Multivariate data analysis regression, cluster and factor analysis on spssAditya Banerjee
Using multiple techniques to analyse data on SPSS. A basic software that can easily help run the numbers. Multivariate Data Analysis runs regressions models, factor analyses, and clustering models apart from many more
At the end of this lecture, the student should be able to:
1. understand structure of research study appropriate for independent-measures t hypothesis test
2. test between two populations or two treatments using independent measures t statistics
3. understand how to evaluate the assumptions underlying this test
At the end of this lecture, the students should be able to
1.Understand structure of research study appropriate for ANOVA test
2.Understand how to evaluate the assumptions underlying this test
3. interpret SPSS outputs and report the results
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
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
This is the 4th of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture shows how we can use mathematical analysis to classify players into stereotypes and leverage this classification into generating more successful decisions.
(Some content appears to be missing from the end of this one - I'll fix this as soon as I can)
Multivariate data analysis regression, cluster and factor analysis on spssAditya Banerjee
Using multiple techniques to analyse data on SPSS. A basic software that can easily help run the numbers. Multivariate Data Analysis runs regressions models, factor analyses, and clustering models apart from many more
At the end of this lecture, the student should be able to:
1. understand structure of research study appropriate for independent-measures t hypothesis test
2. test between two populations or two treatments using independent measures t statistics
3. understand how to evaluate the assumptions underlying this test
At the end of this lecture, the students should be able to
1.Understand structure of research study appropriate for ANOVA test
2.Understand how to evaluate the assumptions underlying this test
3. interpret SPSS outputs and report the results
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
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
This is the 4th of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture shows how we can use mathematical analysis to classify players into stereotypes and leverage this classification into generating more successful decisions.
(Some content appears to be missing from the end of this one - I'll fix this as soon as I can)
محاضرة ألقيت بتنظيم من مجموعة برمج @parmg_sa
https://www.meetup.com/parmg_sa/events/238339639/
في الرياض، مقر حاضنة بادر. بتاريخ 20 جمادى الآخر 1438هـ، الموافق 18 مارس 2017
Paper Study: Melding the data decision pipelineChenYiHuang5
Melding the data decision pipeline: Decision-Focused Learning for Combinatorial Optimization from AAAI2019.
Derive the math equation from myself and match the same result as two mentioned CMU papers [Donti et. al. 2017, Amos et. al. 2017] while applying the same derivation procedure.
본 논문에서는 분배형 강화학습(Distributional Reinforcement Learning)에서 벨만 다이내믹스를 통해 확률 분포를 학습하는 문제를 고려합니다. 이전 연구들은 각 반환 분포의 유한 개의 통계량을 신경망을 통해 학습하는 방법을 사용해왔으나, 이 방법은 반환 분포의 함수적 형태에 제한을 받아 제한적인 표현력을 가지며, 미리 정의된 통계량을 유지하는 것이 어려웠습니다. 본 논문에서는 이러한 제한을 없애기 위해 최대 평균 거리(Maximum Mean Discrepancy, MMD)라는 가설 검정 기술을 활용해 반환 분포의 결정론적인(의사 난수를 사용한) 표본들을 학습하는 방법을 제안합니다. 이를 통해 반환 분포와 벨만 타겟 간의 모든 모멘트(순간값)를 암묵적으로 일치시킴으로써 분배형 벨만 연산자의 수렴성을 보장하며, 분포 근사에 대한 유한 샘플 분석을 제시합니다. 실험 결과, 본 논문에서 제안한 방법은 분배형 강화학습의 기본 모델보다 우수한 성능을 보이며, Atari 게임에서 분산형 에이전트를 사용하지 않는 경우에도 최고 성적을 기록합니다.
Lecture 4 - Linear Regression, a lecture in subject module Statistical & Mach...Maninda Edirisooriya
Simplest Machine Learning algorithm or one of the most fundamental Statistical Learning technique is Linear Regression. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
A primer in Data Analysis. To substantiate the concepts, I presented Python code in the form of an ipython notebook (not included - get in touch for these, email and twitter are on the last slide).
The talk starts by describing general data analysis (and skills required). I then speak about computing descriptive statistics and explain the details of two types of predictive models (simple linear regression and naive Bayes classifiers). We build examples using both predictive models using python (Pandas and Matplotlib).
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
8. Factor Analysis – Explanatory Factor Analysis
Find uncorrelated set of factors 𝚵 :
• Similar to PCA: uncorrelated set of factors/components
•
• 𝑿 = 𝚵𝚲 𝑻 + 𝚫, for example:
𝚫 𝑻 𝚫 = diagonal matrix
• Such that the unexplained part Δ is also uncorrelated:
𝚵 𝑻 𝚵 = identity matrix
•
•
Factual
decision
making 8
9. Factor Analysis – Examples
• Decompose correlation matrix 𝐑 into 𝐑 = 𝐃 𝐓 𝐃
Note: Correlation matrix as input is also possible
𝐑 𝑿 = 𝒁 𝒔 𝑫𝑼 𝑻
• In case of PCA:
𝑿 𝑻 𝑿 = 𝑼𝑼𝒁 𝒔𝑻 𝒁 𝒔 𝑫𝑼 𝑻 = 𝑼𝑫 𝟐 𝑼 𝑻
•
•
• Equals eigen decomposition
• Only the component loadings can be calculated, not the
compents itself
Factual
decision
making 9
10. Multidimensional Scaling – idea
• Given distance matrix 𝚫 (n by n matrix)
With coordinates 𝑿 (n by k matrix)
• Map the objects into k-dimensional space
•
Approximating given distance matrix:
2 1/2
𝛿 𝑖𝑖 ≈ 𝑑 𝑖𝑖 = ∑ 𝑥 𝑖𝑖 − 𝑥 𝑗𝑗
•
𝑘
• 𝑎=1
Factual
decision
making 10
11. MDS – Principle Coordinates Analysis
Create full coordinates 𝑿 (n by n-1 matrix) which result in
• Similar to Principal Component Analyis
•
distance matrix
• Perform principal component analysis to get the most of the
variances
• Main differences:
• MDS focuses on the differences/similarities between objects
• FA focuses on the underlying factors/components
Factual
decision
making 11
12. MDS – Stress Minimization
• Find representative coordinates 𝑿 that has approximately distance
Similar to Principle Coordinates Analysis:
matrix equal to 𝚫
But by minimizing the stress value:
𝐦𝐦𝐦 𝝈 𝑿 = � 𝑑 𝑖𝑖 𝑋 − 𝜹 𝑖𝑖
2
𝑖<𝑗≤𝑛
Factual
decision
making 12
13. Cluster Analysis – idea
• Grouping similar objects in clusters
• Two kinds of clustering methods:
• Partitioning methods (k-means)
• Hierarchical methods (dendrogram)
Factual
decision
making 13
