This document provides an introduction to statistics, including why statistics are studied, applications in business, and key statistical concepts. It discusses descriptive statistics used to organize and summarize data, inferential statistics used to make inferences about populations from samples, and different types of data and variables. It also covers topics like sampling methods, presenting and analyzing qualitative and quantitative data, and scales of measurement. The overall purpose is to introduce foundational statistical concepts.
These presentations are created by Tushar B Kute to teach the subject 'Management Information System' subject of TEIT of University of Pune.
http://www.tusharkute.com
These presentations are created by Tushar B Kute to teach the subject 'Management Information System' subject of TEIT of University of Pune.
http://www.tusharkute.com
Frequency distribution, central tendency, measures of dispersionDhwani Shah
The presentation explains the theory of what is Frequency distribution, central tendency, measures of dispersion. It also has numericals on how to find CT for grouped and ungrouped data.
Republic Act 8293, section 176 states that: No copyright shall subsist in any work of the Government of the Philippines. However, prior approval of the government agency or office wherein the work is created shall be necessary for exploitation of such work for profit. Such agency or office may, among other things, impose as a condition the payment of royalties.
Frequency distribution, central tendency, measures of dispersionDhwani Shah
The presentation explains the theory of what is Frequency distribution, central tendency, measures of dispersion. It also has numericals on how to find CT for grouped and ungrouped data.
Republic Act 8293, section 176 states that: No copyright shall subsist in any work of the Government of the Philippines. However, prior approval of the government agency or office wherein the work is created shall be necessary for exploitation of such work for profit. Such agency or office may, among other things, impose as a condition the payment of royalties.
1. Introduction to statistics in curriculum and Instruction
1 The definition of statistics and other related terms
1.2 Descriptive statistics
3 Inferential statistics
1.4 Function and significance of statistics in education
5 Types and levels of measurement scale
2. Introduction to SPSS Software
3. Frequency Distribution
4. Normal Curve and Standard Score
5. Confidence Interval for the Mean, Proportions, and Variances
6. Hypothesis Testing with One and two Sample
7. Two-way Analysis of Variance
8. Correlation and Simple Linear Regression
9. CHI-SQUARE
Part of a course I run introducing quantitative methods. One of the slideshows on my site www.kevinmorrell.org.uk please reference the site if you use any of it - hope it is useful.
Introduction to Statistics -
Sampling Techniques, Types of Statistics, Descriptive Statistics,
Inferential Statistics,
Variables and Types of Data: Qualitative, Quantitative, Discrete,
Continuous, Organizing and Graphing Data: Qualitative Data, Quantitative Data
The material is consolidated from different sources on the basic concepts of Statistics which could be used for the Visualization an Prediction requirements of Analytics.
I deeply acknowledge the sources which helped me consolidate the material for my students.
Similar to Introduction-To-Statistics-18032022-010747pm (1).ppt (20)
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. Why study statistics?
1. Data are everywhere
2. Statistical techniques are used to make many
decisions that affect our lives
3. No matter what your career, you will make
professional decisions that involve data. An
understanding of statistical methods will help
you make these decisions efectively
3. Applications of statistical concepts in
the business world
Finance – correlation and regression, index
numbers, time series analysis
Marketing – hypothesis testing, chi-square tests,
nonparametric statistics
Personel – hypothesis testing, chi-square tests,
nonparametric tests
Operating management – hypothesis testing,
estimation, analysis of variance, time series
analysis
4. Statistics
The science of collectiong, organizing,
presenting, analyzing, and interpreting data to
assist in making more effective decisions
Statistical analysis – used to manipulate
summarize, and investigate data, so that useful
decision-making information results.
5. Types of statistics
Descriptive statistics – Methods of organizing,
summarizing, and presenting data in an
informative way
Inferential statistics – The methods used to
determine something about a population on the
basis of a sample
Population –The entire set of individuals or objects
of interest or the measurements obtained from all
individuals or objects of interest
Sample – A portion, or part, of the population of
interest
6.
7. Inferential Statistics
Estimation
e.g., Estimate the population
mean weight using the sample
mean weight
Hypothesis testing
e.g., Test the claim that the
population mean weight is 70 kg
Inference is the process of drawing conclusions or making decisions
about a population based on sample results
8. Sampling
a sample should have the same characteristics
as the population it is representing.
Sampling can be:
with replacement: a member of the population
may be chosen more than once (picking the
candy from the bowl)
without replacement: a member of the
population may be chosen only once (lottery
ticket)
9. Sampling methods
Sampling methods can be:
random (each member of the population has an equal
chance of being selected)
nonrandom
The actual process of sampling causes sampling
errors. For example, the sample may not be large
enough or representative of the population. Factors not
related to the sampling process cause nonsampling
errors. A defective counting device can cause a
nonsampling error.
10. Random sampling methods
simple random sample (each sample of the same
size has an equal chance of being selected)
stratified sample (divide the population into groups
called strata and then take a sample from each
stratum)
cluster sample (divide the population into strata and
then randomly select some of the strata. All the
members from these strata are in the cluster sample.)
systematic sample (randomly select a starting point
and take every n-th piece of data from a listing of the
population)
11. Descriptive Statistics
Collect data
e.g., Survey
Present data
e.g., Tables and graphs
Summarize data
e.g., Sample mean = i
X
n
12. Statistical data
The collection of data that are relevant to the
problem being studied is commonly the most
difficult, expensive, and time-consuming part of
the entire research project.
Statistical data are usually obtained by counting
or measuring items.
Primary data are collected specifically for the
analysis desired
Secondary data have already been compiled and
are available for statistical analysis
A variable is an item of interest that can take on
many different numerical values.
A constant has a fixed numerical value.
13. Data
Statistical data are usually obtained by counting or
measuring items. Most data can be put into the
following categories:
Qualitative - data are measurements that each
fail into one of several categories. (hair color,
ethnic groups and other attributes of the
population)
quantitative - data are observations that are
measured on a numerical scale (distance traveled
to college, number of children in a family, etc.)
14. Qualitative data
Qualitative data are generally described by words or
letters. They are not as widely used as quantitative data
because many numerical techniques do not apply to the
qualitative data. For example, it does not make sense to
find an average hair color or blood type.
Qualitative data can be separated into two subgroups:
dichotomic (if it takes the form of a word with two
options (gender - male or female)
polynomic (if it takes the form of a word with more
than two options (education - primary school,
secondary school and university).
15. Quantitative data
Quantitative data are always numbers and are the
result of counting or measuring attributes of a
population.
Quantitative data can be separated into two
subgroups:
discrete (if it is the result of counting (the number of
students of a given ethnic group in a class, the
number of books on a shelf, ...)
continuous (if it is the result of measuring (distance
traveled, weight of luggage, …)
17. Numerical scale of measurement:
Nominal – consist of categories in each of which the number of
respective observations is recorded. The categories are in no logical
order and have no particular relationship. The categories are said to
be mutually exclusive since an individual, object, or measurement
can be included in only one of them.
Ordinal – contain more information. Consists of distinct categories in
which order is implied. Values in one category are larger or smaller
than values in other categories (e.g. rating-excelent, good, fair, poor)
Interval – is a set of numerical measurements in which the distance
between numbers is of a known, sonstant size.
Ratio – consists of numerical measurements where the distance
between numbers is of a known, constant size, in addition, there is a
nonarbitrary zero point.
18.
19. Numerical presentation of qualitative
data
pivot table (qualitative dichotomic statistical
attributes)
contingency table (qualitative statistical
attributes from which at least one of them is
polynomic)
You should know how to convert absolute
values to relative ones (%).