The document discusses different types of statistical data and analysis methods. It covers descriptive and inferential statistics, different levels of measurement for variables, sampling methods, and approaches for organizing and presenting data through frequency distributions and graphs. Specific topics include nominal, ordinal, interval and ratio levels of measurement, constructing frequency distributions, calculating class intervals, and using histograms and frequency polygons to portray the distribution of vehicle selling prices at a car dealership.
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)
Understanding data type is an important concept in statistics, when you are designing an experiment, you want to know what type of data you are dealing with, that will decide what type of statistical analysis, visualizations and prediction algorithms could be used.
#data #data types #ai #machine learning #statistics #data science #data analytics #artificial intelligence
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
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)
Understanding data type is an important concept in statistics, when you are designing an experiment, you want to know what type of data you are dealing with, that will decide what type of statistical analysis, visualizations and prediction algorithms could be used.
#data #data types #ai #machine learning #statistics #data science #data analytics #artificial intelligence
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
Fundamentals Of Statistics-Definition of statistics,Descriptive and Inferential Statistics,Major Types of Descriptive Statistics,Statistical data analysis
Topic: Types of Data
Student Name: Duwa
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
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Types of Statistics Descriptive and Inferential StatisticsDr. Amjad Ali Arain
Topic: Types of Statistics Descriptive and Inferential Statistics
Student Name: Bushra
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
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
Fundamentals Of Statistics-Definition of statistics,Descriptive and Inferential Statistics,Major Types of Descriptive Statistics,Statistical data analysis
Topic: Types of Data
Student Name: Duwa
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
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Types of Statistics Descriptive and Inferential StatisticsDr. Amjad Ali Arain
Topic: Types of Statistics Descriptive and Inferential Statistics
Student Name: Bushra
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
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
Presentation Slides from InfoLab21 and the Intellectual Property Office's event: "Intellectual Property: Value Creation" at Lancaster House Hotel on 14th February 2012.
In theory, agile methodologies are easy, but the act of transitioning a team out of their comfort zone and to a new way of working can be very difficult and if not done well can cause unnecessary frustrations and poor Agile implementations.
Webinar discussed how people process change, how to start your transition and how to support it.
Grow your own Teeny Tiny Farm by Amber O'NeillArt4Agriculture
The Cream of the Crop Competition invites students in NSW secondary and tertiary education institutions to create a PowerPoint or a video which can be published on the web and win $500.
The competition ask the students to promote the importance of agriculture to their peers, to encourage a better understanding of agriculture as well as promote agricultural careers and rural life.
Revegetation - Keeping farmland productive for future generations by Ayla Web...Art4Agriculture
The Cream of the Crop Competition invites students in NSW secondary and tertiary education institutions to create a PowerPoint or a video which can be published on the web and win $500.
The competition invites NSW secondary and tertiary students to promote the importance of agriculture to their peers, to encourage a better understanding of agriculture as well as promote agricultural careers and rural life.
It is the type of data defined in Statistics & it can also used in the process of knowledge discovery or pattern searching such as data mining, web data mining which is important for the purpose of decision making. The presentation focus on the type of data known as four level of measurement in Statistics.
Outline
1.What is Statistics ?
2.Type of Statistics
3.Type of Sampling
4.Four Level of Measurement
5.Describing Data: Frequency Distributions and Graphic Presentation
Simple Random Sample
all members of the population has the same chance of being selected for a sample.
Systematic Sample
A random starting point is selected, then every k item is selected for the sample.
Stratified Sample
Population is divided into several groups or strata and then a sample is selected from each stratum.
Cluster Sample
Primary units and then samples are drawn from the primary unit.
Diapositiva del libro de Anderson de estadística aplicada a los negocios y la economía, muestra los conceptos de estadística descriptiva..Diapositiva del libro de Anderson de estadística aplicada a los negocios y la economía, muestra los conceptos de estadística descriptiva
1. You are given only three quarterly seasonal indices and quarter.docxjackiewalcutt
1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.
Quarter Seasonal Index Seasonally Adjusted Data
Q1 .80 295
Q2 .85 299
Q3 1.15 270
Q4 --- 271
(Points : 3)
325
225
252
271
Question 2. 2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3)
Alpha and Delta
Delta and Gamma
Alpha and Gamma
Std Dev and Mean
Question 3. 3. Why is the residual mean value important to a forecaster? (Points : 3)
Large mean values indicate nonautoregressiveness.
Small mean values indicate the total amount of error is small.
Large absolute mean values indicate estimate bias. Large mean values indicate the standard error of the model is small.
Question 4. 4. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3)
Ho: r = .05 p < .5
Ho: r = 1 p =.05
Ho: r ≠ 0 p≤.05
Ho: r = 0 p≤.05
Question 5. 5. In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3)
The estimated value is 80% of the average monthly seasonal estimate.
The estimate is .80 of the forecasted Y trend value.
The estimated value is .80 of the historical average CMA values.
The estimated value has 20% more variation than the average historical Y data values.
Question 6. 6. A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3)
H1: u ≥ $1.258,000 A one-tailed t-test to the left.
H1: u = $1.258,000 A two-tailed t-test.
H1: u < $1.258,000 A one-tailed t-test to the left.
H1: p < $1.258,000 A one-tailed test to the right.
Question 7. 7.
The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this?
(Points : 3)
Time series data of profits by store.
Recent 10 year sample of profits by stores.
Recent cross section of store profits by city.
Trend of a random sample of store profits over time.
Question 8. 8. Sometimes forecasters get lazy or forgetful and do not check the significance of XY data correlations ...
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
2. OUTLINE
What is Statistics ?
Type of Statistics
Type of Sampling
Four Level of Measurement
Describing Data: Frequency Distributions and
Graphic Presentation
3. WHAT IS STATISTICS?
The science of collecting, organizing, presenting,
analyzing and interpreting data to assist in making
more effective decision.
4. TO MAKE IMPORTANT DECISION
Determine existing information and additional
information.
Gather additional information, but does not lead
misleading result.
Summarize information in a useful and informative
manner.
Analyze the available information.
Draw conclusion while assessing the risk and
incorrect conclusion
5. TYPE OF STATISTICS
Descriptive Statistics(Without analysis)
Method of organizing, summarizing and presenting data
in an informative way.
Inferential Statistics(With analysis)
Population(A collection of all possible individuals,
objects, or measurements of interest.)
Sample (A portion, or part of the population of interest)
6. SAMPLING A POPULATION
Reason
Impossible to check or locate all the members of the
population
Cost of Studying all the items in the population may be
prohabitive.
The result of a sample is the estimate of the population
parameter thus saving time and money.
It may be too time consuming to contact all the
members of the population.
7. TYPE OF SAMPLE
Type of
Sample
Probability
Sample
Simple
Random
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
Non Probability
Sample
Panel
Sampling
Convenience
Sampling
8. PROBABILITY SAMPLE
Simple Random Sample
all members of the population has the same chance of
being selected for a sample.
Systematic Sample
A random starting point is selected, then every k item is
selected for the sample.
Stratified Sample
Population is divided into several groups or strata and
then a sample is selected from each stratum.
Cluster Sample
Primary units and then samples are drawn from the
primary unit.
9. NONPROBABILITY SAMPLING
Inclusion in the sample is based on the judgment of
the person conducting the sample.
Non Probability samples may lead to biased result.
10. SAMPLING ERROR
The difference between the population parameter
and the sample statistic are called the sampling
error.
11. TYPE OF VARIABLE
Data
Qualitative
Example
Type of car owned
Color of Pen
Gender
Quantitative
or numerical
Discrete
Number of Children
Number of Employee
Number of TV Set
Sold last year
Continuous
Weight of a shipment
Miles driven Distance
Between New York
and Bankok
12. QUALITATIVE VARIABLE
Gender, Religious Affiliation, Type of automobile
owned, State of Birth , Eye color
Qualitative variable can be summarized in bar chart
or pie chart.
For example
What percentage of population has blue eye?
How many Buddhist and Catholics in Myanmar?
What percent of the total number of car sold last month
were Toyota?
13. QUANTITATIVE VARIABLE
Discrete (Gaps between possible values) or
Continuous (Any value within specific range)
Discrete variable result from counting
( there is no 3.56 room in a house)
Example of Discrete variables
number of bedrooms in a house(1, 2,3,4 etc)
number of car arrive toll booth(4, 1, 2 etc)
number of student in each section.
14. QUANTITATIVE VARIABLE
Continuous variable can result from measuring
something.
Example of Quantitative Variable
Air pressure in a tire (15.1 ,15.4, 15.0)
The amount of raison in a box (8g, 8.4, 8.2g)
Time taken of a flight(Ygn to mdy -> 2hours, 2hour 20
minutes, 2 hour 10 minutes) depend on the accuracy of
time device
15. SOURCE OF STATISTICAL DATA
Secondary Data ( Government publication,
Statistical year book, Published Data)
16. FOUR LEVEL OF MEASUREMENT
Nominal Level Data
- Data are sorted into categories with no particular order to the categories.
* Mutually Exclusive- An individual object can appear in one category.
*Exhaustive- An individual object appear in at least one of the categories.
Ordinal Leval Data
One Category is ranked higher than the other
Interval Level Data
- Ranking characteristic of Ordinal + Distance between value is meaningful
Ratio Level Data
all characteristic of interval +zero pt and the ratio of two value is meaningful
17. NORMINAL LEVEL DATA
Carrier Number of calls Percent
AT&T 108115800 75
MCI 20577310 14
Sprint 8238740 6
Other 7130620 5
Total 100%
18. ORDINAL LEVEL DATA
Rating of a finance professor
Rating Frequency
Superior 6
Good 28
Average 25
Poor 12
Inferior 3
19. INTERVAL LEVEL DATA
Temperature( can count , classified, can add,
subtract)
Note : zero degree Fahrenheit does not represent
absence of heat.
20. RATIO LEVEL DATA
Point zero is meaningful.
The ratio of two values is also meaningful.
Example-wage, unit of production, weight, height.
Income
Name Father Son
Jone $ 80000 40000
White 90000 30000
Rho 60000 120000
Scazzro 750000 130000
21. HOW TO DISTINGUISH BETWEEN FOUR LEVEL
OF DATA
Norminal Ordinal Interval Ratio
Mutual
Exclusive(in one
category)
* * * *
Can be presented
in Percentage
* * * *
Ranking Order * * *
Meaningful
* *
Interval
Addition &
Subtraction
* *
Meaningful Zero *
Meaningful Ratio *
Can Multiply &
*
Divide
22. WHAT IS THE LEVEL OF MEASUREMENT FOR
EACH OF THE VARIABLE?
Student Grade point Average
?
28. DESCRIBING DATA: FREQUENCY DISTRIBUTION AND
GRAPHIC PRESENTATION
A frequency distribution is a grouping of data into
categories showing the number of observation in
each mutually exclusive category.
The steps in constructing a frequency distribution
are:
1 .Decide on the size of the class interval.
2. Tally the raw data into the classes.
3. Count the number of tallies in each class.
29. CLASS FREQUENCY &CLASS INTERVAL
The class frequency is the number of observation in
each class.
Class Interval =>
i= Highest Value – Lowest Value/number of Class
Class interval is the difference between the lower limit of
the two consecutive classes.
Class mid point is the halfway between the lower limit of
two consecutive classes.
30. CRITERIA FOR CONSTRUCTION FREQUENCY
DISTRIBUTION
Avoid having fewer than 5 or more than 15 classes.
Avoid Open ended Class.
Keep the class interval same size.
Do not have overlapping classes.
31. RELATIVE FREQUENCY
The relative fequency distribution shows the
percent of the observation in each class.
There are two method for graphically portraying
frequency distribution.
1. Histogram=> portrays the number of frequencies
in each class in the form of rectangle.
2. Frequency Polygon=> line segment connecting
the point formed by the intersection of the class mid
point and the class frequency.
32. ANOTHER ALTERNATIVE
Line Chart => ideal for showing the trend of sale,
income over time.
Bar Chart => showing the changes in business and
economic data over time.
Pie Chart => the percent of various components are
of total.
34. CALCULATING CLASS INTERVAL(1)
i=High Value-Low Value/Number of Classes
i=(33625-12546)/8=$ 2635(suggested class
interval)
$2635 is awkward to work with and difficult to tally.
We round up the $2635 , Say $ 3000
35. CALCULATING THE CLASS INTERVAL BASE ON
THE NUMBER OF OBSERVATIONS(2)
i=(High Value – Low Value)/1+3.322*log of total
frequencies
i=($33625-$12546)/1+3.222(Log 10 80 )=$2879
Rather than the awkward value,nearby value $
3000 is easier.
36. FREQUENCY DISTRIBUTION OF SELLING
PRICE AT WHITNER PONTIAC LAST MONTH
Selling Prices ( $
thousands)
Frequency Relative Frequency
12 up to 15 8 8/80=0.1000
15 up to 18 23 0.2875
18 up to 21 17 0.2185
21 up to 24 18 0.2250
24 up to 27 8 0.1000
27 up to 30 4 0.0500
30 up to 33 1 0.0125
33 up to 36 1 0.0125
Total 80 1
37. NOW THAT WE HAVE ORGANIZED THE DATA INTO A
FREQUENCY DISTRIBUTION, WE CAN SUMMARIZE THE
SELLING PRICES OF THE VEHICLES FOR ROB WHITNER
Selling Price ranged from about $12000 up to about
$36000.
Selling price are concentrated between $15000 and
$ 24000.
A total of 58, or 72.5 percent of vehicles sold within
this range.
The largest concentration is in $15000 up to 18000
class.
The middle of the class(mode) is $16500 , so the
typical selling price is 165000.
By presenting the information to the Mr. Whitner ,
we give him a clear picture of the distribution of
selling prices for last month.
38. FREQUENCY POLYGON
2 Frequency Mid Point
12 up to 15 8 13.5
15 up to 18 23 16.5
18 up to 21 17 19.5
21 up to 24 18 22.5
24 up to 27 8 25.5
27 up to 30 4 28.5
30 up to 33 1 31.5
33 up to 36 1 34.5
Total 80
39. Reference
Statistical Techniques in business and economics
Author : Robert D. Mason
Douglas A. Lind
William G. Marchal