Don't get confused with Summary Statistics. Learn in-depth types of summary statistics from measures of central tendency, measures of dispersion and much more.
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Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 3: Describing, Exploring, and Comparing Data
3.3: Measures of Relative Standing and Boxplots
Basic statistics for algorithmic tradingQuantInsti
In this presentation we try to understand the core basics of statistics and its application in algorithmic trading.
We start by defining what statistics is. Collecting data is the root of statistics. We need data to analyse and take quantitative decisions.
While analyzing, there are certain parameters for statistics, this branches statistics into two - descriptive statistics & inferential statistics.
This data that we have collected can be classified into uni-variate and bi-variate. It also tries to explain the fundamental difference.
Going Further we also cover topics like regression line, Coefficient of Determination, Homoscedasticity and Heteroscedasticity.
In this way the presentation look at various aspects of statistics which are used for algorithmic trading.
To learn the advanced applications of statistics for HFT & Quantitative Trading connect with us one our website: www.quantinsti.com.
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
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Statistics is the science of dealing with numbers.
It is used for collection, summarization, presentation and analysis of data.
Statistics provides a way of organizing data to get information on a wider and more formal (objective) basis than relying on personal experience (subjective).
Detailed discussion about the types of statistics form Measures of Central Tendency, Measures of Dispersion, Skewness, Kurtosis, Probability Distributions and much more with their uses cases
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 3: Describing, Exploring, and Comparing Data
3.3: Measures of Relative Standing and Boxplots
Basic statistics for algorithmic tradingQuantInsti
In this presentation we try to understand the core basics of statistics and its application in algorithmic trading.
We start by defining what statistics is. Collecting data is the root of statistics. We need data to analyse and take quantitative decisions.
While analyzing, there are certain parameters for statistics, this branches statistics into two - descriptive statistics & inferential statistics.
This data that we have collected can be classified into uni-variate and bi-variate. It also tries to explain the fundamental difference.
Going Further we also cover topics like regression line, Coefficient of Determination, Homoscedasticity and Heteroscedasticity.
In this way the presentation look at various aspects of statistics which are used for algorithmic trading.
To learn the advanced applications of statistics for HFT & Quantitative Trading connect with us one our website: www.quantinsti.com.
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
http://bit.ly/LETndNET
Statistics is the science of dealing with numbers.
It is used for collection, summarization, presentation and analysis of data.
Statistics provides a way of organizing data to get information on a wider and more formal (objective) basis than relying on personal experience (subjective).
Detailed discussion about the types of statistics form Measures of Central Tendency, Measures of Dispersion, Skewness, Kurtosis, Probability Distributions and much more with their uses cases
Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.
Hierarchical Clustering - Text Mining/NLPRupak Roy
Documented Hierarchical clustering using Hclust for text mining, natural language processing.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Clustering K means and Hierarchical - NLPRupak Roy
Classify to cluster the natural language processing via K means, Hierarchical and more.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Network Analysis using 3D interactive plots along with their steps for implementation.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Widely accepted steps for sentiment analysis.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Detailed Pattern Search using regular expressions using grepl, grep, grepexpr and Replace with sub, gsub and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Detailed documented with the definition of text mining along with challenges, implementing modeling techniques, word cloud and much more.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Bundled with the documentation to the introduction of Apache Hbase to the configuration.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Understand and implement the terminology of why partitioning the table is important and the Hive Query Language (HQL)
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Installing Apache Hive, internal and external table, import-export Rupak Roy
Perform Hive installation with internal and external table import-export and much more
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Well illustrated with definitions of Apache Hive with its architecture workflows plus with the types of data available for Apache Hive
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Automate the complete big data process from import to export data from HDFS to RDBMS like sql with apache sqoop
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Apache Scoop - Import with Append mode and Last Modified mode Rupak Roy
Familiar with scoop advanced functions like import with append and last modified mode.
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Get acquainted with the differences in scoop, the added advantages with hands-on implementation
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Get acquainted with a distributed, reliable tool/service for collecting a large amount of streaming data to centralized storage with their architecture.
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take care!
Enhance analysis with detailed examples of Relational Operators - II includes Foreash, Filter, Join, Co-Group, Union and much more.
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Talk soon!
Passing Parameters using File and Command LineRupak Roy
Explore well versed other functions, flatten operator and other available options to pass parameters
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Talk soon!
Get to know the implementation of apache Pig relational operators like order, limit, distinct, groupby.
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Talk soon!
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.
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• 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
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http://sandymillin.wordpress.com/iateflwebinar2024
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Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
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2. Definition
Summary statistics are used to summarize a
set of observations in order to
communicate as much as information
about the data as possible. It is part of
descriptive statistics and are used to
basically summarize or describe a set of
observations.
Rupak Roy
3. Example
The weight of the population are
45 kg
57kg
72 kg
52 kg
Now what we want here is the summary of
weight of the population , we can say it is the
average weight of the population is 56.5 kg and
now we can describe the population in the
simplest way as possible.
Rupak Roy
4. Types
Summary statistics
Measures of Central
Tendency
1 . Mean
2 . Median
3 . Mode
5 . Geometric Mean
Measures of
Dispersion
1. Standard
Deviation
2. Variance
3. Interquartile
Range
Others
1. Co efficient
2. Skewness
3. Kurtosis
4. Probability
Distributions.
5. Distribution plot
Rupak Roy
5. Definition
Measures of central tendency : is the value that describes
which group of data clusters around a central value. In
simple words , it is a way to describe the center of a data
set. Again what is center of data ? A single number that
summarizes the entire dataset using techniques such as
mean/average or median of the dataset.
Measures of Dispersion: “dispersion (also
called variability, scatter, or spread) is the extent to which
a distribution of data is stretched or squeezed.”
Here in the graph we can see the
distribution of data (assume population)
is more stretched at the right side
ranging from 50 to 80
6. Measures of Central Tendency
1. Mean : is the average of observations. Most effective
when data is not heavily skewed.
2. Median: represents the middle value of the dataset.
Useful for skewed data.
We will talk about skewed data in the upcoming
slides.
3. Mode: means max no of times the data has occurred.
4. Geometric mean: nth root of a product of n numbers.
It is used when we want to get the average rate of the
event and the event rate is determined by multiplication.
For example growth of a bank account per year in a
ABC bank is calculated by geometric mean since the
growth event rate is determined by multiplying the
amount of a bank account by the percentage of growth.
then we use geometric mean.
Rupak Roy
7. Formula for calculating Geometric Mean
GM =
example: Geometric Mean of 23,56,66 ?
3 23 * 56*66
3 85008 = 43.9696761which means 3times of 43.9696761
is 85008
Note:
if one of the observation in the event is zero , Geometric
Mean becomes Zero and also it doesn’t works with
negative numbers like -1 , -4 , -5 and so on.
Rupak Roy
8. Calculation of Mode ; <- Delta
For ungrouped data = Max no of items
Example : 23,45,76,33,54,33,76,33 Therefore Mode = 33
For grouped data = = {(L + Delta 1) / Delta 1+Detal2 } * i
Where Delta 1 = f1 +f0
and Delta 2 = f1- f2
Nowadays, we don’t have to worry about the calculation, as in
any statistical software's like R, excel it will automatically calculate
the intense calculation for large amount of data but
for more in-depth information you can visit this website.
https://www.mathsisfun.com/data/frequency-grouped-mean-median-mode.html
9. Measures of dispersion
Standard Deviation is basically a measure of how near or far the
observations are from the mean.
Variance: the fact or quality of being different , divergent or
inconsistent. A value of zero means that there is no variability , all the
values in the data set are the same.
Interquartile Range: is a measure of variability ,
by dividing a data set into parts that is quartiles .
Say
Q1 is the middle value in the first half of the data set.
Q2 is the median value .
Q3 is the middle value in the second half of the
rank-ordered data set.
There interquartile range = Q3 – Q1
10. Skewness – refers to the lack of symmetry or imbalance in data
distribution.
In a symmetric distribution the data is
normally distributed where mean,
median, mode is at the same point.
However in real life data is never perfectly
distributed, hence we call it skewed data.
If the Left side has longer tail then the mass
distribution of data is concentrated on the right
side which is known as negatively skewed.
11. If the Right side has longer tail then the
mass distribution of data is
concentrated on the left side is
known as positive skewed.
Here is the summary of all the skewness as shown in the figure below.
12. Example (skewed data)
Temp(*c)
10
40
35
33
35
Mean = 153/5 = 30.6, if we apply mean is 30.6
which is incorrect since we can see maximum
number of values are above 35.
So we have to use median For Ungrouped
data ((n+1)/2)th
That will be ((5+1)/2)th = 6/2 = 3
i.e. 3th term ie 35.
For grouped data:
where L, lower class boundary of the group containing the group.
B, Cumulative frequency of the groups
G , Frequency of the median group
W , width/Range of the group
Again, we don’t have to worry about the calculation, as in any statistical software's like R
, excel it will automatically calculate the intense calculation for large amount of data
but for more in-depth information you can visit this website.
https://www.mathsisfun.com/data/frequency-grouped-mean-median-mode.html
13. Kurtosis : is a measure of whether data are peaked or flat relative to
normal distribution
(+) Leptokurtic
(-) PlatyKurtic
(0) Meskurtic
(+) Leptokurtic
This means the distribution is more clustered near the mean and has a
relativity less standard deviation
(-) PlatyKurtic
Where the distribution is less clustered around the mean and a standard
deviation more then Leptokurtic
(0) Meskurtic is typically measured with respect to the normal
distribution. Meskurtic has tails similar to normal distribution i.e neither
high nor low, rather it is consider to be a baseline for the other two’s.
14. Now how to check the data is skewed or not
in Excel:
=skew(select the range of values/numbers)
=skew(10.24,9.48……….-0.42,-0.95)
= - 0.27 means Negatively skewed.
And to check the Kurtosis in Excel
=kurt(select the values/numbers)
=kurt(10.24,9.48……….-0.42,-0.95)
= -1.6 means it is PlatyKurtic
15. Recap
What we have learned ?
Measures of central tendency,
Measures of dispersion,
Measure of risk,
Next we will see how to compute this theory in
practical and analyze any data using our
everyday simple tools like Excel.
Rupak Roy