This document provides an overview of hypothesis testing and statistical significance. It defines key concepts such as the null and alternative hypotheses, types of errors, critical values, and test statistics. Various statistical tests are introduced, including parametric tests that assume normal distributions and equal variances like the t-test, and nonparametric tests that are more flexible in their assumptions, like the chi-square test. Factors that influence the choice of test are outlined, such as the measurement scale of the data and whether samples are related or independent. The goal of hypothesis testing is to systematically evaluate hypotheses using sample data and make statistically sound decisions about population parameters.
Data Analytics and the Small Audit Department: How to Implement for Big GainsCaseWare IDEA
Listen to playback of this webinar: https://www.casewareanalytics.com/webinars/data-analytics-and-small-audit-department-how-implement-big-gains
Most internal auditors recognize the need for data analytics and the improved coverage it offers. But did you know that even small audit teams can effectively leverage data analytics in their audit programs?
It is time to get through the excuses and join our experts as they as they debunk the myth that only large audit teams can use data analytics. This webinar discusses how small audit firms can start with an analytics program; how to leverage analytic techniques along with critical thinking at various phases of the audit process, including risk assessment, macro level audit planning and micro-level audit planning; and finally a methodical plan on how small teams can grow their data analytics program to increase their effectiveness and confidence in the internal audit process.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Course - Machine Learning Basics with R Persontyle
This course is meant to be a fast-paced, hands-on introduction to Machine Learning using R. The course will be focusing mainly on basics of Machine Learning methods and practical implementation of these methods to solve real-world problems. This course aims to develop basic understanding of supervised learning methods, through the use of the R programming platform. It describes the different types of learning and the two main categories of their applications: Classification and Regression.
For corporate bookings or to organize on-site training email hello@persontyle.comor call now +44 (0)20 3239 3141
www.persontyle.com
Data Quality: The Data Science struggle nobody mentions - Data Science MeetUp...University of Twente
Presentation about data quality at the second Data Science MeetUp Twente https://www.meetup.com/Data-Meetup-Twente/events/241545781/ on "Responsible Data Analytics", 7 Sep 2017.
Data Analytics and the Small Audit Department: How to Implement for Big GainsCaseWare IDEA
Listen to playback of this webinar: https://www.casewareanalytics.com/webinars/data-analytics-and-small-audit-department-how-implement-big-gains
Most internal auditors recognize the need for data analytics and the improved coverage it offers. But did you know that even small audit teams can effectively leverage data analytics in their audit programs?
It is time to get through the excuses and join our experts as they as they debunk the myth that only large audit teams can use data analytics. This webinar discusses how small audit firms can start with an analytics program; how to leverage analytic techniques along with critical thinking at various phases of the audit process, including risk assessment, macro level audit planning and micro-level audit planning; and finally a methodical plan on how small teams can grow their data analytics program to increase their effectiveness and confidence in the internal audit process.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Course - Machine Learning Basics with R Persontyle
This course is meant to be a fast-paced, hands-on introduction to Machine Learning using R. The course will be focusing mainly on basics of Machine Learning methods and practical implementation of these methods to solve real-world problems. This course aims to develop basic understanding of supervised learning methods, through the use of the R programming platform. It describes the different types of learning and the two main categories of their applications: Classification and Regression.
For corporate bookings or to organize on-site training email hello@persontyle.comor call now +44 (0)20 3239 3141
www.persontyle.com
Data Quality: The Data Science struggle nobody mentions - Data Science MeetUp...University of Twente
Presentation about data quality at the second Data Science MeetUp Twente https://www.meetup.com/Data-Meetup-Twente/events/241545781/ on "Responsible Data Analytics", 7 Sep 2017.
A complete brief introduction and importance on Data Science, Data Analytics, Business Analytics, Tools used for Analytics, Artificial Intelligence and Machine Learning.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
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Check out what machine learning can do when implemented by Hospital administrators for their operational services. We used historical data to test out and got results that could turn around ROIs for many hospitals suffering loses today
Large amounts of antibiotics used for human therapy result in the selection of pathogenic bacteria resistant to multiple drugs, creating a burden on medical care in hospitals, especially for patients admitted to intensive care units (ICU).
Employing Machine learning techniques and building models, better approaches and preventive ways can thus be introduced to lower mortality rates & costs
Several models using scoring techniques like APACHE and SAPS for mortality prediction have been developed to assess severity of illness and predict mortality in intensive care units (ICUs) standardizing research and assessing performance of ICUs. Machine learning can be employed to build better suited models with locally available data
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execu...Saama
Nikhil Gopinath, Senior Solutions Engineer for the Life Sciences at Saama, spoke at EyeforPharma's Clinical Trial Innovation Summit event in February 2017. These slides are from his "Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execution" presentation.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
How to find new ways to add value to your auditsCaseWare IDEA
Past Presentation at IIA GAM
Aaron Boor, IT Audit & Project Automation Manager talks about how he uses technology and data analytics to deliver more value to his organization.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Audit Webinar: Surefire ways to succeed with Data AnalyticsCaseWare IDEA
While the majority of executives and internal audit leaders agree that data analytics is important, according to the 2016 IIA CBOK study, only 40% of respondents are using technology in audit methodology. Why the disconnect?
In this webinar, we identify some of the common challenges associated with starting and continuing to use data analytics in your audit process. Easy-to-implement methods that help expand the use of data analytics and improve your audit coverage are also presented.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
A complete brief introduction and importance on Data Science, Data Analytics, Business Analytics, Tools used for Analytics, Artificial Intelligence and Machine Learning.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Check out what machine learning can do when implemented by Hospital administrators for their operational services. We used historical data to test out and got results that could turn around ROIs for many hospitals suffering loses today
Large amounts of antibiotics used for human therapy result in the selection of pathogenic bacteria resistant to multiple drugs, creating a burden on medical care in hospitals, especially for patients admitted to intensive care units (ICU).
Employing Machine learning techniques and building models, better approaches and preventive ways can thus be introduced to lower mortality rates & costs
Several models using scoring techniques like APACHE and SAPS for mortality prediction have been developed to assess severity of illness and predict mortality in intensive care units (ICUs) standardizing research and assessing performance of ICUs. Machine learning can be employed to build better suited models with locally available data
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execu...Saama
Nikhil Gopinath, Senior Solutions Engineer for the Life Sciences at Saama, spoke at EyeforPharma's Clinical Trial Innovation Summit event in February 2017. These slides are from his "Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execution" presentation.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
How to find new ways to add value to your auditsCaseWare IDEA
Past Presentation at IIA GAM
Aaron Boor, IT Audit & Project Automation Manager talks about how he uses technology and data analytics to deliver more value to his organization.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Audit Webinar: Surefire ways to succeed with Data AnalyticsCaseWare IDEA
While the majority of executives and internal audit leaders agree that data analytics is important, according to the 2016 IIA CBOK study, only 40% of respondents are using technology in audit methodology. Why the disconnect?
In this webinar, we identify some of the common challenges associated with starting and continuing to use data analytics in your audit process. Easy-to-implement methods that help expand the use of data analytics and improve your audit coverage are also presented.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Data Profiling: The First Step to Big Data QualityPrecisely
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
Moving Beyond Batch: Transactional Databases for Real-time DataVoltDB
Join guest Forrester speaker, Principal Analyst Mike Gualtieri, and Dennis Duckworth Director of Product Marketing from VoltDB to learn how enterprises can create a real-time, “origin-zero” data architecture within transactional databases to become a real-time enterprise.
Analytic Transformation | 2013 Loras College Business Analytics SymposiumCartegraph
Loras College is proud to present our annual Business Analytics Symposium on March 27, 2014 at the Grand River Center in Dubuque, IA. Industry experts will share their insights about the evolving field of business analytics opportunities. Learn about everything from best practices when analyzing data to the importance and benefits of building a culture of analytics within your organization.
To learn more, secure your seat or to take advantage of group discounts visit www.loras.edu/bigdata.
Your AI and ML Projects Are Failing – Key Steps to Get Them Back on TrackPrecisely
With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.
View this webinar on-demand, where we start with four foundational data steps to get our AI and ML projects grounded and underway, specifically:
• Framing the business problem
• Identifying the “right” data to collect and work with
• Establishing baselines of data quality through data profiling and business rules
• Assessing fitness for purpose for training and evaluating the subsequent models and algorithms
The Innovator’s Journey: Alternative Asset ManagersState Street
On behalf of State Street, Longitude conducted a global survey of senior executives at investment
organizations during October and November 2014. We asked them to self-assess their confidence and
progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and
governance. The 400 respondents were drawn from 11 countries and included insurance companies,
private and public pension funds, fund-of-funds, foundations, central banks, endowments, sovereign
wealth funds and supranationals. Twenty-nine alternative asset management companies participated in
the survey.
The Innovator’s Journey: Asset Owners Insights State Street
On behalf of State Street, Longitude conducted a global survey of senior executives at investment
organizations during October and November 2014. We asked them to self-assess their confidence and
progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and
governance. The 400 respondents were drawn from 11 countries and included insurance companies,
private and public pension funds, fund-of-funds, foundations, central banks, endowments, sovereign
wealth funds and supranationals. One hundred asset owners participated in the survey.
The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere.
The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data.
Join Cloudera and Forrester to learn:
- What we mean by pervasive analytics, how it impacts your organization, and how to get started
- How leading organizations are using pervasive analytics for competitive advantage
- How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster
The Innovator’s Journey: Asset Manager InsightsState Street
On behalf of State Street, Longitude conducted a global survey of senior executives at investment
organizations during October and November 2014. We asked them to self-assess their confidence and
progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and
governance. The 400 respondents were drawn from 11 countries and included insurance companies,
private and public pension funds, fund-of-funds, foundations, central banks, endowments, sovereign
wealth funds and supranationals. Two hundred asset managers participated in the survey.
Innovative Data Leveraging for Procurement AnalyticsTejari
This webinar will explore the types of problems and questions faced by procurement executives that can benefit most through the application of analytical solutions (e.g. innovation, strategic cost management, risk mitigation, etc.). In addition, we will cover the different forms of cognitive solutions that are emerging to drive real-time decision-making and predictive sourcing capabilities.
The Innovator’s Journey: Insurance Sector InsightsState Street
On behalf of State Street, Longitude conducted a global survey of senior executives at investment
organizations during October and November 2014. We asked them to self-assess their confidence and
progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and
governance. The 400 respondents were drawn from 11 countries and included insurance companies,
private and public pension funds, fund-of-funds, foundations, central banks, endowments, sovereign
wealth funds and supranationals. One-hundred insurance companies participated in the survey.
(BDT207) Use Streaming Analytics to Exploit Perishable Insights | AWS re:Inve...Amazon Web Services
Streaming analytics is about knowing and acting on what's happening in your business and with your customers right this second. Forrester calls these perishable insights because they occur at a moment's notice and you must act on them fast. The high velocity, whitewater flow of data from innumerable real-time data sources such as market data, internet of things, mobile, sensors, clickstream, and even transactions remain largely un-navigated by most firms. The opportunity to leverage streaming analytics has never been greater. In this session, Forrester analyst Mike Gualtieri explains the opportunity, use cases, and how to use cloud-based streaming solutions in your application architecture.
Similar to 5 data analysis approaches dr. hueihsia holloman (20)
2. 15-2
Goal of Data Decription
“The goal is to transform data into
information, and information into insight.
Carly Fiorina
former president and chairwoman,
Hewlett-Packard Co
5. 15-5
Monitoring
Online Survey Data
Online surveys need
special editing attention.
CfMC provides software
and support to research
suppliers to prevent
interruptions from
damaging data .
7. 15-7
Field Editing
Speed without accuracy won’t
help the manager choose the
right direction.
•Field editing review
•Entry gaps identified
•Callbacks made
•Validate results
8. 15-8
Central Editing
Be familiar with instructions
given to interviewers and coders
Do not destroy the original entry
Make all editing entries identifiable and in
standardized form
Initial all answers changed or supplied
Place initials and date of editing
on each instrument completed
11. 15-11
Coding
Open-Ended Questions
6. What prompted you to purchase your
most recent life insurance policy?
_______________________________
_______________________________
_______________________________
_______________________________
_______________________________
_______________________________
_______________________________
_______________________________
16. 15-16
Open-Question Coding
Locus of
Responsibility Mentioned
Not
Mentioned
A. Company
_____________
___________
_______________
_________
B. Customer
_____________
___________
_______________
_________
C. Joint Company-
Customer
_____________
___________
_______________
_________
F. Other
_____________
___________
_______________
_________
Locus of
Responsibility
Frequency (n =
100)
A. Management
1. Sales manager
2. Sales process
3. Other
4. No action area
identified
B. Management
1. Training
C. Customer
1. Buying processes
2. Other
3. No action area
identified
D. Environmental
conditions
E. Technology
F. Other
10
20
7
3
15
12
8
5
20
17. 15-17
Handling “Don’t Know”
Responses
Question: Do you have a productive relationship
with your present salesperson?
Years of
Purchasing Yes No Don’t Know
Less than 1 year 10% 40% 38%
1 – 3 years 30 30 32
4 years or more 60 30 30
Total
100%
n = 650
100%
n = 150
100%
n = 200
20. 15-20
Key Terms
• Bar code
• Codebook
• Coding
• Content analysis
• Data entry
• Data field
• Data file
• Data preparation
• Data record
• Database
• Don’t know response
• Editing
• Missing data
• Optical character
recognition
• Optical mark
recognition
• Precoding
• Spreadsheet
• Voice recognition
22. 15-22
Research Adjusts for Imperfect
Data
“In the future, we’ll stop moaning about the
lack of perfect data and start using the good
data with much more advanced analytics and
data-matching techniques.”
Kate Lynch
research director
Leo Burnett’s Starcom Media Unit
29. 15-29
Variable Population Sample
Mean µ X
Proportion p
Variance 2
s2
Standard deviation s
Size N n
Standard error of the mean x Sx
Standard error of the proportion p Sp
__
_
Symbols
30. 15-30
Key Terms
• Central tendency
• Descriptive statistics
• Deviation scores
• Frequency distribution
• Interquartile range (IQR)
• Kurtosis
• Median
• Mode
• Normal distribution
• Quartile deviation (Q)
• Skewness
• Standard deviation
• Standard normal
distribution
• Standard score (Z score)
• Variability
• Variance
33. 16-33
Learning Objectives
Understand . . .
• That exploratory data analysis techniques
provide insights and data diagnostics by
emphasizing visual representations of the data.
• How cross-tabulation is used to examine
relationships involving categorical variables,
serves as a framework for later statistical
testing, and makes an efficient tool for data
visualization and later decision-making.
34. 16-34
Research as
Competitive Advantage
“As data availability continues to increase, the
importance of identifying/filtering and analyzing
relevant data can be a powerful way to gain an
information advantage over our competition.”
Tom H.C. Anderson
founder & managing partner
Anderson Analytics, LLC
36. 16-36
Researcher Skill Improves Data
Discovery
DDW is a global player in
research services. As this
ad proclaims, you can
“push data into a template
and get the job done,” but
you are unlikely to make
discoveries using a
template process.
39. 16-39
Research Values the
Unexpected
“It is precisely because the unexpected jolts us
out of our preconceived notions, our
assumptions, our certainties, that it is such a
fertile source of innovation.”
Peter Drucker, author
Innovation and Entrepreneurship
40. 16-40
Frequency of Ad Recall
Value Label Value Frequency Percent Valid Cumulative
Percent Percent
54. 16-54
Guidelines for Using Percentages
Averaging percentages
Use of too large percentages
Using too small a base
Percentage decreases can
never exceed 100%
57. 16-57
Exploratory Data Analysis
This Booth Research
Services ad suggests that
the researcher’s role is to
make sense of data
displays.
Great data exploration and
analysis delivers insight
from data.
67. 17-67
Learning Objectives
Understand . . .
• The nature and logic of hypothesis testing.
• A statistically significant difference
• The six-step hypothesis testing procedure.
68. 17-68
Learning Objectives
Understand . . .
• The differences between parametric and
nonparametric tests and when to use each.
• The factors that influence the selection of an
appropriate test of statistical significance.
• How to interpret the various test statistics
69. 17-69
Hypothesis Testing
vs. Theory
“Don’t confuse “hypothesis” and “theory.”
The former is a possible explanation; the
latter, the correct one. The establishment
of theory is the very purpose of science.”
Martin H. Fischer
professor emeritus. physiology
University of Cincinnati
72. 17-72
Hypothesis Testing Finds Truth
“One finds the truth by making a
hypothesis and comparing the truth to
the hypothesis.”
David Douglass
physicist
University of Rochester
75. 17-75
When Data Present a Clear
Picture
As Abacus states in
this ad, when
researchers ‘sift
through the chaos’ and
‘find what matters’ they
experience the “ah ha!”
moment.
76. 17-76
Approaches to Hypothesis
Testing
Classical statistics
• Objective view of
probability
• Established
hypothesis is rejected
or fails to be rejected
• Analysis based on
sample data
Bayesian statistics
• Extension of classical
approach
• Analysis based on
sample data
• Also considers
established
subjective probability
estimates
86. 17-86
Factors Affecting Probability of
Committing a Error
True value of parameter
Alpha level selected
One or two-tailed test used
Sample standard deviation
Sample size
95. 17-95
How to Select a Test
How many samples are involved?
If two or more samples:
are the individual cases independent or related?
Is the measurement scale
nominal, ordinal, interval, or ratio?
96. 17-96
Recommended Statistical
Techniques
Two-Sample Tests
________________________________________
____
k-Sample Tests
________________________________________
____
Measureme
nt Scale
One-Sample
Case
Related
Samples
Independent
Samples
Related
Samples
Independent
Samples
Nominal • Binomial
• x2 one-sample
test
• McNemar • Fisher exact
test
• x2 two-
samples test
• Cochran Q • x2 for k
samples
Ordinal • Kolmogorov-
Smirnov one-
sample test
• Runs test
• Sign test
• Wilcoxon
matched-
pairs test
• Median test
• Mann-
Whitney U
• Kolmogorov-
Smirnov
• Wald-
Wolfowitz
• Friedman
two-way
ANOVA
• Median
extension
• Kruskal-
Wallis one-
way ANOVA
Interval and
Ratio
• t-test
• Z test
• t-test for
paired
samples
• t-test
• Z test
• Repeated-
measures
ANOVA
• One-way
ANOVA
• n-way
ANOVA
97. 17-97
Questions Answered by
One-Sample Tests
• Is there a difference between observed
frequencies and the frequencies we would
expect?
• Is there a difference between observed and
expected proportions?
• Is there a significant difference between some
measures of central tendency and the
population parameter?
99. 17-99
One-Sample t-Test Example
Null Ho: = 50 mpg
Statistical test t-test
Significance level .05, n=100
Calculated value 1.786
Critical test value 1.66
(from Appendix C,
Exhibit C-2)
100. 17-100
One Sample Chi-Square Test
Example
Living Arrangement
Intend
to Join
Number
Interviewed
Percent
(no. interviewed/200)
Expected
Frequencies
(percent x 60)
Dorm/fraternity 16 90 45 27
Apartment/rooming
house, nearby
13 40 20 12
Apartment/rooming
house, distant
16 40 20 12
Live at home 15
_____
30
_____
15
_____
9
_____
Total 60 200 100 60
101. 17-101
One-Sample Chi-Square
Example
Null Ho: 0 = E
Statistical test One-sample chi-square
Significance level .05
Calculated value 9.89
Critical test value 7.82
(from Appendix C,
Exhibit C-3)
104. 17-104
Two-Sample t-Test Example
Null Ho: A sales = B sales
Statistical test t-test
Significance level .05 (one-tailed)
Calculated value 1.97, d.f. = 20
Critical test value 1.725
(from Appendix C,
Exhibit C-2)
106. 17-106
Two-Sample Chi-Square
Example
Null There is no difference in
distribution channel for age
categories.
Statistical test Chi-square
Significance level .05
Calculated value 6.86, d.f. = 2
Critical test value 5.99
(from Appendix C,
Exhibit C-3)
114. 17-114
k-Independent-Samples Tests:
ANOVA
Tests the null hypothesis that the means of three
or more populations are equal
One-way: Uses a single-factor, fixed-effects
model to compare the effects of a treatment or
factor on a continuous dependent variable
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ANOVA Example Continued
Null A1 = A2 = A3
Statistical test ANOVA and F ratio
Significance level .05
Calculated value 28.304, d.f. = 2, 57
Critical test value 3.16
(from Appendix
C, Exhibit C-9)
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Post Hoc: Scheffe’s S Multiple
Comparison Procedure
Verses Diff
Crit.
Diff. p Value
Lufthansa Malaysia
Airlines
19,950 11.400 .0002
Cathay
Pacific
33.950 11.400 .0001
Malaysia
Airlines
Cathay
Pacific
14.000 11.400 .0122
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Repeated-Measures ANOVA
Example
All data are hypothetical.
___________________________________Means Table by Airline _________________________________________________________________________
Count Mean Std. Dev. Std. Error
Rating 1, Lufthansa 20 38.950 14.006 3.132
Rating 1, Malaysia Airlines 20 58.900 15.089 3.374
Rating 1, Cathay Pacific 20 72.900 13.902 3.108
Rating 2, Lufthansa 20 32.400 8.268 1.849
Rating 2, Malaysia Airlines 20 72.250 10.572 2.364
Rating 2, Cathay Pacific 20 79.800 11.265 2.519
__________________________________________________________Model Summary_________________________________________________________
Source d.f. Sum of Squares Mean Square F Value p Value
Airline 2 3552735.50 17763.775 67.199 0.0001
Subject (group) 57 15067.650 264.345
Ratings 1 625.633 625.633 14.318 0.0004
Ratings by air....... 2 2061.717 1030.858 23.592 0.0001
Ratings by subj..... 57 2490.650 43.696
______________________________________Means Table Effect: Ratings_________________________________________________________________
Count Mean Std. Dev. Std. Error
Rating 1 60 56.917 19.902 2.569
Rating 2 60 61.483 23.208 2.996
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Key Terms
• a priori contrasts
• Alternative hypothesis
• Analysis of variance
(ANOVA
• Bayesian statistics
• Chi-square test
• Classical statistics
• Critical value
• F ratio
• Inferential statistics
• K-independent-samples
tests
• K-related-samples tests
• Level of significance
• Mean square
• Multiple comparison
tests (range tests)
• Nonparametric tests
• Normal probability plot
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Key Terms
• Null hypothesis
• Observed significance
level
• One-sample tests
• One-tailed test
• p value
• Parametric tests
• Power of the test
• Practical significance
• Region of acceptance
• Region of rejection
• Statistical significance
• t distribution
• Trials
• t-test
• Two-independent-
samples tests