This document provides information and guidance about conducting an investigative geography exam, including defining key terms, establishing questions and hypotheses, collecting primary and secondary data, organizing and recording data, and presenting data using various graph types. It discusses qualitative and quantitative data and provides examples of setting up an investigation to analyze how building heights change from suburbs to the city center.
We have two sources for forest variables, from direct measurements, which are always expensive and
would be sparse in space, and correlated LiDAR data that has complete coverage. The Bonanza Creek Experimental Forest (BCEF) is a Long-Term Ecological Research (LTER) site consisting of vegetation and landforms typical of interior Alaska. People are interested in three forest variables: above-ground biomass (AGB); tree density (TD); basal area (BA). The brightness, greenness, and wetness tasseled cap indices can be used as covariates to explain the forest variables. In the undergraduate workshop project, students can brainstorm from the easiest regression models to more sophisticated spatial models and compare the differences
of inferences from different ideas.
Group members: Richard Groenwald, Mehmut Hatip, Katrina Lewis, Jennifer Soter, Astride Tchkaoua, Sylvester Wieb
We have two sources for forest variables, from direct measurements, which are always expensive and
would be sparse in space, and correlated LiDAR data that has complete coverage. The Bonanza Creek Experimental Forest (BCEF) is a Long-Term Ecological Research (LTER) site consisting of vegetation and landforms typical of interior Alaska. People are interested in three forest variables: above-ground biomass (AGB); tree density (TD); basal area (BA). The brightness, greenness, and wetness tasseled cap indices can be used as covariates to explain the forest variables. In the undergraduate workshop project, students can brainstorm from the easiest regression models to more sophisticated spatial models and compare the differences
of inferences from different ideas.
Group members: Richard Groenwald, Mehmut Hatip, Katrina Lewis, Jennifer Soter, Astride Tchkaoua, Sylvester Wieb
LEAN: Understanding a Scatter Gram ( Quality Tools Series 2016)College/University
LEAN: Understanding a Scatter Gram provides information for users to learn key elements of a scatter gram, and to comprehen better its value. After this lesson, learners should be able to:
- Articulate the usage of a scatter diagram
- Explain how to develop a scatter diagram
- Demonstrate the development of a scatter diagram
GIS.INTRODUCTION TO GIS PACKAGES &GEOGRAPHIIC ANALYSISTessaRaju
GIS.HOW GIS WORKS.APPLICATIONS OF GIS.GIS PACKAGES.
TOP 10 GIS SOFTWARE.FATHER OF GIS.USES OF GIS.GEOGRAPHIC ANALYSIS&SPATIAL ANALYSIS.NASA SATELLITE IMAGE OF KERALA FLOOD.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Q-Q Plot:- http://www.transtutors.com/homework-help/statistics/q-q-plot.aspx
LEAN: Understanding a Scatter Gram ( Quality Tools Series 2016)College/University
LEAN: Understanding a Scatter Gram provides information for users to learn key elements of a scatter gram, and to comprehen better its value. After this lesson, learners should be able to:
- Articulate the usage of a scatter diagram
- Explain how to develop a scatter diagram
- Demonstrate the development of a scatter diagram
GIS.INTRODUCTION TO GIS PACKAGES &GEOGRAPHIIC ANALYSISTessaRaju
GIS.HOW GIS WORKS.APPLICATIONS OF GIS.GIS PACKAGES.
TOP 10 GIS SOFTWARE.FATHER OF GIS.USES OF GIS.GEOGRAPHIC ANALYSIS&SPATIAL ANALYSIS.NASA SATELLITE IMAGE OF KERALA FLOOD.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Q-Q Plot:- http://www.transtutors.com/homework-help/statistics/q-q-plot.aspx
Research design decisions and be competent in the process of reliable data co...Stats Statswork
Research Design may be described as the researchers scheme of outlining the flow of his project. It is based on research design, that the researcher goes about gathering data to answer his research question. It enables the researcher to prioritize his work, create better questionnaires and arrive at conclusions with greater clarity. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts.
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Focus on what you learned that made an impression, what may have s.docxkeugene1
Focus on what you learned that made an impression, what may have surprised you, and what you found particularly beneficial and why. Specifically:
What did you find that was really useful, or that challenged your thinking?
What are you still mulling over?
Was there anything that you may take back to your classroom?
Is there anything you would like to have clarified?
ANSWER THE ABOVE QUESTIONS BASED ON THE DOCUMENTS BELOW
Introduction & Goals
This week, we will investigate the distribution of a variable and look at ways to best see the key features of a quantitative variable’s distribution. We will look at visualizations of data, including line plots, frequency tables, stemplots, and histograms. We will hone our ability to describe key features of a distribution from visualizations and use them to compare distributions. We will begin to think about ideas for the Comparative Study by brainstorming in our project groups.
Goals
:
Reinforce the idea that data will vary
Explain what the distribution of variable is
Identify five key features of a distribution: center, spread, shape, clusters & outliers
Identify and create appropriate displays for categorical and quantitative data in one variable, including bar graphs, line plots, frequency tables, and histograms
Analyze distributions using stemplots and histograms
Recognize advantages and limitations of histograms
Begin to explore technology for use in statistics
Begin work on Comparative Study Final Project
DOW #2: How Long Is A Minute?
In week 1, we gathered data for this week’s DoW, addressing the question:
“How long is a minute to an adult?”
This week we'll:
In investigations 1 & 2, you will analyze the data with dot plots, frequency tables, stemplots, and histograms.
In Exercise B2, you will post your initial analysis and interpretation to the discussion board by Wednesday, 10 PM EST and create at least three follow-up posts by Friday, 10 PM EST.
In Exercise D2 & E2, you will post your best histogram to the discussion board by Friday, 10 PM EST. Compare the histograms and choose the one you think best represents the distribution by Sunday, 10 PM EST
Investigation 1: Seeing the Distribution
As we emphasized in Week 1,
data varies
. This point may seem trivial, but it encapsulates one of the most fundamental concepts of statistics:
variability
. Statistical Analysis is really a study of the patterns we find within this variation in the data. The pattern(s) in the variation is called the
distribution
of the variable. Much of statistics focuses on ways to represent and describe the distribution of a variable.
Activities A & B in this investigation focus on representing and describing the distribution.
Activity C introduces Excel as a tool for looking at a distribution.
Inv 1, Activity A: Patterns in the Variation
As we emphasized in Week 1,
data varies
. This point may seem trivial, but it encapsulates one of the most fundamental concepts of statistics:
variability
. Statistical Analy.
Graphs represent data in an engaging manner and make c.docxshericehewat
Graphs represent data in an engaging manner and make comparisons and analyses easier. For example, a graph depicting the number of crimes committed each year over a decade is easier to comprehend visually than reading the numerical values for each year. Before creating a graph, however, it is important to choose one that appropriately represents the data. A histogram, rather than a pie chart, is appropriate for depicting the age groups (e.g., 15–24, 25–34) of murder victims in a city. Histograms are designed to be used with variables that are categorized, but pie charts plot each value. Therefore, it would be easier to read a histogram showing bars for age groups of murder victims than a pie chart in which every single age would have to be plotted. In the past, creating graphs was cumbersome and time consuming, but present-day software programs such as Microsoft Word and Excel provide tutorials that walk you through the process. With knowledge of these software programs, you can create customized charts and figures to represent your research data in visually interesting ways. In this Assignment, you create at least two different graphs in Excel or Word that can be used to illustrate hypothetical data related to six incidents of crime.
· Create at least two different graphs in Excel or Word using the data provided in the table below:
Type of Crime
Offender’s Age
(Years)
Offender’s Gender
Time of the Incident
Theft
22
Male
Early morning
Possession of drugs
21
Female
Late evening
Theft
19
Male
Late evening
Theft
33
Female
Afternoon
Possession of drugs
47
Female
Morning
Possession of drugs
17
Male
Early morning
· Briefly describe the data represented in the graphs and/or charts you created.
· Explain why the graphs and/or charts you created best represent the data compared to other options. Be specific.
Submit the graphs you created in a document that is separate from your written Assignment.
Bachman, R. D., & Schutt, R. K. (2019). The practice of research in criminology and criminal justice (7th ed.). Thousand Oaks, CA: SAGE Publications.
· Chapter 4, “Conceptualization and Measurement” (pp. 86–116)
The Practice of Research in Criminology and Criminal Justice, 7th Edition by Bachman, R. D. & Schutt, R. K. Copyright 2019 by SAGE Publications, Inc. Reprinted by permission of SAGE Publications, Inc via the Copyright Clearance Center.
Bachman, R. D., & Schutt, R. K. (2019). The practice of research in criminology and criminal justice (7th ed.). Thousand Oaks, CA: SAGE Publications.
· Chapter 14, “Analyzing Quantitative Data” (pp. 404–415 and 426–444)
The Practice of Research in Criminology and Criminal Justice, 7th Edition by Bachman, R. D. & Schutt, R. K. Copyright 2019 by SAGE Publications, Inc. Reprinted by permission of SAGE Publications, Inc via the Copyright Clearance Center.
Trochim, W. M. K. (2006). Levels of measurement. In Research methods knowledge base. Retrieved from http://www.socialresearchmethods.net/kb/measlevl.php
Walden Univer ...
t-Test Project Instructions and Rubric Project Overvi.docxmattinsonjanel
t-Test Project Instructions and Rubric
Project Overview
1. Choose a research question:
a) that can be addressed using a t-test
b) for which you can collect data to analyze
2. Devise a plan to collect your data
3. Submit a project plan form to the instructor and obtain approval
4. Once your research question and data collection plan are approved, carry out your research:
a) Collect data
b) Conduct t-test analysis, using guidelines below
c) Write your results in a report, using the outline given below
5. Turn in your written report using the link in Module 9 of the course Blackboard site.
Data Collection Options: The following are suggestions on where you can collect data.
1. Reliable/reputable websites (e.g., sponsored by the census bureau, professional sports leagues, universities, real estate
agencies, car manufacturers, consumer groups, financial institutions, well- known product manufacturers, restaurants/fast
food companies, weather tracking agencies, county/city/state/federal government organizations, etc.)
2. Visit to one or more locations where item(s) you are researching can be found (e.g., stores to write down prices, rivers
to count turtles, car dealers to write down data about cars, etc.)
3. Other resource by permission (if you have an idea, ask your instructor).
Important Note: ERAU and all other universities have strict policies and approval procedures for any research projects that involve
collecting data from human subjects. There is not time in this course for you to go through that approval process. Therefore, your
project in this course cannot involve directly collecting data from human subjects. This includes conducting surveys.
Project Design
There are 3 project design options for the t-test project, listed below. To see components and examples for each type of project
design, consult the t-test Project Examples Word document or the t-test Project Resources PowerPoint file.
A. The 1-sample t-test (Sample size must be at least n = 40.)
B. The matched pairs t-test (Sample size must be at least n = 40.)
C. The 2-sample t-test (independent samples) (Sample size must be at least n = 50. You may split this across your two
samples; for example, you may have two independent samples of size 25, or one of 27 and another of 23, etc. Although your
independent samples are not required to be identical in size, it is better if the sample sizes are similar.)
t-Test Project Instructions and Rubric Page 2 of 7
Revised 6/10/13
Project Plan Form
Download a copy of the project plan Word document, t-Test Project Plan, save a copy for your records, and then complete the form.
Upload the completed form using the link provided in the module assignment item. If the form is not completed satisfactorily, it will be
returned to you for revision.
NOTE:
Your project plan must be approved by the instructor before you may begin your ...
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
9. Researcher tends to become subjectively immersed in the subject matter. Qualitative data is more 'rich', time consuming, and less able to be generalized. Subjective - individuals’ interpretation of events is important ,e.g., uses participant observation, in-depth interviews etc. Data is in the form of words, pictures or objects. Researcher is the data gathering instrument. The design emerges as the study unfolds. Recommended during earlier phases of research projects. Researcher may only know roughly in advance what he/she is looking for. The aim is a complete, detailed description. "All research ultimately has a qualitative grounding" - Donald Campbell Qualitative
10. Researcher tends to remain objectively separated from the subject matter. Quantitative data is more efficient, able to test hypotheses, but may miss contextual detail. Objective – seeks precise measurement & analysis of target concepts, e.g., uses surveys, questionnaires etc. Data is in the form of numbers and statistics. Researcher uses tools, such as questionnaires or equipment to collect numerical data. All aspects of the study are carefully designed before data is collected. Recommended during latter phases of research projects. Researcher knows clearly in advance what he/she is looking for. The aim is to classify features, count them, and construct statistical models in an attempt to explain what is observed. "There's no such thing as qualitative data. Everything is either 1 or 0" - Fred Kerlinger Quantitative
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