162 In-Depth Data collection Questions for Professionals

What is involved in Data collection

Find out what the related areas are that Data collection connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Data collection thinking-frame.

How far is your company on its Data collection journey?

Take this short survey to gauge your organization’s progress toward Data collection leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which Data collection related domains to cover and 162 essential critical questions to check off in that domain.

The following domains are covered:

Data collection, Control chart, Binomial regression, Likelihood-ratio test, Randomization test, Statistical process control, Poisson regression, Jonckheere’s trend test, Seasonal adjustment, Wilcoxon signed-rank test, Logistic regression, Likelihood interval, Multivariate normal distribution, Mann–Whitney U test, Loss function, Hodges–Lehmann estimator, Kaplan–Meier estimator, Method of moments, Bias of an estimator, Statistical classification, One- and two-tailed tests, Accelerated failure time model, Lp space, Run chart, Student’s t-test, Box plot, Clinical trial, Jackknife resampling, McNemar’s test, Factorial experiment, Coefficient of determination, Outline of statistics, Multivariate distribution, Autoregressive–moving-average model, Density estimation, Kruskal–Wallis one-way analysis of variance, Structural break, Bayesian probability, Prentice Hall, Empirical distribution function, Stationary process, Scientific control, Partition of sums of squares, Order statistic, First-hitting-time model, Grouped data, Monotone likelihood ratio, Environmental statistics, Statistical power, Bayes factor, Maximum a posteriori estimation, Geographic information system, Radar chart, Interquartile range, Contingency table, Stratified sampling, Log-rank test, Cluster sampling, Bar chart, Minimum-variance unbiased estimator, Coefficient of variation, Sign test, Survival analysis, Spearman’s rank correlation coefficient:

Data collection Critical Criteria:

Steer Data collection governance and attract Data collection skills.

– Traditional data protection principles include fair and lawful data processing; data collection for specified, explicit, and legitimate purposes; accurate and kept up-to-date data; data retention for no longer than necessary. Are additional principles and requirements necessary for IoT applications?

– How is source data collected (paper questionnaire, computer assisted person interview, computer assisted telephone interview, web data collection form)?

– What should I consider in selecting the most resource-effective data collection design that will satisfy all of my performance or acceptance criteria?

– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?

– Do we double check that the data collected follows the plans and procedures for data collection?

– Do data reflect stable and consistent data collection processes and analysis methods over time?

– What is the definitive data collection and what is the legacy of said collection?

– Who is responsible for co-ordinating and monitoring data collection and analysis?

– To what extent does management recognize Data collection as a tool to increase the results?

– Do we use controls throughout the data collection and management process?

– Do you define jargon and other terminology used in data collection tools?

– How can the benefits of Big Data collection and applications be measured?

– Do you use the same data collection methods for all sites?

– Do you clearly document your data collection methods?

– What is the schedule and budget for data collection?

– Is our data collection and acquisition optimized?

– What are our Data collection Processes?

Control chart Critical Criteria:

Face Control chart planning and prioritize challenges of Control chart.

– How will you measure your Data collection effectiveness?

– How do we maintain Data collections Integrity?

Binomial regression Critical Criteria:

Discuss Binomial regression visions and achieve a single Binomial regression view and bringing data together.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Data collection in a volatile global economy?

– How do we make it meaningful in connecting Data collection with what users do day-to-day?

– How do we go about Securing Data collection?

Likelihood-ratio test Critical Criteria:

Pay attention to Likelihood-ratio test tasks and gather practices for scaling Likelihood-ratio test.

– In what ways are Data collection vendors and us interacting to ensure safe and effective use?

– Why is it important to have senior management support for a Data collection project?

– Is a Data collection Team Work effort in place?

Randomization test Critical Criteria:

Demonstrate Randomization test leadership and get out your magnifying glass.

– Have the types of risks that may impact Data collection been identified and analyzed?

– What are all of our Data collection domains and what do they do?

– What are the Key enablers to make this Data collection move?

Statistical process control Critical Criteria:

Have a round table over Statistical process control strategies and summarize a clear Statistical process control focus.

– Are Acceptance Sampling and Statistical Process Control Complementary or Incompatible?

– How do we Lead with Data collection in Mind?

– How much does Data collection help?

Poisson regression Critical Criteria:

Win new insights about Poisson regression leadership and create Poisson regression explanations for all managers.

– Who needs to know about Data collection ?

– How do we keep improving Data collection?

– Why are Data collection skills important?

Jonckheere’s trend test Critical Criteria:

Study Jonckheere’s trend test engagements and track iterative Jonckheere’s trend test results.

– Think about the people you identified for your Data collection project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?

– Do several people in different organizational units assist with the Data collection process?

– What are current Data collection Paradigms?

Seasonal adjustment Critical Criteria:

Group Seasonal adjustment projects and diversify disclosure of information – dealing with confidential Seasonal adjustment information.

– How important is Data collection to the user organizations mission?

– What is our formula for success in Data collection ?

Wilcoxon signed-rank test Critical Criteria:

Devise Wilcoxon signed-rank test adoptions and develop and take control of the Wilcoxon signed-rank test initiative.

– Can we do Data collection without complex (expensive) analysis?

– Are we Assessing Data collection and Risk?

Logistic regression Critical Criteria:

Do a round table on Logistic regression issues and figure out ways to motivate other Logistic regression users.

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Data collection. How do we gain traction?

Likelihood interval Critical Criteria:

X-ray Likelihood interval strategies and integrate design thinking in Likelihood interval innovation.

– What knowledge, skills and characteristics mark a good Data collection project manager?

Multivariate normal distribution Critical Criteria:

Start Multivariate normal distribution tasks and get answers.

– How do we ensure that implementations of Data collection products are done in a way that ensures safety?

– Is the Data collection organization completing tasks effectively and efficiently?

Mann–Whitney U test Critical Criteria:

Adapt Mann–Whitney U test leadership and get the big picture.

– Who is the main stakeholder, with ultimate responsibility for driving Data collection forward?

– How do we know that any Data collection analysis is complete and comprehensive?

– What about Data collection Analysis of results?

Loss function Critical Criteria:

Rank Loss function projects and oversee Loss function management by competencies.

– What are the barriers to increased Data collection production?

– Why should we adopt a Data collection framework?

Hodges–Lehmann estimator Critical Criteria:

Explore Hodges–Lehmann estimator visions and get answers.

– In the case of a Data collection project, the criteria for the audit derive from implementation objectives. an audit of a Data collection project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Data collection project is implemented as planned, and is it working?

– How do we Improve Data collection service perception, and satisfaction?

Kaplan–Meier estimator Critical Criteria:

Confer over Kaplan–Meier estimator governance and frame using storytelling to create more compelling Kaplan–Meier estimator projects.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data collection processes?

– What potential environmental factors impact the Data collection effort?

Method of moments Critical Criteria:

Test Method of moments strategies and define what our big hairy audacious Method of moments goal is.

– How do we manage Data collection Knowledge Management (KM)?

– Are there recognized Data collection problems?

– What is Effective Data collection?

Bias of an estimator Critical Criteria:

Give examples of Bias of an estimator projects and oversee implementation of Bias of an estimator.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data collection services/products?

Statistical classification Critical Criteria:

Derive from Statistical classification leadership and ask what if.

– What tools do you use once you have decided on a Data collection strategy and more importantly how do you choose?

– How can the value of Data collection be defined?

One- and two-tailed tests Critical Criteria:

Model after One- and two-tailed tests issues and research ways can we become the One- and two-tailed tests company that would put us out of business.

– How would one define Data collection leadership?

Accelerated failure time model Critical Criteria:

Gauge Accelerated failure time model quality and correct better engagement with Accelerated failure time model results.

– How do mission and objectives affect the Data collection processes of our organization?

– Are assumptions made in Data collection stated explicitly?

Lp space Critical Criteria:

Pay attention to Lp space quality and visualize why should people listen to you regarding Lp space.

– What is the total cost related to deploying Data collection, including any consulting or professional services?

– How do we measure improved Data collection service perception, and satisfaction?

– Which individuals, teams or departments will be involved in Data collection?

Run chart Critical Criteria:

Scrutinze Run chart management and observe effective Run chart.

– Can we add value to the current Data collection decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

– Is Supporting Data collection documentation required?

Student’s t-test Critical Criteria:

Add value to Student’s t-test management and acquire concise Student’s t-test education.

– Meeting the challenge: are missed Data collection opportunities costing us money?

– What is the purpose of Data collection in relation to the mission?

Box plot Critical Criteria:

Discuss Box plot quality and report on the economics of relationships managing Box plot and constraints.

– Think about the kind of project structure that would be appropriate for your Data collection project. should it be formal and complex, or can it be less formal and relatively simple?

Clinical trial Critical Criteria:

Analyze Clinical trial visions and catalog Clinical trial activities.

– Are there any disadvantages to implementing Data collection? There might be some that are less obvious?

– What are the record-keeping requirements of Data collection activities?

– What are the long-term Data collection goals?

Jackknife resampling Critical Criteria:

Tête-à-tête about Jackknife resampling visions and find out what it really means.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Data collection process. ask yourself: are the records needed as inputs to the Data collection process available?

– Are there any easy-to-implement alternatives to Data collection? Sometimes other solutions are available that do not require the cost implications of a full-blown project?

McNemar’s test Critical Criteria:

Consider McNemar’s test tasks and summarize a clear McNemar’s test focus.

– How do you determine the key elements that affect Data collection workforce satisfaction? how are these elements determined for different workforce groups and segments?

– How will we insure seamless interoperability of Data collection moving forward?

Factorial experiment Critical Criteria:

Paraphrase Factorial experiment failures and document what potential Factorial experiment megatrends could make our business model obsolete.

– What tools and technologies are needed for a custom Data collection project?

– Do Data collection rules make a reasonable demand on a users capabilities?

Coefficient of determination Critical Criteria:

Detail Coefficient of determination failures and look in other fields.

– Is maximizing Data collection protection the same as minimizing Data collection loss?

Outline of statistics Critical Criteria:

Unify Outline of statistics goals and report on setting up Outline of statistics without losing ground.

– What are your most important goals for the strategic Data collection objectives?

Multivariate distribution Critical Criteria:

Value Multivariate distribution management and create a map for yourself.

– What are our best practices for minimizing Data collection project risk, while demonstrating incremental value and quick wins throughout the Data collection project lifecycle?

– Does Data collection systematically track and analyze outcomes for accountability and quality improvement?

– What sources do you use to gather information for a Data collection study?

Autoregressive–moving-average model Critical Criteria:

Conceptualize Autoregressive–moving-average model governance and ask questions.

– What role does communication play in the success or failure of a Data collection project?

– What is our Data collection Strategy?

Density estimation Critical Criteria:

Guide Density estimation projects and get the big picture.

Kruskal–Wallis one-way analysis of variance Critical Criteria:

Demonstrate Kruskal–Wallis one-way analysis of variance tactics and be persistent.

– Does Data collection create potential expectations in other areas that need to be recognized and considered?

Structural break Critical Criteria:

See the value of Structural break management and stake your claim.

– In a project to restructure Data collection outcomes, which stakeholders would you involve?

Bayesian probability Critical Criteria:

Align Bayesian probability strategies and budget for Bayesian probability challenges.

– Do those selected for the Data collection team have a good general understanding of what Data collection is all about?

– How can you negotiate Data collection successfully with a stubborn boss, an irate client, or a deceitful coworker?

Prentice Hall Critical Criteria:

Graph Prentice Hall governance and balance specific methods for improving Prentice Hall results.

Empirical distribution function Critical Criteria:

Coach on Empirical distribution function management and probe using an integrated framework to make sure Empirical distribution function is getting what it needs.

– What prevents me from making the changes I know will make me a more effective Data collection leader?

– Is the scope of Data collection defined?

Stationary process Critical Criteria:

Powwow over Stationary process goals and get going.

– Are there Data collection problems defined?

Scientific control Critical Criteria:

Meet over Scientific control leadership and raise human resource and employment practices for Scientific control.

– Which customers cant participate in our Data collection domain because they lack skills, wealth, or convenient access to existing solutions?

Partition of sums of squares Critical Criteria:

X-ray Partition of sums of squares risks and find out.

– What new services of functionality will be implemented next with Data collection ?

– How to deal with Data collection Changes?

Order statistic Critical Criteria:

Differentiate Order statistic tasks and integrate design thinking in Order statistic innovation.

– Is there any existing Data collection governance structure?

– What are specific Data collection Rules to follow?

First-hitting-time model Critical Criteria:

Generalize First-hitting-time model strategies and secure First-hitting-time model creativity.

– What are internal and external Data collection relations?

Grouped data Critical Criteria:

Group Grouped data leadership and develop and take control of the Grouped data initiative.

Monotone likelihood ratio Critical Criteria:

Facilitate Monotone likelihood ratio issues and get answers.

– What will be the consequences to the business (financial, reputation etc) if Data collection does not go ahead or fails to deliver the objectives?

– What are the short and long-term Data collection goals?

Environmental statistics Critical Criteria:

Deliberate Environmental statistics projects and improve Environmental statistics service perception.

– Risk factors: what are the characteristics of Data collection that make it risky?

– Have all basic functions of Data collection been defined?

Statistical power Critical Criteria:

Huddle over Statistical power results and attract Statistical power skills.

Bayes factor Critical Criteria:

Incorporate Bayes factor adoptions and find out what it really means.

Maximum a posteriori estimation Critical Criteria:

Familiarize yourself with Maximum a posteriori estimation adoptions and research ways can we become the Maximum a posteriori estimation company that would put us out of business.

– Who will be responsible for deciding whether Data collection goes ahead or not after the initial investigations?

– Does Data collection appropriately measure and monitor risk?

Geographic information system Critical Criteria:

Look at Geographic information system leadership and secure Geographic information system creativity.

– Does Data collection analysis isolate the fundamental causes of problems?

Radar chart Critical Criteria:

Give examples of Radar chart failures and assess what counts with Radar chart that we are not counting.

– What are the disruptive Data collection technologies that enable our organization to radically change our business processes?

Interquartile range Critical Criteria:

Reconstruct Interquartile range tasks and check on ways to get started with Interquartile range.

– What threat is Data collection addressing?

Contingency table Critical Criteria:

Chat re Contingency table governance and clarify ways to gain access to competitive Contingency table services.

Stratified sampling Critical Criteria:

Adapt Stratified sampling management and look at the big picture.

– What are the key elements of your Data collection performance improvement system, including your evaluation, organizational learning, and innovation processes?

– Who are the people involved in developing and implementing Data collection?

– Can Management personnel recognize the monetary benefit of Data collection?

Log-rank test Critical Criteria:

Study Log-rank test decisions and gather Log-rank test models .

Cluster sampling Critical Criteria:

Refer to Cluster sampling engagements and diversify by understanding risks and leveraging Cluster sampling.

– What are your results for key measures or indicators of the accomplishment of your Data collection strategy and action plans, including building and strengthening core competencies?

– How will you know that the Data collection project has been successful?

Bar chart Critical Criteria:

Confer over Bar chart planning and perfect Bar chart conflict management.

– What other jobs or tasks affect the performance of the steps in the Data collection process?

Minimum-variance unbiased estimator Critical Criteria:

Merge Minimum-variance unbiased estimator visions and ask what if.

– How do senior leaders actions reflect a commitment to the organizations Data collection values?

Coefficient of variation Critical Criteria:

Paraphrase Coefficient of variation planning and find answers.

– At what point will vulnerability assessments be performed once Data collection is put into production (e.g., ongoing Risk Management after implementation)?

– How do we go about Comparing Data collection approaches/solutions?

Sign test Critical Criteria:

Track Sign test tasks and reduce Sign test costs.

– Does Data collection analysis show the relationships among important Data collection factors?

– Who will provide the final approval of Data collection deliverables?

Survival analysis Critical Criteria:

Consider Survival analysis tasks and inform on and uncover unspoken needs and breakthrough Survival analysis results.

– Is there a Data collection Communication plan covering who needs to get what information when?

– What are our needs in relation to Data collection skills, labor, equipment, and markets?

– Do we all define Data collection in the same way?

Spearman’s rank correlation coefficient Critical Criteria:

See the value of Spearman’s rank correlation coefficient leadership and display thorough understanding of the Spearman’s rank correlation coefficient process.

– Where do ideas that reach policy makers and planners as proposals for Data collection strengthening and reform actually originate?

– What are the Essentials of Internal Data collection Management?


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data collection Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | http://theartofservice.com



Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Data collection External links:

Welcome! > Demographic Data Collection Tool

A Guide to CRA Data Collection and Reporting

L.A. COUNTY PUBLIC HEALTH – Data Collection & Analysis

Control chart External links:

[PDF]CONTROL CHART – Air University

Chart control chartarea title | The ASP.NET Forums

[PPT]Control Chart – Indiana University of Pennsylvania
http://www.hhs.iup.edu/CJANICAK/SAFE541CJ/Attribute Control Charts.ppt

Binomial regression External links:

Negative Binomial Regression « The Mathematica Journal

12.3 – Log-binomial Regression | STAT 507

[PDF]Negative Binomial Regression Models and …

Randomization test External links:

The randomization test – a significance test using resampling

Randomization Tests (1 of 6) – David Lane

Statistical process control External links:

Statistical Process Control (SPC) Tutorial – MoreSteam.com

WinSPC – statistical process control software

Statistical Process Control Flashcards | Quizlet

Poisson regression External links:

Bivariate Poisson Regression in R? – Stack Overflow

9.2 – R – Poisson Regression Model for Count Data | STAT 504

Poisson Regression – msdn.microsoft.com

Jonckheere’s trend test External links:

Jonckheere’s Trend Test – STATEXT

Seasonal adjustment External links:

Seasonal Adjustment – investopedia.com

Seasonal adjustment (Book, 2003) [WorldCat.org]

[PDF]Seasonal Adjustment and Multiple Time Series Analysis

Wilcoxon signed-rank test External links:

Wilcoxon Signed-Rank Test – VassarStats

Wilcoxon signed-rank test – Handbook of Biological Statistics

Wilcoxon Signed-Rank Test Calculator

Logistic regression External links:

Logistic Regression – San Francisco State University

Assumptions of Logistic Regression – Statistics Solutions

Lesson 8: Multinomial Logistic Regression Models – …

Likelihood interval External links:

[PDF]E. Santovetti lesson 4 Maximum likelihood Interval …

Likelihood interval – iSnare Free Encyclopedia

Multivariate normal distribution External links:

Lesson 4: Multivariate Normal Distribution | STAT 505

Multivariate Normal Distribution Matlab, probability …

Loss function External links:

What is Taguchi Loss Function – Lean Manufacturing and …

How to use custom loss function (PU Learning) – Stack Overflow

Using Taguchi’s Loss Function to Estimate Project Benefits
http://www.isixsigma.com › Methodology › Robust Design/Taguchi Method

Method of moments External links:

In statistics, the method of moments is a method of estimation of population parameters. One starts with deriving equations that relate the population moments (i.e., the expected values of powers of the random variable under consideration) to the parameters of interest.
http://Reference: en.wikipedia.org/wiki/Method_of_moments_(statistics)

[PDF]Method of Moments – University of Arizona

Method of Moments | STAT 414 / 415

Bias of an estimator External links:

Fixed Effects | Estimator | Bias Of An Estimator

Method of Moments | Estimator | Bias Of An Estimator

Statistical classification External links:

What Is Statistical Classification? (with pictures) – wiseGEEK

[PDF]International Statistical Classification of Diseases …

One- and two-tailed tests External links:

One- and Two-Tailed Tests – Free Statistics Book

One- and Two-Tailed Tests (3 of 4) – David Lane

One- and two-tailed tests – YouTube

Accelerated failure time model External links:

Accelerated failure time model – YouTube

The Accelerated Failure Time Model – YouTube

Lp space External links:

Space Heaters | MR. Heater | LP Space Heater – Next Day MRO

Qwika – Lp space

Lp space – Wiktionary

Run chart External links:

[PDF]Run Chart – CDC – Centers for Disease Control and …

RUN CHART IN EXCEL – Manage Naturally

Run Chart – spcforexcel.com

Student’s t-test External links:

Student’s t-test | statistics | Britannica.com

Box plot External links:

[PDF]Title stata.com graph box — Box plots

Box plot – MATLAB boxplot – MathWorks

What is box plot? – Definition from WhatIs.com

Clinical trial External links:

Clinical Trial Logistics | MARKEN

Clinical Trial Center | Loma Linda University

Greenphire | Reimbursement Solutions | Clinical Trial …

McNemar’s test External links:

Example of McNemar’s test – GraphPad Software

McNemar’s Test – Statistics Solutions

McNemar’s Test | Real Statistics Using Excel

Coefficient of determination External links:

Definition of Coefficient Of Determination | Chegg.com

Coefficient of Determination – Investopedia

1.5 – The Coefficient of Determination, r-squared | STAT 501

Outline of statistics External links:

Jan 01, 1982 · Schaum’s Outline of Statistics and Econometrics has 43 ratings and 0 reviews. Confusing Textbooks? Missed Lectures? Not Enough Time?Fortunately for …

Density estimation External links:

Plotting 2D Kernel Density Estimation with Python

[PDF]Density Estimation for Censored Economic Data

[PDF]L7: Kernel density estimation

Structural break External links:

What is Structural Break | IGI Global

Bayesian probability External links:

Bayesian Probability Theory (eBook, 2014) [WorldCat.org]

What is BAYESIAN PROBABILITY? definition of …

Empirical distribution function External links:

DTIC ADA030940: The Empirical Distribution Function …

Empirical Distribution Function – ubalt.edu

Empirical Distribution Function in Excel – YouTube

Stationary process External links:

Stationary process – YouTube

What does it mean by ‘Ergodic Stationary Process ‘? – Quora

Scientific control External links:

Abstract | Coagulation | Scientific Control

[PDF]Scientific Control Group – Explorable.com

Partition of sums of squares External links:

Partition Of Sums Of Squares images on Photobucket
http://photobucket.com/images/partition of sums of squares#!

Order statistic External links:

Order statistic – Encyclopedia of Mathematics

Order Statistic | Median | Probability Distribution

[PDF]Order Statistics – University of Toronto
http://fisher.utstat.toronto.edu/~hadas/STA257/Lecture notes/week10.pdf

First-hitting-time model External links:

“First-hitting-time model” on Revolvy.com
https://update.revolvy.com/topic/First-hitting-time model

First-hitting-time model explained

First-hitting-time model – YouTube

Grouped data External links:

5.6 Mean, Variance and Standard Deviation for Grouped Data

Grouped Data Histograms | Passy’s World of Mathematics

Select first and last row from grouped data – Stack Overflow

Monotone likelihood ratio External links:

[PDF]Testing for the Monotone Likelihood Ratio Assumption

Environmental statistics External links:

Environmental Statistics – Statistic Brain

Environmental statistics – Ballotpedia

Environmental statistics (eBook, 1994) [WorldCat.org]

Statistical power External links:

Statistical Power | Real Statistics Using Excel

Making sense of statistical power – American Nurse Today

Bayes factor External links:

[PDF]The Bayes Factor

How to calculate a Bayes factor – YouTube

Bayes factor legal definition of Bayes factor

Geographic information system External links:

COT – Geographic Information System (GIS)

Geographic Information System

Fulton County Geographic Information System

Radar chart External links:

Radar Chart (4 Measures & 1 Dimension) ??? |Tableau …

Top 50 Radar Chart | SmoothJazz.com

Using a Radar chart in Excel to see the big picture

Contingency table External links:

r – How do I get a contingency table? – Stack Overflow

Contingency Table | JMP 12

Contingency Tables – onlinestatbook.com

Stratified sampling External links:

Stratified Sampling Flashcards | Quizlet

6.1 How to Use Stratified Sampling | STAT 506

Log-rank test External links:

Log-rank test in R – YouTube

[PDF]Power and Sample Size Calculation for Log-rank Test …

Survival Curves and Log-Rank Test (Evan’s Awesome A/B …

Cluster sampling External links:

[PDF]Cluster Sampling and Its Applications in Image …

Cluster Sampling – stattrek.com

Cluster Sampling Flashcards | Quizlet

Bar chart External links:

R Bar Charts – Tutorials Point

Bar Charts | Charts | Google Developers


Coefficient of variation External links:

Z-4: Mean, Standard Deviation, And Coefficient Of Variation

Coefficient Of Variation (CV) – Video | Investopedia

Coefficient Of Variation – Merriam-Webster
https://www.merriam-webster.com/dictionary/coefficient of variation

Sign test External links:

DMV Virginia Traffic Sign Test 4 – DMVVATest.com

Sign test – Encyclopedia of Mathematics

Traffic & Road Sign Test

Survival analysis External links:

[PDF]Lecture 15 Introduction to Survival Analysis

GraphPad – FAQ 1226 – Hazard ratio from survival analysis.

Survival Analysis with Stata – IDRE Stats

Spearman’s rank correlation coefficient External links:

Spearman’s rank correlation coefficient – YouTube