QuestionPro Advanced Training Keys to Success - Discrete Conjoint Analysis 101QuestionPro
Our April 2017 Keys to Success series deep dives into Discrete Choice Conjoint Analysis, an effective and advanced survey technique. In this training, learn to analyze how and why customers choose certain products or services over others. This training discusses research industry best practices on creating a comprehensive end-to-end conjoint analysis project, so that you can address and find solutions to even some of the most complex business objectives.
Module 4: Model Selection and EvaluationSara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
QuestionPro Advanced Training Keys to Success - Discrete Conjoint Analysis 101QuestionPro
Our April 2017 Keys to Success series deep dives into Discrete Choice Conjoint Analysis, an effective and advanced survey technique. In this training, learn to analyze how and why customers choose certain products or services over others. This training discusses research industry best practices on creating a comprehensive end-to-end conjoint analysis project, so that you can address and find solutions to even some of the most complex business objectives.
Module 4: Model Selection and EvaluationSara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
Scott Lundberg, Microsoft Research - Explainable Machine Learning with Shaple...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/ngOBhhINWb8
Explainable Machine Learning with Shapley Values
Shapley values are popular approach for explaining predictions made by complex machine learning models. In this talk I will discuss what problems Shapley values solve, an intuitive presentation of what they mean, and examples of how they can be used through the ‘shap’ python package.
Bio: I am a senior researcher at Microsoft Research. Before joining Microsoft, I did my Ph.D. studies at the Paul G. Allen School of Computer Science & Engineering of the University of Washington working with Su-In Lee. My work focuses on explainable artificial intelligence and its application to problems in medicine and healthcare. This has led to the development of broadly applicable methods and tools for interpreting complex machine learning models that are now used in banking, logistics, sports, manufacturing, cloud services, economics, and many other areas.
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
The talk has three parts : the first part gives an overview of data science work, including roadmap of data science team, responsibility and value of data scientists; the second part talks about pitfalls in analysis and teaches some common analysis methods; the third part takes decision support, metrics and AB testing as examples to explain the data science work and how they are translated to business value.
H2O World - Top 10 Data Science Pitfalls - Mark LandrySri Ambati
H2O World 2015 - Mark Landry
Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Hen or egg – DoE-factors or -responses first?
In the context of design of experiments, many start directly with factor selection, although a definition of the target functions seems almost more important. Discussions on this topic resemble the "chicken or egg" problem that Popper once analyzed. In an effort to shed some light on the methodology, I have repurposed this QBD-DoE learning unit. How do you get started with a DoE? Feel free to write it in the comments.
Conjoint Analysis Alternatives in Questionnaire DesignLaurie Gelb
Conjoint analysis in survey research is outmoded: static, closed, attribute-based in a real-time, turn-on-a-dime, conversational world. Heuristic methods offer a cheaper, faster, more actionable framework for both qualitative and quantitative work. This deck briefly outlines the quantitative framework.
DoWhy Python library for causal inference: An End-to-End toolAmit Sharma
As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning.
Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.
For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018) conference: causalinference.gitlab.io/kdd-tutorial.
The concepts of Alpha and Beta errors have long been documented in the statistical literature. Often, the notion of significance has been more widely promoted than statistical Power. Under and over powered tests can easily lead data analysts to draw invalid conclusions. The problem is further complicated by the boom of ‘Big Data’ where rates of automation and data collection have increased exponentially. It is not uncommon for a data set to easily run into millions of rows and, potentially, thousands of columns. This talk will start by discussing the fundamental concepts of alpha and beta error in data analysis, as well as statistical power, while discussing some of the common pitfalls. As data collection and computing power grow at alarming rates, there is a risk for some to focus on the utilizing standard data analysis tools and be lured into making false conclusions. Newer analytic techniques have been developed to complement the wave of Big Data. Some of these newer methods will be briefly introduced as tools for modern day data analysts to consider in their quest for managing risk via proper data analysis.
Fairness and Transparency in Machine LearningAndreas Dewes
My presentation on fairness on transparency in machine learning that I gave at the PyData Berlin. I investigated the "Stop and Frisk" dataset and tried to show how algorithms can pick up (or remove) biases from our data.
Giving You the Edge - The Science of Winning Elections Michael Lieberman
Giving You the Edge – The Science of Winning Elections, written by experienced political consultant Michael Lieberman, identifies and explains the use of key research methodology and multivariate analysis in supporting political campaign goals through the various stages of an election.
This Slideshare presentation is a partial preview of the full business document. To view and download the full document, please go here:
http://flevy.com/browse/business-document/strategy-toolkit-446
Strategy is often a challenging topic. This Toolkit will help you in the development of your business strategy with some models such as:
*Common STEEP Factors
*Five Forces Questions
*5 Market Test
*Generic Strategies
*Competitor Analysis
*SWOT
*TOWS Analysis
*Grand Strategy Selection Matrix
*Grand Strategy Clusters
*Risks & Mitigations
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
Scott Lundberg, Microsoft Research - Explainable Machine Learning with Shaple...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/ngOBhhINWb8
Explainable Machine Learning with Shapley Values
Shapley values are popular approach for explaining predictions made by complex machine learning models. In this talk I will discuss what problems Shapley values solve, an intuitive presentation of what they mean, and examples of how they can be used through the ‘shap’ python package.
Bio: I am a senior researcher at Microsoft Research. Before joining Microsoft, I did my Ph.D. studies at the Paul G. Allen School of Computer Science & Engineering of the University of Washington working with Su-In Lee. My work focuses on explainable artificial intelligence and its application to problems in medicine and healthcare. This has led to the development of broadly applicable methods and tools for interpreting complex machine learning models that are now used in banking, logistics, sports, manufacturing, cloud services, economics, and many other areas.
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
The talk has three parts : the first part gives an overview of data science work, including roadmap of data science team, responsibility and value of data scientists; the second part talks about pitfalls in analysis and teaches some common analysis methods; the third part takes decision support, metrics and AB testing as examples to explain the data science work and how they are translated to business value.
H2O World - Top 10 Data Science Pitfalls - Mark LandrySri Ambati
H2O World 2015 - Mark Landry
Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Hen or egg – DoE-factors or -responses first?
In the context of design of experiments, many start directly with factor selection, although a definition of the target functions seems almost more important. Discussions on this topic resemble the "chicken or egg" problem that Popper once analyzed. In an effort to shed some light on the methodology, I have repurposed this QBD-DoE learning unit. How do you get started with a DoE? Feel free to write it in the comments.
Conjoint Analysis Alternatives in Questionnaire DesignLaurie Gelb
Conjoint analysis in survey research is outmoded: static, closed, attribute-based in a real-time, turn-on-a-dime, conversational world. Heuristic methods offer a cheaper, faster, more actionable framework for both qualitative and quantitative work. This deck briefly outlines the quantitative framework.
DoWhy Python library for causal inference: An End-to-End toolAmit Sharma
As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning.
Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.
For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018) conference: causalinference.gitlab.io/kdd-tutorial.
The concepts of Alpha and Beta errors have long been documented in the statistical literature. Often, the notion of significance has been more widely promoted than statistical Power. Under and over powered tests can easily lead data analysts to draw invalid conclusions. The problem is further complicated by the boom of ‘Big Data’ where rates of automation and data collection have increased exponentially. It is not uncommon for a data set to easily run into millions of rows and, potentially, thousands of columns. This talk will start by discussing the fundamental concepts of alpha and beta error in data analysis, as well as statistical power, while discussing some of the common pitfalls. As data collection and computing power grow at alarming rates, there is a risk for some to focus on the utilizing standard data analysis tools and be lured into making false conclusions. Newer analytic techniques have been developed to complement the wave of Big Data. Some of these newer methods will be briefly introduced as tools for modern day data analysts to consider in their quest for managing risk via proper data analysis.
Fairness and Transparency in Machine LearningAndreas Dewes
My presentation on fairness on transparency in machine learning that I gave at the PyData Berlin. I investigated the "Stop and Frisk" dataset and tried to show how algorithms can pick up (or remove) biases from our data.
Giving You the Edge - The Science of Winning Elections Michael Lieberman
Giving You the Edge – The Science of Winning Elections, written by experienced political consultant Michael Lieberman, identifies and explains the use of key research methodology and multivariate analysis in supporting political campaign goals through the various stages of an election.
This Slideshare presentation is a partial preview of the full business document. To view and download the full document, please go here:
http://flevy.com/browse/business-document/strategy-toolkit-446
Strategy is often a challenging topic. This Toolkit will help you in the development of your business strategy with some models such as:
*Common STEEP Factors
*Five Forces Questions
*5 Market Test
*Generic Strategies
*Competitor Analysis
*SWOT
*TOWS Analysis
*Grand Strategy Selection Matrix
*Grand Strategy Clusters
*Risks & Mitigations
A series of modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
Part 8 of 11
« PreviousHomeNext »Home » Measurement » Levels of Measure.docxodiliagilby
« PreviousHomeNext »
Home » Measurement » Levels of Measurement
The level of measurement refers to the relationship among the values that are assigned
to the attributes for a variable. What does that mean? Begin with the idea of the
variable, in this example "party affiliation." That variable has a number of attributes. Let's
assume that in this particular election context the only relevant attributes are
"republican", "democrat", and "independent". For purposes of
analyzing the results of this variable, we arbitrarily assign the values 1, 2 and 3 to the
three attributes. The level of measurement describes the relationship among
these three values. In this case, we simply are using the numbers as shorter placeholders
for the lengthier text terms. We don't assume that higher values mean "more" of
something and lower numbers signify "less". We don't assume the the value of 2
means that democrats are twice something that republicans are. We don't assume that
republicans are in first place or have the highest priority just because they have the
value of 1. In this case, we only use the values as a shorter name for the attribute.
Here, we would describe the level of measurement as "nominal".
Why is Level of Measurement Important?
First, knowing the level of measurement helps you decide how to interpret the data from
that variable. When you know that a measure is nominal (like the one just described), then
you know that the numerical values are just short codes for the longer names. Second,
knowing the level of measurement helps you decide what statistical analysis is appropriate
on the values that were assigned. If a measure is nominal, then you know that you would
never average the data values or do a t-test on the data.
There are typically four levels of measurement that are defined: Nominal Ordinal Interval Ratio
In nominal measurement the numerical values just "name" the attribute
uniquely. No ordering of the cases is implied. For example, jersey numbers in basketball
are measures at the nominal level. A player with number 30 is not more of anything than a
player with number 15, and is certainly not twice whatever number 15 is.
In ordinal measurement the attributes can be rank-ordered. Here, distances
between attributes do not have any meaning. For example, on a survey you might code
Educational Attainment as 0=less than high school; 1=some high school.; 2=high school degree; 3=some college;
4=college degree; 5=post college. In this measure, higher numbers mean more education.
But is distance from 0 to 1 same as 3 to 4? Of course not. The interval between values is
not interpretable in an ordinal measure.
In interval measurement
the distance between attributes does have meaning. For example, when we measure
temperature (in Fahrenheit), the distance from 30-40 is same as distance from 70-80. The
interval between values is interpretable. Because of this, it makes sense to compute an
average of an interval variable, where it doesn't make sens ...
Top of FormA random telephone survey of adults (aged and older.docxturveycharlyn
Top of Form
A random telephone survey of adults (aged and older) was conducted by research corporation on behalf of an online tax preparation and e-filing service. The survey results showed that of those surveyed planned to file their taxes electronically.
a. Develop a descriptive statistic that can be used to estimate the percentage of all taxpayers who file electronically.
(to nearest whole number)
b. The survey reported that the most frequently used method for preparing the tax return is to hire an accountant or professional tax preparer. If of the people surveyed had their tax return prepared this way, how many people used an accountant or professional tax preparer?
(to nearest whole number)
c. Other methods that the person filing the return often used include manual preparation, use of an online tax service, and use of a software tax program. Would the data for the method for preparing the tax return be considered categorical or quantitative?
· Check My Work
Top of Form
The Tennessean, an online newspaper located in Nashville, Tennessee, conducts a daily poll to obtain reader opinions on a variety of current issues. In a recent poll, readers responded to the following question: "If a constitutional amendment to ban a state income tax is placed on the ballot in Tennessee, would you want it to pass?" Possible responses were Yes, No, or Not Sure.
a. What was the sample size for this poll?
b. Are the data categorical or quantitative?
c. Would it make more sense to use averages or percentages as a summary of the data for this question?
d. Of the respondents in the United States, said Yes, they would want it to pass. How many individuals provided this response? (Round your answer to the integer.)
· Check My Work
The Bureau of Transportation Statistics Omnibus Household Survey is conducted annually and serves as an information source for the U.S. Department of Transportation. In one part of the survey the person being interviewed was asked to respond to the following statement: "Drivers of motor vehicles should be allowed to talk on a hand-held cell phone while driving." Possible responses were strongly agree, some what agree, some what disagree, and strongly disagree. Forty-five respondents said that they strongly agree with this statement, said that they some what agree, said they some what disagree, and said they strongly disagree with this statement.
a. Do the responses for this statement provide categorical or quantitative data?
b. Would it make more sense to use averages or percentages as a summary of the responses for this statement?
c. What percentage of respondents strongly agree with allowing drivers of motor vehicles to talk on a hand-held cell phone while driving?
(to the nearest whole number)
d. Do the results indicate general support for or against allowing drivers of motor vehicles to talk on a hand-held cell phone while driving?
Top of Form
Figure 1.7 provides a bar chart showing the annual revenue for ...
Scenario You are a lieutenant in charge of an undercove.docxkenjordan97598
Scenario:
You are a lieutenant in charge of an undercover strike force team, charged with the responsibility of apprehending fugitives from justice. Your team has been criticized by the local media for some of its members' actions in carrying out their responsibilities, such as using questionable methods that could be seen as potential violation of some individual civil rights. Your team has been very effective in carrying out its assigned duties, resulting in an 80% apprehension rate.
You have been advised by the chief that all he wants is results, not excuses. He wants you to use whatever means are necessary to apprehend fugitives because anything less would reflect badly on the department and his leadership. He reminds you that he has the firm backing of the mayor and city commission in how he runs the department.
The next day, a news reporter informs you that he is working on a story regarding the apprehension of child rapist. Information he has gathered indicates that the arresting officers on the team, under your supervision, may have used questionable methods during the apprehension, which resulted in significant injuries to the individual. He asks for you to comment on the potential violation, and you inform him that you will look into the matter and get back with him later.
Later that evening, you call a meeting of your team and advise the members of the allegations made. It is then brought to your attention that there was some force used in the apprehension that may have exceeded what was necessary. The next morning, you advise the chief of the inquiry by the media, and you tell him that based on your preliminary inquiry, there may be some validity to what the reporter told you. He reminds you of what he expects out of your team: results, not excuses.
Ethics and Police Administration
Respond to the given scenario in 500-600 words addressing the following 8 questions
Due March 5th
Primary Task Response: Write 500–600 words that respond to the following questions with your thoughts, ideas, and comments. Be substantive and clear, and use examples to reinforce your ideas:
1. What do you think are the legal issues involved in the scenario? Explain.
2. What do you think are the ethical issues involved in the scenario? Explain.
3. What are the possible consequences of not addressing these ethical issues? Explain.
4. Considering the directive given to you by your chief that he wants results and not excuses, what are some of the factors that you should take into consideration?
5. How would you respond to the follow-up questions from the reporter? Why?
6. What will most likely result from your responses, and how will you protect yourself and your career? Explain.
7. How significant is it to you that a superior officer is implying that you should make an unethical decision? Explain.
8. How did this affect what you would say to the reporter? Explain.
*Must have a minimum of 2 reliable references with websit.
Real Estate Executive Summary (MKT460 Lab #5)Mira McKee
This is a lab report I wrote for my Marketing 460: Information & Analysis class. Utilizing SPSS, a statistical analysis program, to analyze real estate data, I wrote this report detailing my research steps (including regression analysis, CHAID analysis, customer profiles, etc.) and conclusion. I designed the Lab Report in Canva.
Presentation on the uses & misues of data, embracing illustrations & examples, as presented to the Numis Securities Media Conference in London April 2011
In a May 9, 2024 paper, Juri Opitz from the University of Zurich, along with Shira Wein and Nathan Schneider form Georgetown University, discussed the importance of linguistic expertise in natural language processing (NLP) in an era dominated by large language models (LLMs).
The authors explained that while machine translation (MT) previously relied heavily on linguists, the landscape has shifted. “Linguistics is no longer front and center in the way we build NLP systems,” they said. With the emergence of LLMs, which can generate fluent text without the need for specialized modules to handle grammar or semantic coherence, the need for linguistic expertise in NLP is being questioned.
role of women and girls in various terror groupssadiakorobi2
Women have three distinct types of involvement: direct involvement in terrorist acts; enabling of others to commit such acts; and facilitating the disengagement of others from violent or extremist groups.
हम आग्रह करते हैं कि जो भी सत्ता में आए, वह संविधान का पालन करे, उसकी रक्षा करे और उसे बनाए रखे।" प्रस्ताव में कुल तीन प्रमुख हस्तक्षेप और उनके तंत्र भी प्रस्तुत किए गए। पहला हस्तक्षेप स्वतंत्र मीडिया को प्रोत्साहित करके, वास्तविकता पर आधारित काउंटर नैरेटिव का निर्माण करके और सत्तारूढ़ सरकार द्वारा नियोजित मनोवैज्ञानिक हेरफेर की रणनीति का मुकाबला करके लोगों द्वारा निर्धारित कथा को बनाए रखना और उस पर कार्यकरना था।
03062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
01062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
31052024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
‘वोटर्स विल मस्ट प्रीवेल’ (मतदाताओं को जीतना होगा) अभियान द्वारा जारी हेल्पलाइन नंबर, 4 जून को सुबह 7 बजे से दोपहर 12 बजे तक मतगणना प्रक्रिया में कहीं भी किसी भी तरह के उल्लंघन की रिपोर्ट करने के लिए खुला रहेगा।
Maximum Difference Statistical Analysis For Determining Political Campaign Messages 2010
1. Strategic Message Analysis Maximum Difference Analysis Multivariate Solutions www.mvsolution.com Michael Lieberman [email_address]
2.
3.
4.
5.
6. Maximum Difference Survey Structure Below are three potential choice scenarios. Least Important Factor Most Important Increasing funding for local schools x Creating more electricity to meet increasing demand Protecting Social Security and Medicare x Restricting the sale of handguns Least Important Factor Most Important Ensuring everyone has access to affordable health care Protecting the environment x Improving the local transportation system and reducing traffic congestion x Keeping taxes and government spending down Least Important Factor Most Important Protecting Social Security and Medicare x Fighting and preventing crime Restricting the sale of handguns Creating more electricity to meet increasing demand x
8. Maximum-Difference Analysis 'Most Appealing' vs. 'Least Appealing' Percentage Total Sample The larger the contrast between 'Most Appealing' (blue), and 'Least Appealing' (red), the more desirable the attribute.
9. Maximum-Difference Analysis 'Most Appealing' vs. 'Least Appealing' Percentage Total Sample The larger the contrast between 'Most Appealing' (blue), and 'Least Appealing' (red), the more desirable the attribute.
10.
Editor's Notes
1 National Jewish Outreach Hebrew Reading Crash Course 1
2 National Jewish Outreach Hebrew Reading Crash Course
2 National Jewish Outreach Hebrew Reading Crash Course
2 National Jewish Outreach Hebrew Reading Crash Course
2 National Jewish Outreach Hebrew Reading Crash Course
2 National Jewish Outreach Hebrew Reading Crash Course
2 National Jewish Outreach Hebrew Reading Crash Course