
· In other words, the original variable does not have to be normally distributed.
· This is because, if we as eccentric researchers, drew an almost infinite number of random samples from a single population (such as the student body of NMSU), the means calculated from the many samples of that population will be normally distributed and the mean calculated from all of those samples would be a very close approximation to the true population mean. It is this very characteristic that makes it possible for us, using sound probability based sampling techniques, to make highly accurate statements about characteristics of a population based upon the statistics calculated on a sample drawn from that population.
· Furthermore, we can calculate a statistic known as the standard error of the mean (abbreviated s.e.) that describes the variability of the distribution of all possible sample means in the sa ...
Findings, Conclusions, & Recommendations
Report Writing
Findings
Conclusions
Recommendations
Findings
Conclusions
Recommendations
Findings
Data
Conclusions
What the data means
Recommendations
What should we do?
Types of Reports
Proposal
Feasibility
Analysis
Annual/Quarterly
Sales/Revenue
Investment
Marketing
Research
Consumer
Research
Types of Reports
Proposal
Feasibility
Analysis
Annual/Quarterly
Sales/Revenue
Investment
Marketing
Research
Consumer
Research
Report Sections
1. Title page
2. Table of contents
3. Executive summary
4. Body sections
a. Purpose
b. Scope
c. Factors
d. Conclusions
5. References (endnotes)
Report Sections
1. Title page
2. Table of contents
3. Executive summary
4. Body sections
a. Purpose
b. Scope
c. Factors
d. Conclusions
5. References (endnotes)
New Page
New Page
New Page
New Page
New Page
Title Page
1. Title
2. Author
3. Date (use due date)
4. Audience*
5. No page number
Findings
Conclusions
Recommendations
65% of employees use Facebook
during company time.
Employees are wasting time at
work.
We should establish a social
media policy.
Findings
Conclusions
Recommendations
SHA applications are down 15%.
Exploring Report Myths
Myth Truth
Reports are entirely different
from memos and letters.
Reports may be formatted as
memos or letters.
Exploring Report Myths
Myth Truth
Reports are strictly “objective”
presentations of factual data.
Report writers use their best
judgement to select data to
provide in reports.
Exploring Report Myths
Myth Truth
Reports are mere collections
of data: they should not
incorporate the writer’s
opinion.
Reports should be adapted to
the needs of the readers.
-If readers merely need numerical or
factual data, then mere numerical or
factual data should be sufficient.
Exploring Report Myths
Myth Truth
Reports are mere collections
of data: they should not
incorporate the writer’s
opinion.
Reports should be adapted to
the needs of the readers.
-If readers rely on the report writer to
interpret the data, then the report
should incorporate the writer’s best
attempt to draw conclusions and, if
appropriate, recommendations.
Exploring Report Myths
Myth Truth
A report should be structured
as a sequence of steps in
which the writer engaged in
the “discovery process” to
collect the data.
A report should be structured
according to the needs of the
readers: to learn conclusions
or to act on recommendations.
Google Report
Hilton Annual Report
Hilton Annual Report
Aramark
Report Examples
https://storage.googleapis.com/gfw-touched-accounts-pdfs/google-cloud-security-and-compliance-whitepaper.pdf
http://ir.hilton.com/~/media/Files/H/Hilton-Worldwide-IR-V3/annual-report/Hilton_2013_AR.pdf
http://ir.hilton.com/~/media/Files/H/Hilton-Worldwide-IR-V3/annual-report/1948-Annual-Report.pdf
http://www.elon.edu/docs/e-web/bft/sustainability/ARAMARK%20Trayless%20Dining%20July ...
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:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
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.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
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.
3. First of all we have to know that: Mean = N * P Standard Deviation = N * P * (1 – P) N means the number of trials, P means the probability of success of the trial, and (1 – P) means the probability of failure of the trial. So in our question, N = 200 because there are 200 people but here we represent them as trials. We have a probability of 25% so that would be our P. So now that we have our variables: N = 200 P = .25
4. Here’s the mean: Mean = N * P 200 * .25 = = 50 So our mean is 50. Here’s the Standard Deviation: S.D = N * P * (1 – P) (200 * .25) * (1 - .25) = = 5.3
5. Now we can construct a 95% confidence interval. How do we find it? First of all we have to know that the 95% confidence interval is the probability that a data value falls within 1.96 standard deviations of the mean. This is how it looks like: To find it we use : M+1.96(S.D)
6. So we have the mean of 50 and the standard deviation of 5.3. If we plug that in to our data we get: M+1.96(S.D) 50 + 1.96 (5.3) =60 M-1.96(S.D) 50 – 1.96 (5.3) =40 So our 95% confidence interval is between 20% and 60%. Now we need to find the percentage error =40/200 *100% =20% =60/200*100% =60
7. To find the percent margin of error we need to know: S.D/N * 100% So we quickly look back for our answers from before and we get: 5.3/200 * 100% =.0265(100) = 2.65% is our percent margin of error
![[object Object],[object Object],[object Object],200 students apply for university of manitoba. Most of them have been to the U of M but 25% of them had not been there at all.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)