Forecasting Stock Market using Multiple Linear Regressionijtsrd
Regression is one of the most powerful statistical methods used in business and marketing researches. This paper shows the important instance of regression methodology called Multiple Linear Regression MLR and proposes a framework of the forecasting of the Stock Index Price, based on the Interest Rate and the Unemployment Rate. This paper was applied the aid of the Statistical Package for Social Sciences SPSS version 23 and PYTHON version 3.7. Yee Mon Khaing | Myint Myint Yee | Ei Ei Aung "Forecasting Stock Market using Multiple Linear Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27819.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/27819/forecasting-stock-market-using-multiple-linear-regression/yee-mon-khaing
A Fibonacci analysis is a popular tool among technical traders. It is based on the Fibonacci sequence numbers identified by Leonardo Fibonacci in the 13th century. Here are the Fibonacci sequence numbers:
0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89,144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, …………………
As the Fibonacci number become large, the constant relationship is established between neighbouring numbers. For example, every time, when we divide the former number by latter: Fn-1/Fn, we will get nearly 0.618 ratio. Likewise, when we divide the latter number by former: Fn/Fn-1, we will get nearly 1.618. These two Fibonacci ratio 0.618 and 1.618 are considered as the Golden Ratio. We can use these Golden ratios to start our Fibonacci analysis. However, many technical traders use additional Fibonacci ratios derived from the Golden ratio. Since the calculation of each Fibonacci ratio is well known, I have listed all the available Fibonacci ratio calculation in Table 1-1.
For the Price Action and Pattern Analysis, it is important to have good visualization tools. Since we want to find important patterns for our trading, we will need a good size monitor and good visualization software. Of course, you should invest on them as much as you can afford. No single visualization techniques are perfect. They always possess some advantages as well as some disadvantages. Firstly, line chart is the most basic visualization technique for traders. Line is simply drawn by connecting each session’s closing price. For example, 1-hour line chart is simply drawn by connecting the closing price of 1-hour candle. As line chart are produced by connecting two points at the fixed time interval, they can provide a great insight about some regularities in the price series. For this reason, not only traders use the line chart but also many mathematicians use them to visualize the price series data. Line chart is useful when we want to exam some cyclic behaviour like seasonality or any cyclic patterns made up from sine or cosine function. Line chart is also useful when you want to compare multiple price series in one chart. On the other hands, the disadvantage of the line chart is that it does not provide the trading range of each session. In addition, due to the continuously drawn line, it is difficult to see any gap between sessions. In addition, line chart miss some important attributes like highest and lowest prices of each session.
Forecasting Stock Market using Multiple Linear Regressionijtsrd
Regression is one of the most powerful statistical methods used in business and marketing researches. This paper shows the important instance of regression methodology called Multiple Linear Regression MLR and proposes a framework of the forecasting of the Stock Index Price, based on the Interest Rate and the Unemployment Rate. This paper was applied the aid of the Statistical Package for Social Sciences SPSS version 23 and PYTHON version 3.7. Yee Mon Khaing | Myint Myint Yee | Ei Ei Aung "Forecasting Stock Market using Multiple Linear Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27819.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/27819/forecasting-stock-market-using-multiple-linear-regression/yee-mon-khaing
A Fibonacci analysis is a popular tool among technical traders. It is based on the Fibonacci sequence numbers identified by Leonardo Fibonacci in the 13th century. Here are the Fibonacci sequence numbers:
0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89,144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, …………………
As the Fibonacci number become large, the constant relationship is established between neighbouring numbers. For example, every time, when we divide the former number by latter: Fn-1/Fn, we will get nearly 0.618 ratio. Likewise, when we divide the latter number by former: Fn/Fn-1, we will get nearly 1.618. These two Fibonacci ratio 0.618 and 1.618 are considered as the Golden Ratio. We can use these Golden ratios to start our Fibonacci analysis. However, many technical traders use additional Fibonacci ratios derived from the Golden ratio. Since the calculation of each Fibonacci ratio is well known, I have listed all the available Fibonacci ratio calculation in Table 1-1.
For the Price Action and Pattern Analysis, it is important to have good visualization tools. Since we want to find important patterns for our trading, we will need a good size monitor and good visualization software. Of course, you should invest on them as much as you can afford. No single visualization techniques are perfect. They always possess some advantages as well as some disadvantages. Firstly, line chart is the most basic visualization technique for traders. Line is simply drawn by connecting each session’s closing price. For example, 1-hour line chart is simply drawn by connecting the closing price of 1-hour candle. As line chart are produced by connecting two points at the fixed time interval, they can provide a great insight about some regularities in the price series. For this reason, not only traders use the line chart but also many mathematicians use them to visualize the price series data. Line chart is useful when we want to exam some cyclic behaviour like seasonality or any cyclic patterns made up from sine or cosine function. Line chart is also useful when you want to compare multiple price series in one chart. On the other hands, the disadvantage of the line chart is that it does not provide the trading range of each session. In addition, due to the continuously drawn line, it is difficult to see any gap between sessions. In addition, line chart miss some important attributes like highest and lowest prices of each session.
Econometrics notes (Introduction, Simple Linear regression, Multiple linear r...Muhammad Ali
Econometrics notes for BS economics students
Muhammad Ali
Assistant Professor of Statistics
Higher Education Department, KPK, Pakistan.
Email:Mohammadale1979@gmail.com
Cell#+923459990370
Skyp: mohammadali_1979
Learn to anchor cells, move around Excel without a mouse, functions to summarize data, PivotTables, filters, sorting, charts, and macros in this course to take your Excel skills to the next level. Include information on functions: countif, sumif, vlookup, index, match, left, right, mid, len, trim, find, now, date, int
Intangible Assets under IAS 38 worry people more than it needs to. Here's a straightforward presentation which covers the essentials you should know when studying IAS 38 for work or exams.
Econometrics notes (Introduction, Simple Linear regression, Multiple linear r...Muhammad Ali
Econometrics notes for BS economics students
Muhammad Ali
Assistant Professor of Statistics
Higher Education Department, KPK, Pakistan.
Email:Mohammadale1979@gmail.com
Cell#+923459990370
Skyp: mohammadali_1979
Learn to anchor cells, move around Excel without a mouse, functions to summarize data, PivotTables, filters, sorting, charts, and macros in this course to take your Excel skills to the next level. Include information on functions: countif, sumif, vlookup, index, match, left, right, mid, len, trim, find, now, date, int
Intangible Assets under IAS 38 worry people more than it needs to. Here's a straightforward presentation which covers the essentials you should know when studying IAS 38 for work or exams.
know the Importance and Need of Bank Reconciliation Statement.
Understand the Causes for Disagreement between Cash Book and Pass Book Balances.
Prepare Bank Reconciliation Statement.
Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data. Descriptive Statistical algorithms are sophisticated techniques that, within the confines of a self-serve analytical tool, can be simplified in a uniform, interactive environment to produce results that clearly illustrate answers and optimize decisions.
4
DDBA 8307 Week 7 Assignment Template
John Doe
DDBA 8307-6
Dr. Jane Doe
1
Two-Way Contingency Table Analysis
Type text here. You will describe and defend using the two-way contingency table analysis. Use at least two outside resources—that is, resources not provided in the course resources, readings, etc. These citations will be presented in the References section. This exercise will give you practice for addressing Rubric Item 2.13b, which states, “Describes and defends, in detail, the statistical analyses that the student will conduct….” This section should be no more than two paragraphs.
Research Question
Type appropriate research question here?
Hypotheses
H0: Type appropriate null hypothesis here.
H1: Type appropriate alternative hypothesis here.
Results
Type introduction here.
Descriptive Statistics
Present the descriptive statistics here—use appropriate table and figures.
Inferential Results
Type the inferential results here.
2
References
Type references here in proper APA format.
Appendix – Two-Way Contingency Table Analysis
SPSS Output
BUS 308 Week 2 Lecture 2
Statistical Testing for Differences – Part 1
After reading this lecture, the student should know:
1. How statistical distributions are used in hypothesis testing.
2. How to interpret the F test (both options) produced by Excel
3. How to interpret the T-test produced by Excel
Overview
Lecture 1 introduced the logic of statistical testing using the hypothesis testing procedure.
It also mentioned that we will be looking at two different tests this week. The t-test is used to
determine if means differ, from either a standard or claim or from another group. The F-test is
used to examine variance differences between groups.
This lecture starts by looking at statistical distributions – they underline the entire
statistical testing approach. They are kind of like the detective’s base belief that crimes are
committed for only a couple of reasons – money, vengeance, or love. The statistical distribution
that underlies each test assumes that statistical measures (such as the F value when comparing
variances and the t value when looking at means) follow a particular pattern, and this can be used
to make decisions.
While the underlying distributions differ for the different tests we will be looking at
throughout the course, they all have some basic similarities that allow us to examine the t
distribution and extrapolate from it to interpreting results based on other distributions.
Distributions. The basic logic for all statistical tests: If the null hypothesis claim is
correct, then the distribution of the statistical outcome will be distributed around a central value,
and larger and smaller values will be increasingly rare. At some point (and we define this as our
alpha value), we can say that the likelihood of getting a difference this large is extremely
unlikely and we will say that our results do.
A few ideas for how Excel commands may be useful. All scientist can find the knowledge of Excel can really assist in their planning of an experiment, and checking the data.
This is a very simple introduction, so you may realize why you can use Excel in your journey to becoming a quantitatively savvy scientist.
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docxcurwenmichaela
BUS308 – Week 1 Lecture 2
Describing Data
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Basic descriptive statistics for data location
2. Basic descriptive statistics for data consistency
3. Basic descriptive statistics for data position
4. Basic approaches for describing likelihood
5. Difference between descriptive and inferential statistics
What this lecture covers
This lecture focuses on describing data and how these descriptions can be used in an
analysis. It also introduces and defines some specific descriptive statistical tools and results.
Even if we never become a data detective or do statistical tests, we will be exposed and
bombarded with statistics and statistical outcomes. We need to understand what they are telling
us and how they help uncover what the data means on the “crime,” AKA research question/issue.
How we obtain these results will be covered in lecture 1-3.
Detecting
In our favorite detective shows, starting out always seems difficult. They have a crime,
but no real clues or suspects, no idea of what happened, no “theory of the crime,” etc. Much as
we are at this point with our question on equal pay for equal work.
The process followed is remarkably similar across the different shows. First, a case or
situation presents itself. The heroes start by understanding the background of the situation and
those involved. They move on to collecting clues and following hints, some of which do not pan
out to be helpful. They then start to build relationships between and among clues and facts,
tossing out ideas that seemed good but lead to dead-ends or non-helpful insights (false leads,
etc.). Finally, a conclusion is reached and the initial question of “who done it” is solved.
Data analysis, and specifically statistical analysis, is done quite the same way as we will
see.
Descriptive Statistics
Week 1 Clues
We are interested in whether or not males and females are paid the same for doing equal
work. So, how do we go about answering this question? The “victim” in this question could be
considered the difference in pay between males and females, specifically when they are doing
equal work. An initial examination (Doc, was it murder or an accident?) involves obtaining
basic information to see if we even have cause to worry.
The first action in any analysis involves collecting the data. This generally involves
conducting a random sample from the population of employees so that we have a manageable
data set to operate from. In this case, our sample, presented in Lecture 1, gave us 25 males and
25 females spread throughout the company. A quick look at the sample by HR provided us with
assurance that the group looked representative of the company workforce we are concerned with
as a whole. Now we can confidently collect clues to see if we should be concerned or not.
As with any detective, the first issue is to understand the.
Data Science - Part III - EDA & Model SelectionDerek Kane
This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
These is info only ill be attaching the questions work CJ 301 – .docxmeagantobias
These is info only ill be attaching the questions work CJ 301 –
Measures of Dispersion/Variability
Think back to the description of
measures of central tendency
that describes these statistics as measures of how the data in a distribution are clustered, around what summary measure are most of the data points clustered.
But when comes to descriptive statistics and describing the characteristics of a distribution, averages are only half story. The other half is measures of variability.
In the most simple of terms, variability reflects how scores differ from one another. For example, the following set of scores shows some variability:
7, 6, 3, 3, 1
The following set of scores has the same mean (4) and has less variability than the previous set:
3, 4, 4, 5, 4
The next set has no variability at all – the scores do not differ from one another – but it also has the same mean as the other two sets we just showed you.
4, 4, 4, 4, 4
Variability (also called spread or dispersion) can be thought of as a measure of how different scores are from one another. It is even more accurate (and maybe even easier) to think of variability as how different scores are from one particular score. And what “score” do you think that might be? Well, instead of comparing each score to every other score in a distribution, the one score that could be used as a comparison is – that is right- the mean. So, variability becomes a measure of how much each score in a group of scores differs from the mean.
Remember what you already know about computing averages – that an average (whether it is the mean, the median or the mode) is a representative score in a set of scores. Now, add your new knowledge about variability- that it reflects how different scores are from one another. Each is important descriptive statistic. Together, these two (average and variability) can be used to describe the characteristics of a distribution and show how distribution differ from one another.
Measures of dispersion/variability
describe how the data in a distribution a
re scattered or dispersed around, or from, the central point represented by the measure of central tendency.
We will discuss
four different measures of dispersion
, the
range
, the
mean deviation
, the
variance
, and the
standard deviation
.
RANGE
The
range
is a very simple measure of dispersion to calculate and interpret.
The
range
is simply the difference between the highest score and the lowest score in a distribution.
Consider the following distribution that measures the “Age” of a random sample of eight police officers in a small rural jurisdiction.
Officer
X = Age_
41
20
35
25
23
30
21
32
First, let’s calculate the mean as our measure of central tendency by adding the individual ages of each officer and dividing by the number of officers.
The calculation is 227/8 = 28.375 years.
In general, the formula for the range is:
R=h-l
Where:
r is the range
h.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
CJ 301 – Measures of DispersionVariability Think back to the .docxmonicafrancis71118
CJ 301 – Measures of Dispersion/Variability
Think back to the description of measures of central tendency that describes these statistics as measures of how the data in a distribution are clustered, around what summary measure are most of the data points clustered.
But when comes to descriptive statistics and describing the characteristics of a distribution, averages are only half story. The other half is measures of variability.
In the most simple of terms, variability reflects how scores differ from one another. For example, the following set of scores shows some variability:
7, 6, 3, 3, 1
The following set of scores has the same mean (4) and has less variability than the previous set:
3, 4, 4, 5, 4
The next set has no variability at all – the scores do not differ from one another – but it also has the same mean as the other two sets we just showed you.
4, 4, 4, 4, 4
Variability (also called spread or dispersion) can be thought of as a measure of how different scores are from one another. It is even more accurate (and maybe even easier) to think of variability as how different scores are from one particular score. And what “score” do you think that might be? Well, instead of comparing each score to every other score in a distribution, the one score that could be used as a comparison is – that is right- the mean. So, variability becomes a measure of how much each score in a group of scores differs from the mean.
Remember what you already know about computing averages – that an average (whether it is the mean, the median or the mode) is a representative score in a set of scores. Now, add your new knowledge about variability- that it reflects how different scores are from one another. Each is important descriptive statistic. Together, these two (average and variability) can be used to describe the characteristics of a distribution and show how distribution differ from one another.
Measures of dispersion/variability describe how the data in a distribution are scattered or dispersed around, or from, the central point represented by the measure of central tendency.
We will discuss four different measures of dispersion, the range, the mean deviation, the variance, and the standard deviation.
RANGE
The range is a very simple measure of dispersion to calculate and interpret. The range is simply the difference between the highest score and the lowest score in a distribution.
Consider the following distribution that measures the “Age” of a random sample of eight police officers in a small rural jurisdiction.
Officer X = Age_
1 41
2 20
3 35
4 25
5 23
6 30
7 21
8 32
First, let’s calculate the mean as our measure of central tendency by adding the individual ages of each officer and dividing by the number of officers. The calculation is 227/8 = 28.375 years.
In general, the formula for the range is:
R=h-l
Where:
· r is the range
· h is the highest score in the .
Sheet1Number of Visits Per DayNumber of Status Changes Per WeekAge.docxlesleyryder69361
Sheet1Number of Visits Per DayNumber of Status Changes Per WeekAge5138VariableMeanMedianModeRangeVarianceStandard Deviation15527Visits Per Day5121Status Changes Per Week25319Age50525631881024Data from:http://www.statcrunch.com/5.0/index.php?dataid=48537311028331910120114210481032112715118207190040003015526417311219414110320302051325324511851205621301201512053411551933190049543111182120422000520027101013531841219511820194121211811218124202019101018501002230224532050142337205120113331841514102541547319402010019004215320412800401166103145220122910524005032173119206310018212125719152202020002533310025512021332024002000211021002750201031911195718213810002221211532600361010245018511942202149311884185126013220102131371551500561502000285656185136001421471053022211012061352511800422083200212223113715520514520719106211581120332128241328233810540522111122113522632370035108540013761181120104194120004800.52351012128458221911390033321973025114820532022211272514471043405030191111004535281424602019201529105192002010140052342620718212630321120231800401910520151721942221071910719305041200058362143102251237120215119332632193832721356301810519421832351128531931193810221822310127425820234339204183218321900532238105411007519424318102362020171022040283850100292121286143250232319101025121823198204219442831293218203451432143117021181031815418530202521825104010263120101952243325102201511930220114131171051944240065231920220121443015121531851193024757144246203210213125621920275121104319121725104626191048102581181167441643400556320452051182024474181001841192312562481932339525214210494810255132310561312191155013650100181152101910552224191039210043302500560136814151263020107323262260415152124303193061004758108180039103900550050315910322602048404411354020http://www.statcrunch.com/5.0/index.php?dataid=485373
Sheet2
Sheet3
PSYC 354
Excel Homework 3
(70 pts possible)
The objective of your third Excel assignment is to learn to describe a data set using measures of central tendency and variability. First, be sure you view the presentation that covers computing central tendency and variability in Excel found in the Reading & Study folder in Module/Week 3. This presentation goes through the steps you will need to be familiar with in order to complete this assignment.
In Module/Week 3, the goal is to use Excel formulas to calculate specific measures of central tendency and variability of a given data set, using the steps you learned during the presentation. Open “Data Set 3,” found in the Assignment Instructions folder, under “Excel Homework 3,” then follow the steps below to complete Module/Week 3’s assignment.
1. Research Question: In Module/Week 3, the data comes from an internet survey that assessed the frequency of use of the social networking site Facebook ™. A psychologist interested in time spent visiting a social networking site collected data from 366 respondents concerning: 1.) number of visits to FB per day; 2.) number of times participants changed FB “status” per w.
Similar to Lecture2 Applied Econometrics and Economic Modeling (20)
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
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
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.