A hypothesis is a testable statement about the relationship between two or more variables and errors reveal about the rejection and acceptance of the statement.
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Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
How to choose the right statistics techniques in different situation. This short presentation provide a compact summary on various method of statistics either descriptive and inferential.
for further inquiry please reach me at bodhiyawijaya@gmail.com
A hypothesis is a testable statement about the relationship between two or more variables and errors reveal about the rejection and acceptance of the statement.
Please like, comment and share
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
How to choose the right statistics techniques in different situation. This short presentation provide a compact summary on various method of statistics either descriptive and inferential.
for further inquiry please reach me at bodhiyawijaya@gmail.com
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
Quantitative data analysis - John RichardsonOUmethods
Your project report should include: a viable research question; a critical literature review; a research proposal; and a work plan for the project. The proposed methods should include methods of data collection and methods of data analysis. Whether you are carrying out qualitative of quantitative research, you should know broadly how you are going to analyse your data before you collect them. And the work plan for your project should include a realistic estimate of the time it will take you to do the analysis. The aim of this presentation is to get you to think creatively about the kinds of analysis that might address your research problem.
Discriminant analysis is a technique that is used by the researcher to analyze the research data when the criterion or the dependent variable is categorical and the predictor or the independent variable is the interval in nature. The term categorical variable means that the predictor variable is divided into a number of categories.
DA is typically used when the groups are already defined prior to the study.
The end result of DA is a model that can be used for the prediction of group memberships. This model allows us to understand the relationship between the set of selected variables and the observations. Furthermore, this model will enable one to assess the contributions of different variables.
For more course tutorials visit
www.tutorialrank.com
1
To make tests of hypotheses about more than two population means, we use the:
t distribution
normal distribution
chi-square distribution
analysis of variance distribution
This presentation discusses in detail about the procedure involved in two-factor MANOVA. Both the analysis i.e. multivariate as well as univariate has been shown in this design by solving an illustration using SPSS software.
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.
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
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.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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
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.
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.
2. Types I errors, Type II
Errors &statistical Power
Type I error
: the probability of rejecting
the null hypothesis when it
is actually true.
Type II error
the probability of failing to
reject the null hypothesis
given that the alternative
hypothesis is actually true.
3. Statistical power
(1 - ):
the probability
of correctly
rejecting the null
hypothesis.
alpha
Sample size
Effect
size
4.
5. Testing Hypotheses on a Single Mean
One sample t-test: statistical
technique that is used to test the
hypothesis that the mean of the
population from which a sample is
drawn is equal to a comparison
standard.
6. Testing hypothesis about two
related means
Paired sample t-test to examine the differences
in the same group before and after treatment.
The Wilcoxon signed-rank test: a nonparametric test for examining significant
differences between two related samples or
repeated measurements on a single sample.
Used as an alternative for a paired samples ttest when the population cannot be assumed to
be normally distributed.
8. Testing hypothesis about two related
means
McNemar's test: non-parametric method used on
nominal data. It assesses the significance of the
difference between two dependent samples when
the variable of interest is dichotomous. It is used
primarily in before-after studies to test for an
experimental effect.
10. Testing hypothesis about two unrelated
means
• Independent samples t-test: is done to see
if there are any significant differences in
the means for two groups in the variable
of interest.
11. Testing hypothesis about several
means
• Analysis Of Variance (ANOVA) helps to examine
the significant mean differences among more than
two groups on an interval or ratio-scaled
dependent variable.
12. Regression Analysis
• Simple regression analysis is used in a
situation where one metric
independent variable is hypothesized
to affect one metric dependent
variable.
15. Standardized regression coefficients
Standardized regression coefficients or beta
coefficients are the estimates resulting from a
multiple regression analysis performed on
variable that have been standardized. This is
usually done to allow the researcher to compare
the relative effects of independent variable on
the dependent variable, when independent
variable are measured in different unit of
measurement.
16. Regression with dummy
variable
• A dummy variable (also known as an
indicator variable, design variable,
categorical variable, binary variable, or
qualitative variable)
• Dummy variable allow to use nominal or
ordinal variable as independent variable
to explain, understand, or predict the
dependent variable.
17. MULTICOLLINEARITY
• Encountered statistical phenomenon in which two or more independent
variables in a multiple regression model are highly correlated.
• It makes the estimation of the regression coefficients impossible and
sometimes unreliable.
• To detect multicollinearity, we must check the correlation matrix for the
independent variables.
• The high correlations is first sign of sizeable multicollinearity.
TWO MEASURES :
Tolerance value
Variance inflation factor ( VIF )
To measure indicate the degree to which one independent variable and explained
by the other independent variable.
19. • To fit multiple linear regression model in SPSS using the FEV
data do the following:
• Analyze > Regression > Linear and then move forced
expiratory volume into the dependent box and Smoke and age
into independent(s) box. Then Click OK.
• This will give you the model summary table, ANOVA table
and the regression coefficients table in the output window.
20. A demonstration of how to start fitting the multiple
regression model in SPSS
21. A demonstration of how to select the dependent and
independent variable(s) for fitting multiple regression in SPSS.
22. A demonstration of how to select diagnostic statistic for
checking outliers and
multicollinearity issues in SPSS.
23. Multicollinearity is not a serious problem, because the
estimation of the regression coefficients may be unstable.
But when the objective of the study is to reliably estimate the
individual regression coefficients, multicollinearity is a
problem.
The Methods to Reduce
Reduce the set of independent variables to a set that are not
collinear.
Use more sophisticated ways to analyze the data, such as
ridge regression.
Create a new variable that is a composite of the highly
correlated variables.
24. Testing moderating using regression
analysis : interaction effects
It is effect one variable ( X1 ) on Y depends on the value of
another variable ( X2 ).
Moderating variable as a variable that modifies the original
relationship between an independent variable and dependent
variable.
Example :
H1 : The students’ judgement of the university’s library is
affected by the students’ judgement of the computers.
-It’s means the relationship between the judgement of computers
in the library and the judgement of the library is affected by
computer ownership.
H2 : The relationship between the judgement of computers in the
library is moderated by computer ownership.
27. Other multivariate tests and
analysis
• Discriminant analysis
-help to identify IV that discriminate a
normally scaled DV of interest.
28. Other multivariate tests and
analysis
• Logistic regression
-used when the DV is nonmetric
-always used when DV has only 2
groups.
-it allows researcher to predict discrete
outcome.
29. Other multivariate tests and
analysis
• Conjoint analysis
-statistical technique used in many fields.
-used to understand how consumers develop
preferences for product/services
-built on the idea that consumers evaluate
the value of a product or service by
combining the value that is provided by each
attribute.
30. Other multivariate tests and
analysis
• Two-way ANOVA
-used to examine the effect of two non
metric IV on a single metric DV
-enable us to examine main effects &
also interaction effects that exist
between the independent variables.
31. Other multivariate tests and
analysis
• Two-way ANOVA
-example
DV : Satisfy with toy
IV : i) toy colour (pink & blue)
ii) gender (male & female)
Main effect of toy colour. Pink toys significantly more
satisfaction than the blue toys.
Main effect of gender. The female are more satisfy with the
toy than the male
32. Other multivariate tests and
analysis
• Multivariate Analysis of Variance
(MANOVA)
-is a multivariate extension of analysis of
variance.
-the IV measured on a nominal scale & the
DV on interval/ratio scale
i) The null hyphothesis:
Hₒ
:µ1=µ2=µ3... µn
ii) The alternate hyphothesis:
HA:µ1≠µ2≠µ3≠... µn
33. Other multivariate tests and
analysis
• Canonical correlation
-examine the relationship between two or
more DV & several IV
34. Data warehousing
•
Most companies are now aware of the benefits of
creating a data warehouse that serves as the central
repository of all data collected from disparate
sources including those pertaining to the company's
finance, manufacturing, sales, and the like.
35. Data Mining
• Complementary to the functions of data
warehousing, many companies resort to data
mining as a strategic tool for reaching new levels of
business intelligence.
• Using algorithms to analyze data in a meaningful
way, data mining more effectively leverages the
data warehouse by identifying hidden relations and
patterns in the data stored in it.
36. Operations Research
• Operations research (OR) or management science
(MS) is another sophisticated tool used to simplify
and thus clarify certain types of complex problem
that lend themselves to quantification.