This presentation covers statistics, its importance, its applications, branches of statistics, basic concepts used in statistics, data sampling, types of sampling,types of data and collection of data.
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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 includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
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
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 includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Measures of Central Tendency
Requirements of good measures of central tendency
mean, median, mode
skewness of distribution
relation between mean, median,mode
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.
Topic: Variance
Student Name: Sonia Khan
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
A Step-By-Step Introduction to SAS Report ProcedureYesAnalytics
The presentation of data is an essential part of every analytics project and there are number of tools within SAS that allows to create a large variety of charts, reports, and data summaries.
PROC REPORT is a particularly powerful and valuable procedure that can be used in this process. It can be used to both summarize and display data, and is highly customizable and highly flexible. It combines features of the PRINT, MEANS, and TABULATE procedures with features of the DATA step.
Here is a step by step introduction to Report Procedure which walks through the PROC REPORT statement and a few of its key options.
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Measures of Central Tendency
Requirements of good measures of central tendency
mean, median, mode
skewness of distribution
relation between mean, median,mode
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.
Topic: Variance
Student Name: Sonia Khan
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
A Step-By-Step Introduction to SAS Report ProcedureYesAnalytics
The presentation of data is an essential part of every analytics project and there are number of tools within SAS that allows to create a large variety of charts, reports, and data summaries.
PROC REPORT is a particularly powerful and valuable procedure that can be used in this process. It can be used to both summarize and display data, and is highly customizable and highly flexible. It combines features of the PRINT, MEANS, and TABULATE procedures with features of the DATA step.
Here is a step by step introduction to Report Procedure which walks through the PROC REPORT statement and a few of its key options.
Concepts and types of anomaly detection and also step-by-step explanation on how to detect anomalies with normal distribution and multivariate normal distribution.
Proc report used in SAS for enhancing the appearance of the report with Output. It is one of the most important procedures with wide scope. Wide range of options can be used in this procedure to vary the output. Even ODS and compute block can be used with this procedure.
The Definitive Guide to Data Modeling for Business IntelligenceEran Levy
Data modelling is one of the most important steps in preparing data for BI analysis. Anyone working with data - either as an analyst or as a passive 'consumer' - should take a few moments to check out this slideshow and learn the bare basics of data modelling for business intelligence.
To learn more about data modeling, watch our free webinar at http://bit.ly/1N095Sn
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.
concept of sample and sampling, sampling process and problems, types of samples: probability and non probability sampling, determination and sample size, sampling and non sampling errors
Statistics and probability - For Demo in Senior High School.pptxJamesRogerBadillo3
Statistics and probability
Tell me something about the picture
What are the things that you want to study?
What is random sampling?
The leader of the group will present their output in the class.
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Random Sampling
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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.
2. Learning Objectives
Definition of Statistics
Importance of Statistics
Applications of Statistics
Branches of Statistics
Population and Sample
Data Sampling
Types of Sampling
Types of Data
Scales of Measurements
Collection of Data
3. Definition of Statistics
Statistics is a branch of science which deals with collection,
presentation, analysis and interpretation of data.
It provides methods for analyzing and assessing the
significance of data.
Statistics enables the transformation of data into information
that can then serve as the basis for decision-making.
4. Importance of Statistics
Presents facts and figures in a definite form.
Helps to condense the data.
Gives idea about the shape ,spread and symmetry of the data.
Facilitates comparison.
Measures the relationship between two or more variables.
Helps in estimation and prediction.
Helps in formulating and testing the hypothesis or a new
theory.
Helps in planning, controlling and decision making.
5. Applications of Statistics
Statistical methods are used in almost all fields at several
phases. Some of the fields are listed below.
• Business and Industry
• Agriculture
• Commerce
• Demography
• Economics
• Education
• Social Sciences
• Biological Sciences
• Medical Sciences
6. Branches of Statistics
There are two main branches of Statistics,
1. Descriptive Statistics:
• Organizes, describes and summarizes the characteristics of
data.
• It includes construction of graphs, charts ,tables and the
calculation of various numeric measures such as mean,
median, standard deviation, percentiles, etc.
• It does not involve generalizing beyond the data at hand.
Examples: a batsman wants to find his batting average for the
past 12 months , a politician wants to know the average
number of votes he received in the past 3 years ,average daily
temperature of a Pune city.
7. Branches of Statistics
2. Inferential Statistics
• Concerns with drawing conclusions or predictions about a
population from the analysis of a random sample drawn from
that population.
• It includes methods like,
• Point estimation
• Interval estimation
• Hypothesis testing
Examples: a politician would like to estimate based on pre-
election polling techniques such as opinion polls; his chance for
winning in the upcoming election, researcher wants to
determine if treatment A is better than treatment B.
8. Population and Sample
Population: An aggregate of objects or individuals under study.
Sample: Any part of population under study.
Example: We want to study the industrial development of XYZ city.
There are total 500 industries in this city. All these 500 industries
constitute a Population. If we randomly choose 100 industries from the
total of 500 industries, these 100 industries will constitute a sample.
Parameters- are numerical values that summarize
characteristics of a population under investigation. Parameter
values are typically unknown.
Statistics- are numerical values that summarize characteristics
of a sample, which can then be used to estimate parameters.
10. Data Sampling
What is Data Sampling?
Sampling is a statistical technique of obtaining a sample of data which
is representative of the population. So that the inferences based on
the sample hold true for population as well.
Why we do Sampling?
When it is not possible to measure every item in the population
and population is infinite.
When the results are needed urgently.
When the area of study is wide.
When the element gets destroyed under investigation.
11. Benefits of Sampling
Sampling reduces processing time. Results can be obtained
quickly due to time saved in data collection and further analysis.
Reduces expenses incurred in collection of data and its
analysis, thus sampling is economical.
Due to reduced volume of work ,data collection and analysis
can be completed efficiently using well trained staff and
sophisticated machinery .Thus it increases accuracy of results.
12. Key concepts used in Sampling
Sampling Units: Members or elements of population.
Sample Size: The number of units in a sample.
Sampling Frame: A list of all members or elements of
population.
13. Types of Sampling
Below are popularly used sampling methods,
Simple Random sampling
Stratified Random Sampling
Systematic Sampling
Cluster Sampling
14. Simple Random Sampling
Each element of a population has an equal chance of being
selected in the sample.
Simple random samples are obtained either by sampling with
replacement or by sampling without replacement.
Sampling with replacement : a population element can be
selected more than one time.
Sampling without replacement: a population element can be
selected only one time.
15. Simple Random Sampling
Generally, the simple random sampling is conducted without
replacement because it is more convenient and gives more
precise results.
Simple random sampling is effective if population is
homogenous i.e. population has no differentiated sections or
classes.
Example: In order to conduct a socio-economic survey of a
particular village, we can randomly select a sample of families
and find per capita income of a village.
17. Stratified Random Sampling
In this method ,the entire population is divided into different non
overlapping homogenous groups called as strata and then a
simple random sample of a suitable size is selected from each
stratum to form a sample.
The strata are divided according to some criterion such as
geographic location, age, gender, religion or income.
Example: To estimate annual income per family we divide the
population into homogenous groups such as families with
yearly income below Rs. 50,000; between Rs. 50,000 - Rs.1
lakh;between Rs. 1 lakh – Rs. 1.5 lakh and above Rs. 1.5 lakh.
Then we use stratified random sampling taking above groups
as strata.
19. Systematic Sampling
This method involves the selection of elements from an
ordered sampling frame.
To draw a systematic sample of size n,
sampling units are numbered from 1 to N where N is the
population size.
calculate the sampling interval k as N/n, where N is population
size and n is sample size.
select a random number say j from 1 to k (sampling interval) and
thereafter select every kth element j+k, j+2k,etc.
Thus systematic sample of size n will include jth ,(j+k) th,(j+2k) th
,…..,(j+(n-1))kth observations.
Only the first unit selected at random determines the entire
sample.
20. Systematic Sampling
Suppose a committee of n=6 students is to be selected from a
class of N=60 students.
To draw a systematic sample of size n = 6,
Students are numbered from 1 to 60 using their roll numbers.
calculate the sampling interval k = 60/6 = 10
select a random number from 1 to 10 (sampling interval),suppose it is 5.
If 5th student is selected ,then the systematic sample will include students
with roll numbers 5,15,25,35,45,55.
22. Cluster Sampling
This method is used when population is large and consists of
several groups. These groups are called as clusters.
In this method, cluster is considered as sampling unit. We
select a simple random sample of clusters. All observations in
the selected clusters are included in the sample.
Smaller the size of clusters better will be the results.
Example: In health survey of a state, state can be divided into
villages (clusters). A simple random sample of villages may be
selected first and then information about each individual in the
selected village can be collected.
25. Types of Data
Data : is any facts or observations collected together for
reference or analysis which is used as a basis for decision
making.
Variable :
Any characteristic which changes its values.
Examples: height, weight, sex, marital status, eye color.
Variables can be classified as Qualitative or Quantitative.
Constant:
A characteristic which does not changes its value or nature.
Example: height of a person after 25 years of age.
26. Types of Data
Qualitative Data:
It is non numerical data that can be arranged into categories.
This data is also called as Categorical data.
Examples: gender of an individual, nationality of a player ,grade in
examination.
Quantitative Data :
It is a numerical data that consists of counts or measurements.
Examples: weight of person, examination marks, profit of a
salesman.
27. Quantitative Data
Quantitative data can further be classified as discrete and
continuous data .
Discrete Data: takes on only a finite or countable number of
values. These are usually whole numbers.
Examples: population of a country, number of cases of certain
disease, number of student in a class.
Continuous Data: takes all possible values in a certain range and
thus have an infinite number of values. This data does not contain
any gaps, breaks or jumps.
Examples: height of a person, temperature at a certain place,
agricultural production.
28. Scales of Measurement
Variables can also be classified based on its scales of
measurements.
Steven S.S introduced four types of scales of measurements:
nominal, ordinal, interval and ratio scales.
There are two scales of measurement for categorical
variables: nominal and ordinal.
There are two scales of measurement for quantitative
variables: interval and ratio.
29. Scales of Measurement
Nominal Scale:
Consist of two or more named categories into which objects are
classified
Data at this level can't be ordered in a meaningful way.
Examples: Classification of individual using blood group,
Classification of individual using sex, caste, nationality.
Ordinal Scale:
Similar to nominal scale ,however data at this level can be
ordered in a meaningful way, but differences between data values
either can not be determined or are meaningless.
Examples: Groups of individuals according to income such as
poor, middle class, rich., Groups of students according to grades
in examination such as fail, second class, first class, first class
with distinction.
30. Scales of Measurement
Interval Scale:
Data from an interval scale can be rank-ordered and has a
sensible spacing of observations such that differences between
measurements are meaningful.
Interval scales lack the ability to calculate ratios between numbers
on the scale because there is no true zero point.
Example: Temperature on the Fahrenheit and Celsius scales.
Ratio Scale:
Data on a ratio scale includes all of the features of interval scale ,in
addition to a true zero point and can therefore accurately indicate
the ratio of difference between two spaces on the measurement
scale.
It is the best scale of measurement and used in almost all places.
Examples: monthly income, height in cm, weight in kg.
31. Collection of Data
There are two types of data according to the method of collection;
Primary Data:
This is the original data collected by investigator himself/herself for
a specific purpose.
This type of data are generally a fresh and collected for the first
time.
This data can be collected by below methods,
• Observation Method
• Interview Method
• Survey method
• Questionnaire method
Examples: Population census, Data collected by a researcher for
his/her project.
32. Collection of Data
Secondary Data:
Data collected by someone else prior to and for a purpose other
than the current project.
This is processed or finished data.
Secondary data is data that is being reused.
It involves less cost, time and effort.
Examples: Data taken from sources like office records, reports
which are already collected by some other agency, Data available
on Internet ,Data from books, Data from magazines.
Note: Data which are primary for one may be secondary for the
other.