Top 5 tips on how to learn statistics more effectivelyStat Analytica
In this infographic, you will go through how to learn statistics more effectively than ever before. Have a look at this infographic to start learning statistics
This document discusses 7 basic quality tools:
1. Flow charts map out sequential or parallel processes to understand relationships.
2. Histograms illustrate the frequency and distribution of two variables using columns.
3. Cause and effect diagrams identify organizational problem causes through team brainstorming.
4. Check sheets gather and organize data that can then be analyzed in software.
5. Scatter diagrams present the relationship between two variables on a Cartesian plane.
6. Control charts monitor process performance to identify stability, predictability, and special conditions.
7. Pareto charts identify priority issues by recording the number of occurrences.
This document provides an overview of predictive analytics. It explains that predictive analytics uses past data, statistics, and assumptions to predict the future. Good quality data is important for predictive analytics, and determining what data is needed is critical. Predictive analytics finds correlations between independent and dependent variables, and regression analysis is commonly used to find the best predictive model. Models are based on assumptions like the future resembling the past, but assumptions can become invalid over time. Managers should understand the assumptions, data sources, and conditions that could impact a model's validity when using predictive analytics models.
Data Types with Matt Hansen at StatStuffMatt Hansen
This document discusses the differences between continuous and discrete data types. Continuous data is measured on a continuum and is virtually infinite in scale or divisibility, with examples like dollars, time, and distance. Discrete data is measured by counts or classifications with limited scale and divisibility, with examples like yes/no, colors, and names. The document notes that while percentages are numeric, they actually represent discrete proportions. It also discusses count and classification data as two types of discrete data and provides examples of how each is used. Finally, it prompts the reader to analyze metrics from their own organization to determine if they are continuous or discrete and how they could potentially be measured differently.
Analysis of "A Predictive Analytics Primer" by Tom DavenportEt Hish
Predictive analytics uses statistical techniques like predictive modelling, machine learning, and data mining to analyze past and present data to predict future events. The quality of predictive analytics depends on three factors: the quality of past data used, the appropriate use of statistical techniques like various types of regression analysis, and the validity of assumptions made in the analysis. Issues like poor historical data, assumptions not holding true over time, and data not being streamlined across sources can create barriers to accurate prediction.
This document discusses different types of statistics used in research. Descriptive statistics are used to organize and summarize data using tables, graphs, and measures. Inferential statistics allow inferences about populations based on samples through techniques like surveys and polls. The key difference is that descriptive statistics describe samples while inferential statistics allow conclusions about populations beyond the current data.
Types of statistical analysis infographicIntellspot
This document outlines 7 main types of statistical analysis:
1. Descriptive statistics describe basic features of data in a summarized way but cannot make conclusions about broader populations.
2. Inferential statistics allow conclusions and generalizations about larger populations based on samples using mathematical estimations.
3. Predictive analytics use statistical algorithms and machine learning to define the likelihood of future results and trends based on historical data, though cannot predict the future with certainty.
4. Causal analysis examines reasons for outcomes by answering why things occurred.
10 everyday reasons why statistics are importantJason Edington
Statistics is used in many fields to analyze data and make predictions. It helps separate signals from noise. Examples given where statistics is used include stock markets, quality assurance, retail, insurance, political campaigns, genetic engineering, medical studies, weather forecasting, and emergency preparedness. The document emphasizes that an important reason to study statistics is to be better consumers of information and understand when data may be manipulated.
Top 5 tips on how to learn statistics more effectivelyStat Analytica
In this infographic, you will go through how to learn statistics more effectively than ever before. Have a look at this infographic to start learning statistics
This document discusses 7 basic quality tools:
1. Flow charts map out sequential or parallel processes to understand relationships.
2. Histograms illustrate the frequency and distribution of two variables using columns.
3. Cause and effect diagrams identify organizational problem causes through team brainstorming.
4. Check sheets gather and organize data that can then be analyzed in software.
5. Scatter diagrams present the relationship between two variables on a Cartesian plane.
6. Control charts monitor process performance to identify stability, predictability, and special conditions.
7. Pareto charts identify priority issues by recording the number of occurrences.
This document provides an overview of predictive analytics. It explains that predictive analytics uses past data, statistics, and assumptions to predict the future. Good quality data is important for predictive analytics, and determining what data is needed is critical. Predictive analytics finds correlations between independent and dependent variables, and regression analysis is commonly used to find the best predictive model. Models are based on assumptions like the future resembling the past, but assumptions can become invalid over time. Managers should understand the assumptions, data sources, and conditions that could impact a model's validity when using predictive analytics models.
Data Types with Matt Hansen at StatStuffMatt Hansen
This document discusses the differences between continuous and discrete data types. Continuous data is measured on a continuum and is virtually infinite in scale or divisibility, with examples like dollars, time, and distance. Discrete data is measured by counts or classifications with limited scale and divisibility, with examples like yes/no, colors, and names. The document notes that while percentages are numeric, they actually represent discrete proportions. It also discusses count and classification data as two types of discrete data and provides examples of how each is used. Finally, it prompts the reader to analyze metrics from their own organization to determine if they are continuous or discrete and how they could potentially be measured differently.
Analysis of "A Predictive Analytics Primer" by Tom DavenportEt Hish
Predictive analytics uses statistical techniques like predictive modelling, machine learning, and data mining to analyze past and present data to predict future events. The quality of predictive analytics depends on three factors: the quality of past data used, the appropriate use of statistical techniques like various types of regression analysis, and the validity of assumptions made in the analysis. Issues like poor historical data, assumptions not holding true over time, and data not being streamlined across sources can create barriers to accurate prediction.
This document discusses different types of statistics used in research. Descriptive statistics are used to organize and summarize data using tables, graphs, and measures. Inferential statistics allow inferences about populations based on samples through techniques like surveys and polls. The key difference is that descriptive statistics describe samples while inferential statistics allow conclusions about populations beyond the current data.
Types of statistical analysis infographicIntellspot
This document outlines 7 main types of statistical analysis:
1. Descriptive statistics describe basic features of data in a summarized way but cannot make conclusions about broader populations.
2. Inferential statistics allow conclusions and generalizations about larger populations based on samples using mathematical estimations.
3. Predictive analytics use statistical algorithms and machine learning to define the likelihood of future results and trends based on historical data, though cannot predict the future with certainty.
4. Causal analysis examines reasons for outcomes by answering why things occurred.
10 everyday reasons why statistics are importantJason Edington
Statistics is used in many fields to analyze data and make predictions. It helps separate signals from noise. Examples given where statistics is used include stock markets, quality assurance, retail, insurance, political campaigns, genetic engineering, medical studies, weather forecasting, and emergency preparedness. The document emphasizes that an important reason to study statistics is to be better consumers of information and understand when data may be manipulated.
Data science has become essential for businesses to analyze large amounts of data. While machine learning seems promising, there are no shortcuts - data exploration is needed to improve model accuracy. The key steps of data exploration are identifying variables, univariate and bivariate analysis, and treating missing values and outliers to clean the data before building predictive models. Understanding the data generation process is important for leaders to evaluate the quality of analytics.
This document discusses 7 quality tools used in continuous improvement workshops: affinity diagram, relations diagram, tree diagram, matrix diagram, matrix data analysis, arrow diagram, and process decision program chart. It provides a brief overview of each tool, including what it is used for and typical situations where each tool would be applicable.
The document discusses the importance of data quality, proper use of statistics, and correct interpretation of results in statistical analysis. It provides a 3 step approach: 1) Ensuring high quality data by addressing issues like missing values and outliers. 2) Appropriate use of statistical techniques after defining the variables and objectives clearly. Considering issues like correlation, normality, and model assumptions. 3) Careful interpretation of results while preserving the multidimensional nature of phenomena and considering partial correlations between variables. It emphasizes the need for collaboration between data miners, statisticians and domain experts for successful knowledge discovery.
This document outlines the objectives and requirements for a research project being conducted by the UOEC club. Small teams will choose a topic to research, gather quantitative data, use STATA to run regressions, and answer a provided question on how independent variables affect the dependent variable. Teams are then free to further analyze the data. Each team must have an executive member experienced with econometrics. Teams will present their findings to the club. The document provides a list of objectives for participants and resources for gathering data.
Different Sources of Data with Matt Hansen at StatStuffMatt Hansen
This document discusses different sources of data for statistical analysis, including source systems, system reports, and manual observations. It notes that source systems are the ideal primary source because they provide consistent, comprehensive, and reliable data, while system reports are also good sources that are fast but may lack detail. Manual observations are less reliable due to small sample sizes and inconsistencies. The document recommends considering the tradeoff between data accuracy and the time required to obtain the data from each potential source.
This document introduces tools for continuous improvement. It discusses two components of continuous improvement processes in companies: philosophy and graphical techniques. The philosophy components emphasize quality improvement through eliminating problems, empowering workers, and encouraging collaboration. The document then describes seven classic quality tools: Ishikawa diagrams, Pareto charts, check sheets, control charts, flowcharts, histograms, and scatter diagrams. It provides a brief explanation of when each tool is used.
Statistical Significance is important terminology in statistical inference namely Hypothesis testing. Statistical Significance indicates that obtained outcome is occurred solely due to some cause and has not occurred because of any randomness or concurrence Copy the link given below and paste it in new browser window to get more information on Statistical Significance:- www.transtutors.com/homework-help/statistics/statistical-significance.aspx
This document discusses bunking lectures and uses a Pareto chart to analyze the key factors responsible. It introduces the Pareto principle, which states that a small number of causes are responsible for the majority of problems. A survey found that 80% of reasons for bunking lectures were to attend coaching classes, dislike a subject, peer pressure, course content available elsewhere, and issues with the teacher. The document defines a Pareto chart as a type of bar graph used to visualize important situations or causes. Analyzing the major factors through a Pareto chart can help address the biggest problems and improve outcomes.
Top 10 Uses Of Statistics In Our Day to Day Life Stat Analytica
Don't you know the uses of statistics is our daily life? If yes then check out this presentation you will learn a lot more about the use of statistics in our daily life.
Important Terminologies In Statistical InferenceZoha Qureshi
This document provides an overview of statistics and their applications in business decision making. It discusses why statistics are important for managers to make informed decisions using all relevant data. Statistical methods allow companies to collect, analyze, and interpret market data to aid decision making. The document also covers key statistical concepts like descriptive and inferential statistics, data types and measurements, sources of data, and common statistical terms. It provides examples of how companies like American Express have benefited from applying statistical analysis to customer feedback data.
This document discusses misuses and limitations of statistics. It provides examples of how statistics can be misleading when organizations selectively publish studies, questions are worded to influence responses, or samples are not representative of the overall population. Limitations of statistics include that they deal with aggregates rather than individuals, quantitative rather than qualitative data, and laws that are true on average rather than exactly. Statistics also cannot prove causation and are limited by the quality of data collection and analysis.
This document provides an introduction to statistics, including definitions, characteristics, importance, and key concepts. It defines statistics as a discipline involving procedures and techniques used to collect, process, and analyze numerical data to make inferences and decisions despite uncertainty. Statistics deals with aggregate data rather than isolated figures, considers variability, and is useful for summarizing large datasets, designing experiments, and aiding decision-making in many fields including business, government, and science. The document also introduces important statistical concepts such as populations, samples, observations, and quantitative versus qualitative variables.
This document provides an introduction to statistics. It defines statistics as the science of collecting, analyzing, interpreting and presenting numerical data. Descriptive statistics are used to describe or make conclusions about a group using data from that group, while inferential statistics are used to make conclusions about a larger population based on a sample of data. The document discusses how statistics are used in business for tasks like market research, reducing workplace stress through surveys, making financial decisions, and measuring economic indicators. It also notes how technology has impacted data collection and analysis.
Application of probability in daily life and in civil engineeringEngr Habib ur Rehman
This document provides examples of how statistics are used in various fields including civil engineering, real life situations, and the sciences. It discusses how statistics are applied in areas like weather forecasting, emergency preparedness, psychology, the stock market, predicting disease, education, genetics, political campaigns, quality testing, banking, business, insurance, consumer goods, management, medical studies, large companies, and the natural and social sciences. It also provides specific examples of applying statistics in civil engineering domains such as sanitary engineering, traffic engineering, surveying, coastal/port engineering, geotechnical engineering, hydrology, environmental engineering, earthquake engineering, and structural engineering.
Statistics are used widely in many areas of real life including weather forecasting, emergency preparedness, disease prediction, education, genetics, politics, quality testing, business, banking, insurance, government administration, astronomy, and the natural and social sciences. Some key examples provided include how weather models use statistics to predict future weather, emergency teams rely on statistics to prepare for danger, disease rates are calculated using statistics, teachers evaluate students' performance statistically, and businesses use statistics to plan production and marketing.
The document discusses several problem solving tools:
- Cause-and-effect diagrams (fishbone diagrams) are used to categorize potential causes of problems and identify root causes. They are used when studying why a process is having issues or not achieving desired results.
- Flowcharts visualize processes and identify responsibilities to locate flaws.
- Pareto diagrams arrange information to establish improvement priorities by highlighting the most important issues. They display relative importance and direct efforts towards biggest opportunities.
- Histograms graphically summarize data distributions by segmenting ranges into equal bins. They can analyze how a process data is distributed.
The document summarizes topics to be covered at the 4th meeting of the Boston University Undergraduate Economics Association, including quantitative analysis concepts, statistical modeling theory, and popular statistical software programs. Main concepts include what statistics is, statistical modeling, and econometrics. Statistical theory outlines parametric modeling and linear models. Popular statistical software programs that will be discussed are R, JMP, STATA, and SAS.
TMGT 361Assignment VII A InstructionsLectureEssayControl Ch.docxherthalearmont
This document provides instructions for Assignment VII A in the TMGT 361 course. It discusses control charts and quality management. It instructs students to construct and analyze control charts using provided or work data, including X-bar and range charts, p charts, and c charts. It emphasizes doing calculations and chart construction by hand rather than solely using software. It also provides guidance on interpretation, discussion, and showing work.
It turns out that strategy is one of those words that we inevitably define in one way yet often use in another way. How do you go about analyzing how well your organization is positioned to achieve its intended objective? This is a question that has been asked for many years, and there are many different answers. Some approaches look at internal factors, others look at external ones, some combine these perspectives, and others look for congruence between various aspects of the organization being studied. Ultimately, the issue comes down to which factors to study.
Perception Analyzer allows moderators to capture instant feedback from consumers viewing commercials, concepts or other materials using a dial system. Respondents provide continuous feedback that is displayed in real-time, allowing researchers to quickly understand reactions and gauge what elements are positively or negatively received. After viewing, respondents discuss their reactions in an in-depth moderated discussion to understand the "whys" behind them. The process provides rich qualitative insights that can be used to refine new concepts or messages and ensure they will resonate with target audiences.
Intra Cranial Pressure ( Icp ) Measurements Are Taken Via...Michelle Love
Invasive procedures to measure intra cranial pressure (ICP) carry health risks, so researchers are testing a non-invasive technique using a cerebral and cochlear fluid pressure (CCFP) analyzer. The CCFP measures ICP fluctuations in perilymphatic fluid. A study is recruiting healthy male volunteers to have ICP measured with the CCFP during weight lifting, as exertion is expected to cause transient ICP increases detectable by the device. The CCFP utilizes tympanic membrane displacement to non-invasively measure ICP.
Data science has become essential for businesses to analyze large amounts of data. While machine learning seems promising, there are no shortcuts - data exploration is needed to improve model accuracy. The key steps of data exploration are identifying variables, univariate and bivariate analysis, and treating missing values and outliers to clean the data before building predictive models. Understanding the data generation process is important for leaders to evaluate the quality of analytics.
This document discusses 7 quality tools used in continuous improvement workshops: affinity diagram, relations diagram, tree diagram, matrix diagram, matrix data analysis, arrow diagram, and process decision program chart. It provides a brief overview of each tool, including what it is used for and typical situations where each tool would be applicable.
The document discusses the importance of data quality, proper use of statistics, and correct interpretation of results in statistical analysis. It provides a 3 step approach: 1) Ensuring high quality data by addressing issues like missing values and outliers. 2) Appropriate use of statistical techniques after defining the variables and objectives clearly. Considering issues like correlation, normality, and model assumptions. 3) Careful interpretation of results while preserving the multidimensional nature of phenomena and considering partial correlations between variables. It emphasizes the need for collaboration between data miners, statisticians and domain experts for successful knowledge discovery.
This document outlines the objectives and requirements for a research project being conducted by the UOEC club. Small teams will choose a topic to research, gather quantitative data, use STATA to run regressions, and answer a provided question on how independent variables affect the dependent variable. Teams are then free to further analyze the data. Each team must have an executive member experienced with econometrics. Teams will present their findings to the club. The document provides a list of objectives for participants and resources for gathering data.
Different Sources of Data with Matt Hansen at StatStuffMatt Hansen
This document discusses different sources of data for statistical analysis, including source systems, system reports, and manual observations. It notes that source systems are the ideal primary source because they provide consistent, comprehensive, and reliable data, while system reports are also good sources that are fast but may lack detail. Manual observations are less reliable due to small sample sizes and inconsistencies. The document recommends considering the tradeoff between data accuracy and the time required to obtain the data from each potential source.
This document introduces tools for continuous improvement. It discusses two components of continuous improvement processes in companies: philosophy and graphical techniques. The philosophy components emphasize quality improvement through eliminating problems, empowering workers, and encouraging collaboration. The document then describes seven classic quality tools: Ishikawa diagrams, Pareto charts, check sheets, control charts, flowcharts, histograms, and scatter diagrams. It provides a brief explanation of when each tool is used.
Statistical Significance is important terminology in statistical inference namely Hypothesis testing. Statistical Significance indicates that obtained outcome is occurred solely due to some cause and has not occurred because of any randomness or concurrence Copy the link given below and paste it in new browser window to get more information on Statistical Significance:- www.transtutors.com/homework-help/statistics/statistical-significance.aspx
This document discusses bunking lectures and uses a Pareto chart to analyze the key factors responsible. It introduces the Pareto principle, which states that a small number of causes are responsible for the majority of problems. A survey found that 80% of reasons for bunking lectures were to attend coaching classes, dislike a subject, peer pressure, course content available elsewhere, and issues with the teacher. The document defines a Pareto chart as a type of bar graph used to visualize important situations or causes. Analyzing the major factors through a Pareto chart can help address the biggest problems and improve outcomes.
Top 10 Uses Of Statistics In Our Day to Day Life Stat Analytica
Don't you know the uses of statistics is our daily life? If yes then check out this presentation you will learn a lot more about the use of statistics in our daily life.
Important Terminologies In Statistical InferenceZoha Qureshi
This document provides an overview of statistics and their applications in business decision making. It discusses why statistics are important for managers to make informed decisions using all relevant data. Statistical methods allow companies to collect, analyze, and interpret market data to aid decision making. The document also covers key statistical concepts like descriptive and inferential statistics, data types and measurements, sources of data, and common statistical terms. It provides examples of how companies like American Express have benefited from applying statistical analysis to customer feedback data.
This document discusses misuses and limitations of statistics. It provides examples of how statistics can be misleading when organizations selectively publish studies, questions are worded to influence responses, or samples are not representative of the overall population. Limitations of statistics include that they deal with aggregates rather than individuals, quantitative rather than qualitative data, and laws that are true on average rather than exactly. Statistics also cannot prove causation and are limited by the quality of data collection and analysis.
This document provides an introduction to statistics, including definitions, characteristics, importance, and key concepts. It defines statistics as a discipline involving procedures and techniques used to collect, process, and analyze numerical data to make inferences and decisions despite uncertainty. Statistics deals with aggregate data rather than isolated figures, considers variability, and is useful for summarizing large datasets, designing experiments, and aiding decision-making in many fields including business, government, and science. The document also introduces important statistical concepts such as populations, samples, observations, and quantitative versus qualitative variables.
This document provides an introduction to statistics. It defines statistics as the science of collecting, analyzing, interpreting and presenting numerical data. Descriptive statistics are used to describe or make conclusions about a group using data from that group, while inferential statistics are used to make conclusions about a larger population based on a sample of data. The document discusses how statistics are used in business for tasks like market research, reducing workplace stress through surveys, making financial decisions, and measuring economic indicators. It also notes how technology has impacted data collection and analysis.
Application of probability in daily life and in civil engineeringEngr Habib ur Rehman
This document provides examples of how statistics are used in various fields including civil engineering, real life situations, and the sciences. It discusses how statistics are applied in areas like weather forecasting, emergency preparedness, psychology, the stock market, predicting disease, education, genetics, political campaigns, quality testing, banking, business, insurance, consumer goods, management, medical studies, large companies, and the natural and social sciences. It also provides specific examples of applying statistics in civil engineering domains such as sanitary engineering, traffic engineering, surveying, coastal/port engineering, geotechnical engineering, hydrology, environmental engineering, earthquake engineering, and structural engineering.
Statistics are used widely in many areas of real life including weather forecasting, emergency preparedness, disease prediction, education, genetics, politics, quality testing, business, banking, insurance, government administration, astronomy, and the natural and social sciences. Some key examples provided include how weather models use statistics to predict future weather, emergency teams rely on statistics to prepare for danger, disease rates are calculated using statistics, teachers evaluate students' performance statistically, and businesses use statistics to plan production and marketing.
The document discusses several problem solving tools:
- Cause-and-effect diagrams (fishbone diagrams) are used to categorize potential causes of problems and identify root causes. They are used when studying why a process is having issues or not achieving desired results.
- Flowcharts visualize processes and identify responsibilities to locate flaws.
- Pareto diagrams arrange information to establish improvement priorities by highlighting the most important issues. They display relative importance and direct efforts towards biggest opportunities.
- Histograms graphically summarize data distributions by segmenting ranges into equal bins. They can analyze how a process data is distributed.
The document summarizes topics to be covered at the 4th meeting of the Boston University Undergraduate Economics Association, including quantitative analysis concepts, statistical modeling theory, and popular statistical software programs. Main concepts include what statistics is, statistical modeling, and econometrics. Statistical theory outlines parametric modeling and linear models. Popular statistical software programs that will be discussed are R, JMP, STATA, and SAS.
TMGT 361Assignment VII A InstructionsLectureEssayControl Ch.docxherthalearmont
This document provides instructions for Assignment VII A in the TMGT 361 course. It discusses control charts and quality management. It instructs students to construct and analyze control charts using provided or work data, including X-bar and range charts, p charts, and c charts. It emphasizes doing calculations and chart construction by hand rather than solely using software. It also provides guidance on interpretation, discussion, and showing work.
It turns out that strategy is one of those words that we inevitably define in one way yet often use in another way. How do you go about analyzing how well your organization is positioned to achieve its intended objective? This is a question that has been asked for many years, and there are many different answers. Some approaches look at internal factors, others look at external ones, some combine these perspectives, and others look for congruence between various aspects of the organization being studied. Ultimately, the issue comes down to which factors to study.
Perception Analyzer allows moderators to capture instant feedback from consumers viewing commercials, concepts or other materials using a dial system. Respondents provide continuous feedback that is displayed in real-time, allowing researchers to quickly understand reactions and gauge what elements are positively or negatively received. After viewing, respondents discuss their reactions in an in-depth moderated discussion to understand the "whys" behind them. The process provides rich qualitative insights that can be used to refine new concepts or messages and ensure they will resonate with target audiences.
Intra Cranial Pressure ( Icp ) Measurements Are Taken Via...Michelle Love
Invasive procedures to measure intra cranial pressure (ICP) carry health risks, so researchers are testing a non-invasive technique using a cerebral and cochlear fluid pressure (CCFP) analyzer. The CCFP measures ICP fluctuations in perilymphatic fluid. A study is recruiting healthy male volunteers to have ICP measured with the CCFP during weight lifting, as exertion is expected to cause transient ICP increases detectable by the device. The CCFP utilizes tympanic membrane displacement to non-invasively measure ICP.
ANOVA is a hypothesis testing technique used to compare the equali.docxjustine1simpson78276
ANOVA is a hypothesis testing technique used to compare the equality of means for two or more groups; for example, it can be used to test that the mean number of computer chips produced by a company on each of the day, evening, and night shifts is the same. Give an example of an application of ANOVA in an industrial, operations, or manufacturing setting that is different from the examples provided in the overview. Discuss and share this information with your classmates.
In responding to your peers, select responses that use an ANOVA application that is different from your own. Are the results of the ANOVA application statistically significant? Why are the results significant or not significant? Explain your reasoning. Consider how ANOVA could be applied to the final project case study.
Support your initial posts and response posts with scholarly sources cited in APA style.
https://statistics4beginners.wordpress.com/2015/02/18/how-to-calculate-anova-in-excel-2013/
PLEASE GIVE A 1-2 PARAGRAPH RESPONSE TO THE FOLLOWING:
1.
In this module, our goal is to learn the statistical process of comparing several population means through a procedure called "analysis of variance", or ANOVA. ANOVA uses the variance from the mean of 2 or more sample populations to see if there is a statistically significant difference between them (Sharpe, DeVeaux, Velleman, 2016). We've learned that this is a valuable tool in all sorts of areas of study, including automotive, chemical, and medical industries.
There are many practical examples of ANOVA throughout business. As previously mentioned, the medical field can benefit from the use of this statistics tool. For example, a drug company may be interested in the results of clinical trials for a few new drugs they plan to release. Medicine A, B, and C are all now in the clinical testing phase, so the instances in which each cures a specific ailment can be summed up using ANOVA. Each of the individual drugs, through the course of multiple trials, will have a number of "cured" patients. The following is an example of what the results may be, in table format:
A B C
Trial 1 4 9 2
2 5 8 7
3 7 1 6
4 6 1 5
5 6 4 9
Using ANOVA to evaluate the variance from the mean for each trial, the ultimate goal would be to compare each trial to one another. By comparing the variance, we can say, with statistical confidence, that one medicine may be more effect.
Towards a Systemic Design Toolkit: A Practical Workshop - #RSD5 Workshop, Tor...Koen Peters
Namahn (BE), a human-centred design agency, and shiftN (BE), a futures and systems thinking studio from Brussels, are developing a Systemic Design Toolkit combining the methodologies of both practices. The toolkit is currently piloted with the EU Policy Lab of the European Commission’s Joint Research Centre. The toolkit is structured as a suite of discrete thinking-and-doing instruments, to be applied selectively, sequentially and iteratively. The purpose of this toolkit is to enable co-analyses of complex challenges and co-creation of systemic solutions mode with users and other stakeholders This workshop aims to exchange insights between participants and facilitators in a hands-on, case-based format.
Workshop presenters are: Philippe Vandenbroeck, Kristel Van Ael, Clementina Gentile (@clementina_g) and Koen Peters (@2pk_koen)
Course Project Part 1—Gap Analysis Plan and Visio DraftThe.docxbuffydtesurina
Course Project Part 1—Gap Analysis Plan and Visio Draft
The process of comparing the current state of a system or workflow and to the desired, future state of the workflow, and then formulating preliminary ideas about how to move from the current to the future is known as a gap analysis. There are several different ways to collect information about an identified problem and potential solution, including observations, interviews, oral surveys, questionnaires, and existing data, tests.
This week you will begin crafting a Gap Analysis Plan to acquire information about a specific workflow issue in your organization or in one with which you are familiar.
Note:
Your workflow issue MUST be related to the implementation or optimization of electronic health records (EHRs) in the organization in order to connect to meaningful use and the HITECH Act. If the organization currently uses an EHR system, your workflow issue should be related to an inefficiency or problem with how the EHR system is used. If the organization does not yet have an EHR system, your workflow issue should be related to an inefficiency or problem that could be solved by implementing an EHR system.
To prepare for Part 1 of the Course Project:
Identify the workflow issue you would like to explore throughout your Course Project. This should be a specific task about which you have sufficient content knowledge and access to describe and analyze in detail. Your workflow issue MUST be related to EHRs and be significantly tied to one or more meaningful use objectives. The workflow issue may be related to either the optimization of an existing EHR system or the implementation of an entirely new EHR system.
Establish your goals for conducting your gap analysis. These goals describe why you are conducting the gap analysis, the workflow issue you are pinpointing, and what you hope to accomplish in examining this issue.
Determine how you will collect data about the current workflow you are exploring for the Course Project. For example, you may choose to observe people as they move through the workflow, ask people about their experiences, confer with managers and leaders about their involvement, refer to your organization’s training manual or other informative documents, etc. Be sure to use more than one data-collection method and observe or consult with more than one person to ensure that the information you gather is balanced.
Note:
If you are not currently working in a health care organization, you must locate an organization in your area and obtain informal permission to speak with and observe individuals. Consult your Instructor if you have questions on how to proceed.
Participate in the Week 4 Discussion and review the comments made by your colleagues and the Instructor on your preliminary workflow issue. Revise your workflow issue and data-collection methods as appropriate.
After you have revised your workflow issue, begin composing your Gap Analysis Plan paper using.
After Action Report: a structured support to the practice of continuous impro...Learning Everywhere
This paper is about Lean and Continuous Improvement principles and tools, focusing in the After Action Report as the way to practice the “check” and “adjust” at PDCA cycle.
It presents the After Action Report - a Lean and Continuous Improvement tool, as a tool for those who are interested in supporting continuous improvement, practicing “check” and “adjust”, individually and in organizations and teams. This tool can be applied everywhere, in every projects or daily situations, and every time (as it is “continuous”). The main goal is to clarify that, when we have a structure, it becomes easier to support the practice of continuous improvement.
The document provides instructions for a course project involving a gap analysis plan and Visio draft. Students are asked to:
1) Identify a workflow issue related to electronic health records (EHRs) and meaningful use objectives.
2) Establish goals for the gap analysis and methods for collecting current workflow data.
3) Write a 3-4 page gap analysis plan and create a draft current-state workflow diagram in Visio.
Application 3 Health Information Technology Project [Major Assess.docxrossskuddershamus
Application 3: Health Information Technology Project [Major Assessment 5]
In previous Discussions and Applications, you have explored various aspects of health information technology systems: the historic development of HIT, how data flows across HIT systems, and standards and interoperability requirements including specific terminologies used in your practice setting.
In this Application Assignment, you will have the opportunity to further develop your analysis skills by closely examining the implementation of a health information technology system. As a DNP-prepared nurse, you may find yourself in the position of leading a HIT project team; to be an effective leader and move health information technology projects forward in your organization, you must be able to logically and critically analyze the many aspects and challenges of implementing such a system and then present your insights in a succinct and professional manner. This exercise provides an opportunity to hone those skills.
Carefully review the project requirements below and plan your time accordingly.
To prepare:
Investigate a health information technology system or health information technology application in your area of interest. The health information technology system/application may be in any setting where health care information is developed or managed. You may choose your system or application from any organization or virtual environment.
Examples of health information technology systems or health information technology applications that are acceptable include but are not limited to:
· Consumer health applications
· Clinical information systems
· Electronic medical record (EMR) systems in hospitals or provider offices
· Home health care applications
· School health applications
· Patient portal/personal health record
· Public health information systems
· Telehealth (i.e., from facility to home)
· Simulation laboratories
· Health care informatics research and development centers
NOTE: In your submitted report, do not share proprietary information, personal names, or organization names without permission.
Application 3: Health Information Technology Project [Major Assessment 5]
To complete:
Your deliverable is a 12- to-15-page scholarly report, not counting the title page, abstract, or references. A successful report should leave the reader with confidence in understanding the answers to all the questions listed below. Graphics may be used to illustrate key points.
Organization Information
Briefly describe the health information technology system/application and the organization type (hospital, clinic, public health agency, health care software company, government health information website, private virtual health information site, etc.)
Is the health information technology system/application clinical, administrative, educational, or research related?
What were the key reasons for the development of this health information technology system/application, i.e., what mad.
The document provides instructions to critique an example report or data visualization by describing its purpose, noting what is liked about it, what makes it work well, what is disliked, and how it could be improved. It then provides an example of a "dashboard" reporting various quality metrics for nursing floors that allows seeing a wide range of statistics quickly and tracking progress over time. The only suggested improvement is reducing the header size to see more information on screen.
This document discusses Deming quality management and provides resources on the topic. It summarizes Deming's 14-point philosophy for quality management, which calls for constancy of purpose, cooperation over competition, continuous process improvement, and eliminating fear in the workplace. The document then lists and briefly describes six common quality management tools: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Finally, it lists additional related topics for further reading.
STAT 2103 Project 4 Performing a Multiple Linear Regress.docxdessiechisomjj4
STAT 2103 Project 4:
Performing a Multiple Linear Regression Analysis
Goal: Use the data set provided and the statistical methods learned in class to carry out an
applied multiple linear regression analysis.
Data: The data set for this project has been posted to Blackboard. The observational units in the
sample are 146 countries. The response variable (Y) is a “HAPPY”, an index of each country’s
overall happiness. Also included are 10 predictor variables (X’s), such as GDP, life expectancy,
health care expenditure, and population density. The “Description” tab explains each variable.
Method: You can complete the regression using StatCrunch (recommended) or Excel:
StatCrunch: On MyStatLab, select “StatCrunch”, then “StatCrunch website”, then “Type
or paste data into a blank data table”. Then use the “Stat” menu, “Regression”, and
“Multiple Linear”. Choose the correct variables and specifications.
Excel: Download “Analysis ToolPak” add-in (File – Options – Add-Ins – Manage). Then
“Data Analysis”, select “Regression”. Choose the correct input and specifications.
Assignment: Perform a multiple linear regression analysis. This includes:
List Variables: Select and list 4 predictor variables that you think may be related to
happiness.
Explore Variables: Include a scatterplot of the response variable “HAPPY” on the y-axis
and one of your predictor variables on the x-axis. Describe their relationship/correlation.
Write Model: Construct and write out a multiple linear regression model with your
selected variables.
Analyze Model: Use the statistical output to identify which predictor variables are
significantly important and how much of the variability in the response variable is
explained (the r
2
value).
Finalize Model: Rerun the regression model using only the significant predictor variables.
(If none were significant the first time, use the two variables with the lowest p-values.)
Learn from Model: Choose one variable from this finalized model and interpret its
coefficient. Also, why do you think that the r
2
is so high or so low?
Predict with Model: Select a country from the sample. Use the values of that country’s
predictor variables and the final regression model to estimate that country’s HAPPY
index. Find how much the model overestimated or underestimated the true value.
Details: Due date is in class on Thursday, December 4. The previous class on Tuesday,
December 2 will be partially spent as an in-class work day for the project, so it is recommended
that you bring your laptop to class that day if you have questions.
DescriptionVARNAMEVariable DescriptionCOUNTRY country nameHAPPY Forbes happiness indexCOMP health success indexHLTHEXP per capita health expenditureEDUC average years of educationDALE life expectancyGINI index of income distribution – higher is worse (less equal)POPDEN population density in people per square kilometerPUBTHE percent of.
Certified Specialist Business Intelligence (.docxdurantheseldine
Certified Specialist Business
Intelligence (CSBI) Reflection
Part 5 of 6
CSBI Course 5: Business Intelligence and Analytical and Quantitative Skills
● Thinking about the Basics
● The Basic Elements of Experimental Design
● Sampling
● Common Mistakes in Analysis
● Opportunities and Problems to Solve
● The Low Severity Level ED (SL5P) Case Setup as an Example of BI Work
● Meaningful Analytic Structures
Analysis and Statistics
A key aspect of the work of the BI/Analytics consultant is analysis. Analysis can be defined as
how the data is turned into information. Information is the outcome when the data is analyzed
correctly.
Rigorous analysis is having the best chance of creating the sharpest picture of what the data
might reveal and is the product of proper application of statistics and experimental design.
Statistics encompasses a complex and detailed series of disciplines. Statistical concepts are
foundational to all descriptive, predictive and prescriptive analytic applications. However, the
application of simple descriptive statistical calculations yields a great deal of usable information
for transformational decision-making. The value of the information is amplified when using these
same simple statistics within the context of a well-designed experiment.
This module is not designed to teach one statistic. It is designed to place statistical work within
the appropriate context so that it can be leveraged most effectively in driving organizational
performance..
An important review of the basic knowledge for work with descriptive and inferential statistics.
The Basic Elements of Experimental Design
Analytic tools also can provide an enhanced ability to conduct experiments. More than just
allowing analysis of output of activities or processes, experiments can be performed on
processes and the output of processes. Experimenting on processes is a movement beyond
the traditional r.
This document discusses the four main types of analytics: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics answers the question "What happened?" by summarizing past data. Diagnostic analytics answers "Why did this happen?" by analyzing data to determine causes of trends. Predictive analytics answers "What might happen in the future?" by using statistics and modeling to predict outcomes. Prescriptive analytics answers "What should we do next?" by recommending actions based on predictive analytics. The document provides examples of each type.
This document provides an overview of various quality tools that can be used to improve processes and solve problems. It discusses both traditional "old" tools like cause-and-effect diagrams, histograms, Pareto charts, and control charts, as well as newer tools introduced in the last 10-15 years. The document categorizes and describes the purpose and use of each tool, including how they can help with cause analysis, decision-making, process analysis, data collection, idea creation, and project planning. Key tools covered include fishbone diagrams, check sheets, scatter plots, affinity diagrams, Gantt charts, and PDCA/PDSA cycles.
Between Black and White Population1. Comparing annual percent .docxjasoninnes20
Between Black and White Population
1. Comparing annual percent of Medicare enrollees having at least one ambulatory visit between B and W
2. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having hemoglobin A1c between B and W
3. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having eye examination between B and W
4. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having
Students will develop an analysis report, in five main sections, including introduction, research method (research questions/objective, data set, research method, and analysis), results, conclusion and health policy recommendations. This is a 5-6 page individual project report.
Here are the main steps for this assignment.
Step 1: Students require to submit the topic using topic selection discussion forum by the end of week 1 and wait for instructor approval.
Step 2: Develop the research question and
Step 3: Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction.
The Report Structure:
Start with the
1.Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
· Title, this is the approved topic by your instructor.
· Student name
· Class name
· Instructor name
· Date
2.Introduction
Introduce the problem or topic being investigated. Include relevant background information, for example;
· Indicates why this is an issue or topic worth researching;
· Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
· Specify how others have operationalized this concept and measured these phenomena
Note: Introduction should not be more than one or two paragraphs.
Literature Review
There is no need for a literature review in this assignment
3.Research Question or Research Hypothesis
What is the Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents' answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer o ...
9/23/15, 6:48 PM
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Module 4 - Case
FEEDBACK LOOP AND ORGANIZATIONAL LEARNING
Assignment Overview
XCG
THE EXCELLENT CONSULTING GROUP
COMMUNICATION FROM ART:
Let's wrap up this project.
So far, ABC Company and Whole Foods Market like what we've been doing. I
have this last project, which involves analyzing Whole Foods Market's feedback
loops and organizational learning opportunities.
I want you to take a look at the feedback loops in Whole Foods Market.
REQUIRED READING:
Refer to the background reading on System Feedback Loops.
Case Assignment
Identify one Balancing Loop and one Reinforcing Loop. These feedback loops
should be critical to Whole Foods Market's performance and success. You should
have a good idea of what these are from your previous analysis.
Explain each one of these loops - what are the causal factors and how do they
affect each other. For the Reinforcing Loop, look for an area where there is
growth. For the Balancing Loop, look for goal behavior. Once you have identified
and explained these critical feedback loops, identify how Whole Foods Market
has generated organizational learning, and how they can go further and generate
additional organizational learning. What do they need to do to improve their
performance further?
Be sure to include references. Turn in the 5- to 6-page paper by the end of the
module.
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KEYS TO THE ASSIGNMENT:
This is what you need to do:
1. Determine the two critical feedback loops. Describe each Feedback Loop that
you identify in your organization and explain why you selected them. Make
sure you explain the Loop, the cause and effect process within the
Loop. You could also include a Causal Loop Diagram. If you do, show the
arrows and direction of effect (+ or -). Also, determine what the warrant is for
your case.
2. Briefly discuss the theory of organizational learning so that you provide a
summary of this information to the executives - establish this as common
ground.
3. Identify the learning activities in each feedback loop that Whole Foods Market
has already undertaken.
4. Identify the opportunities for organizational learning in each Feedback Loop.
Make a Case that these are learning opportunities. Logically show how the
feedback process provides an opportunity for the organization to learn and
improve its performance. Be precise. Depth and breadth in your discussion is
always a good thing.
Assignment Expectations
Your paper will be evaluated on the following seven points:
Precision - Does the paper address the question(s) or task(s)?
Breadth - Is the full breadth of the subject, that is, the Keys to the Assignment,
addressed?
Depth - Does the paper .
Assignment 2 Tests of SignificanceThroughout this assignment yo.docxrock73
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies. You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables. You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how every question should be formatted. You will complete all of the problems. Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses. All calculations should be coming from your SPSS. You will need to submit the SPSS output file to get credit for this assignment. This file will save as a .spv file and will need to be in a single file. In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
1. State your research hypothesis (H1) and null hypothesis (H0).
2. Identify your confidence interval (.05 or .01)
3. Conduct your analysis using SPSS.
4. Look for the valid score for comparison. This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’. We will call this “p”.
5. Compare the two and apply the following rule:
a. If “p” is < or = confidence interval, than you reject the null.
Be sure to explain to the reader what this means in regards to your study. (Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable. Perhaps you bold your font or use a different color.
ASSIGNMENT 2(200) WORD MINIUM
1. They allow us to see if our relationship is "statistically significant". (Remember that this only shows us that there is or is not a relationship but does NOT show us if it is big, small, or in-between.)
2. It let's us know if our findings can be generalized to the population which our sample was selected from and represents.
This week you will decide which test of significance you will use for your project. For this class your choices for tests will include one of the following:
· Chi-square
· t Test
· ANOVA
We will be using a process for hypothesis testing which outlines five steps researchers can follow to complete this process:
1. Write your research hypothesis (H1) and your null hypothesis (H0).
2. Identify and record your confidence interval. These are usually .05 (95%) or .01 (99%).
3. Complete the test using SPSS.
4. Identify the number under Sig. (2-tail). This will be represented by "p".
5. Compare the numbers in steps 2 and 4 and apply the following rule:
1. If p < or = confidence interval, than you reject the null hypothesis
Determine what to do with your null and explain this to your reader. Be sure to go beyond the phrase "reject or fail to reject the null" and explain how that impacts your research and best describes the relationship between variables.
TEST QUESTIONS-NEED FULL ANSWERS
Q1
Make up and discuss research examples corresponding to the various ...
This document outlines an agenda for a conference on applying behavioural insights to improve healthcare. The objectives are to help attendees understand the concept of "nudging" and how behavioural insights can be applied in clinical settings. Presentations will cover topics like understanding and changing behaviour using insights from psychology. Attendees will learn how to apply these insights to specify target behaviours, understand what influences behaviours using models like COM-B, and design interventions using techniques like simplifying messages and leveraging social influences. The goal is to encourage attendees to implement what they learn back in their own organizations.
Isn't this about me? The role of patients and the public in implementing evid...NEQOS
Master Class, led by Professor Richard Thomson- focusing on the role of patients and public in implementing evidence-based healthcare- including shared decision making
Master Class 'Putting evidence into practice' (plenary) presentation 25 11 14NEQOS
This document summarizes a master class on implementing evidence into practice using NICE guidance and quality standards. The event included presentations on NICE guidance and quality standards, a case study on implementing dementia guidance, and workshops on NICE pathways and resources. The goal was to improve awareness of NICE implementation support and consider challenges to applying evidence locally.
NICE Master Class final presentation 25 11 14 (including workshops)NEQOS
Collaborating for Better Care Partnership Master Class with NICE: 'Putting Evidence into Practice' - complete ppt slide pack including the workshop ppts and web links.
The document describes the Knowledge-To-Action Cycle, which consists of an Action Cycle and Knowledge Funnel. The Action Cycle is a 7-phase process for implementing knowledge to create planned changes. It involves identifying knowledge gaps, adapting knowledge to context, assessing barriers, selecting interventions, monitoring use, evaluating outcomes, and sustaining use. The Knowledge Funnel distills knowledge through inquiry, synthesis, and creating tools/products for end-users.
NICE Guidance implementation pro forma (nov 14)NEQOS
A Guidance implementation pro-forma to support organisations plan and scope their Guidance implementation*
* Disclaimer: This document was developed specifically for a workshop and is not a resource formally endorsed by NICE.
NICE support for commissioning resources (Nov 2014)NEQOS
Presentation from the Collaborating for Better Care Partnership's Master Class with NICE on 25th November 'Putting Evidence into Practice'. Information and resources to help commissioners implement NICE Guidance
Presentation given at 25th November Collaborating for Better Care Partnership Master Class with NICE - Information about the NICE Fellows and Scholars Scheme (to support implementation projects/ programmes)
NPT is a framework for thinking about implementing interventions by focusing on how interventions can become part of everyday practice through different groups working together. It involves using four sets of questions to identify potential barriers to successfully implementing an intervention and proposing solutions to improve the implementation process.
Executive summary:From Evidence to Practice: Addressing the Second Translatio...NEQOS
Supporting paper for Collaborating for Better Care Partnership Master Class 23rd October 2014: Executive summary 'From Evidence to Practice: Addressing the Second Translational Gap for Complex Interventions in Primary Care'
Supporting paper for NPT Master Class 'Getting ideas into Practice: normalising implementation of complex interventions across the healthcare system' - Collaborating for Better Care Partnership Master Class 23rd October 2014
Master Class 'Getting New Ideas in to Practice' presentation, Normalisation P...NEQOS
Master Class Presentation slides for 'Getting ideas into Practice: normalising the implementation of complex interventions across the healthcare system', Collaborating for Better Care Partnership Master Class with Dr Tracy Finch, Professor Carl May, Dr Tim Rapley.
Using Implementation Science to transform patient care (Knowledge to Action C...NEQOS
Master Class presentation and workshop materials from the NENC AHSN Collaborating for Better Care Partnership's Master Class, led by Professor Jeremy Grimshaw' on 1st September 2014
'Demystifying Knowledge Transfer- an introduction to Implementation Science M...NEQOS
Powerpoint presentation from 'Demystifying Knowledge Transfer: an introduction to Implementation Science' - 28th May 2014.
Facilitated by Professor Jeremy Grimshaw and Dr Justin Presseau
Collaborating for Better Care Stakeholder workshop presentation 14 03 14NEQOS
This document summarizes a stakeholder workshop for a Best Practice Partnership (BPP) collaboration between the North East and North Cumbria Academic Health Science Network.
The workshop included sessions on the BPP's strategic purpose and direction, stakeholder engagement, and a proposed 1-year work plan. The BPP aims to increase adoption of NICE guidance across the region through an implementation science approach. A 2-year work plan was drafted focusing on networking, showcasing innovations, developing metrics, and research. Feedback was gathered from groups on refining the vision, priorities, governance structure, and work plan. The BPP steering group will oversee the collaboration and have its inaugural meeting in May 2014.
Cyclothymia Test: Diagnosing, Symptoms, Treatment, and Impact | The Lifescien...The Lifesciences Magazine
The cyclothymia test is a pivotal tool in the diagnostic process. It helps clinicians assess the presence and severity of symptoms associated with cyclothymia.
VEDANTA AIR AMBULANCE SERVICES IN REWA AT A COST-EFFECTIVE PRICE.pdfVedanta A
Air Ambulance Services In Rewa works in close coordination with ground-based emergency services, including local Emergency Medical Services, fire departments, and law enforcement agencies.
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The Importance of Black Women Understanding the Chemicals in Their Personal C...bkling
Certain chemicals, such as phthalates and parabens, can disrupt the body's hormones and have significant effects on health. According to data, hormone-related health issues such as uterine fibroids, infertility, early puberty and more aggressive forms of breast and endometrial cancers disproportionately affect Black women. Our guest speaker, Jasmine A. McDonald, PhD, an Assistant Professor in the Department of Epidemiology at Columbia University in New York City, discusses the scientific reasons why Black women should pay attention to specific chemicals in their personal care products, like hair care, and ways to minimize their exposure.
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THE SPECIAL SENCES- Unlocking the Wonders of the Special Senses: Sight, Sound...Nursing Mastery
Title: Unlocking the Wonders of the Special Senses: Sight, Sound, Smell, Taste, and Balance
Introduction:
Welcome to our captivating SlideShare presentation on the Special Senses, where we delve into the extraordinary capabilities that allow us to perceive and interact with the world around us. Join us on a sensory journey as we explore the intricate structures and functions of sight, sound, smell, taste, and balance.
The special senses are our primary means of experiencing and interpreting the environment, each sense providing unique and vital information that shapes our perceptions and responses. These senses are facilitated by highly specialized organs and complex neural pathways, enabling us to see a vibrant sunset, hear a symphony, savor a delicious meal, detect a fragrant flower, and maintain our equilibrium.
In this presentation, we will:
Visual System (Sight): Dive into the anatomy and physiology of the eye, exploring how light is converted into electrical signals and processed by the brain to create the images we see. Understand common vision disorders and the mechanisms behind corrective measures like glasses and contact lenses.
Auditory System (Hearing): Examine the structures of the ear and the process of sound wave transduction, from the outer ear to the cochlea and auditory nerve. Learn about hearing loss, auditory processing, and the advances in hearing aid technology.
Olfactory System (Smell): Discover the olfactory receptors and pathways that enable the detection of thousands of different odors. Explore the connection between smell and memory and the impact of olfactory disorders on quality of life.
Gustatory System (Taste): Uncover the taste buds and the five basic tastes – sweet, salty, sour, bitter, and umami. Delve into the interplay between taste and smell and the factors influencing our food preferences and eating habits.
Vestibular System (Balance): Investigate the inner ear structures responsible for balance and spatial orientation. Understand how the vestibular system helps maintain posture and coordination, and explore common vestibular disorders and their effects.
Through engaging visuals, interactive diagrams, and insightful explanations, we aim to illuminate the complexities of the special senses and their profound impact on our daily lives. Whether you're a student, educator, or simply curious about how we perceive the world, this presentation will provide valuable insights into the remarkable capabilities of the human sensory system.
Join us as we unlock the wonders of the special senses and gain a deeper appreciation for the intricate mechanisms that allow us to experience the richness of our environment.
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As Mumbai's premier kidney transplant and donation center, L H Hiranandani Hospital Powai is not just a medical facility; it's a beacon of hope where cutting-edge science meets compassionate care, transforming lives and redefining the standards of kidney health in India.
NURSING MANAGEMENT OF PATIENT WITH EMPHYSEMA .PPTblessyjannu21
Prepared by Prof. BLESSY THOMAS, VICE PRINCIPAL, FNCON, SPN.
Emphysema is a disease condition of respiratory system.
Emphysema is an abnormal permanent enlargement of the air spaces distal to terminal bronchioles, accompanied by destruction of their walls and without obvious fibrosis.
Emphysema of lung is defined as hyper inflation of the lung ais spaces due to obstruction of non respiratory bronchioles as due to loss of elasticity of alveoli.
It is a type of chronic obstructive
pulmonary disease.
It is a progressive disease of lungs.
Basics of Electrocardiogram
CONTENTS
●Conduction System of the Heart
●What is ECG or EKG?
●ECG Leads
●Normal waves of ECG.
●Dimensions of ECG.
● Abnormalities of ECG
CONDUCTION SYSTEM OF THE HEART
ECG:
●ECG is a graphic record of the electrical activity of the heart.
●Electrical activity precedes the mechanical activity of the heart.
●Electrical activity has two phases:
Depolarization- contraction of muscle
Repolarization- relaxation of muscle
ECG Leads:
●6 Chest leads
●6 Limb leads
1. Bipolar Limb Leads:
Lead 1- Between right arm(-ve) and left arm(+ve)
Lead 2- Between right arm(-ve) and left leg(+ve)
Lead 3- Between left arm(-ve)
and left leg(+ve)
2. Augmented unipolar Limb Leads:
AvR- Right arm
AvL- Left arm
AvF- Left leg
3.Chest Leads:
V1 : Over 4th intercostal
space near right sternal margin
V2: Over 4th intercostal space near left sternal margin
V3:In between V2 and V4
V4:Over left 5th intercostal space on the mid
clavicular line
V5:Over left 5th intercostal space on the anterior
axillary line
V6:Over left 5th intercostal space on the mid
axillary line.
Normal ECG:
Waves of ECG:
P Wave
•P Wave is a positive wave and the first wave in ECG.
•It is also called as atrial complex.
Cause: Atrial depolarisation
Duration: 0.1 sec
QRS Complex:
•QRS’ complex is also called the initial ventricular complex.
•‘Q’ wave is a small negative wave. It is continued as the tall ‘R’ wave, which is a positive wave.
‘R’ wave is followed by a small negative wave, the ‘S’ wave.
Cause:Ventricular depolarization and atrial repolarization
Duration: 0.08- 0.10 sec
T Wave:
•‘T’ wave is the final ventricular complex and is a positive wave.
Cause:Ventricular repolarization Duration: 0.2 sec
Intervals and Segments of ECG:
P-R Interval:
•‘P-R’ interval is the interval
between the onset of ‘P’wave and onset of ‘Q’ wave.
•‘P-R’ interval cause atrial depolarization and conduction of impulses through AV node.
Duration:0.18 (0.12 to 0.2) sec
Q-T Interval:
•‘Q-T’ interval is the interval between the onset of ‘Q’
wave and the end of ‘T’ wave.
•‘Q-T’ interval indicates the ventricular depolarization
and ventricular repolarization,
i.e. it signifies the
electrical activity in ventricles.
Duration:0.4-0.42sec
S-T Segment:
•‘S-T’ segment is the time interval between the end of ‘S’ wave and the onset of ‘T’ wave.
Duration: 0.08 sec
R-R Interval:
•‘R-R’ interval is the time interval between two consecutive ‘R’ waves.
•It signifies the duration of one cardiac cycle.
Duration: 0.8 sec
Dimension of ECG:
How to find heart rhytm of the heart?
Regular rhytm:
Irregular rhytm:
More than or less than 4
How to find heart rate using ECG?
If heart Rhytm is Regular :
Heart rate =
300/No.of large b/w 2 QRS complex
= 300/4
=75 beats/mins
How to find heart rate using ECG?
If heart Rhytm is irregular:
Heart rate = 10×No.of QRS complex in 6 sec 5large box = 1sec
5×6=30
10×7 = 70 Beats/min
Abnormalities of ECG:
Cardiac Arrythmias:
1.Tachycardia
Heart Rate more than 100 beats/min
Research, Monitoring and Evaluation, in Public Healthaghedogodday
This is a presentation on the overview of the role of monitoring and evaluation in public health. It describes the various components and how a robust M&E system can possitively impact the results or effectiveness of a public health intervention.
Simple Steps to Make Her Choose You Every DayLucas Smith
Simple Steps to Make Her Choose You Every Day" and unlock the secrets to building a strong, lasting relationship. This comprehensive guide takes you on a journey to self-improvement, enhancing your communication and emotional skills, ensuring that your partner chooses you without hesitation. Forget about complications and start applying easy, straightforward steps that make her see you as the ideal person she can't live without. Gain the key to her heart and enjoy a relationship filled with love and mutual respect. This isn't just a book; it's an investment in your happiness and the happiness of your partner
1. Collaborating for Better Care Partnership
How to use the NPT toolkit
The NPT toolkit is here to help you think through implementation and integration problems in health care.
Whether you're involved in technological innovations, testing complex interventions, or implementing and
evaluating any new way of thinking, working, or organising change in healthcare.
You can use it at any stage: from initially thinking through an idea, to understanding outcomes.
There are 3 steps to using the NPT toolkit:
Working the bars
We've simplified the constructs of Normalisation Process Theory and created sixteen items. These
represent the core variables of the theory. You can use these items to think through your implementation
problem. Each item has three distinct features:
A statement that defines the NPT variable.
A sliding bar that allows you to input information about that variable.
An explanation, that gives more detail about what the statement means.
The default position for each bar is at the centre. This is not neutral. With your cursor move the bar to the
right, for a positive response to the Item, and to the left for a negative response. This does not give you a
numerical score - instead it gives a subjective assessment.
Creating a summary
When you have reached Item 16, you will see a label telling you to 'view results'. These are presented to
you as a set of graphs. You can save them to your own computer (press the 'download pdf' button) and
then print them out. The summary is a private document, only you will see it.
Interpreting the report
The graphs show the strength that you have assigned to each variable. Positive responses extend further
out from the centre than negative ones. Look for areas where the responses are closer to the centre. These
may tell you that participants cannot make sense, or have not signed up to the innovation. Perhaps they
cannot enact it in a way that works for them, or cannot assess its effects and their value. If the responses
are positive, the opposite may be true - but read our health warning below!
Each report consists of a primary graph that represents all 16 Items, and four graphs that describe your
responses to statements, or items that relate to each of the theory's four constructs. These give a clearer
picture of each specific area of work that leads to the embedding of an innovation or complex intervention.
To properly interpret this information you need to read the sections of this website that tell you what NPT
is and how it works - and where NPT constructs are explained and examples are provided for each variable.
Health Warning
This is not a scientific instrument. The bars do not provide objective scores for each variable. Use them as
heuristic tools to think through an implementation or integration process. Instruments to measure these
variables need to be constructed using a different set of techniques - see section on Survey Research.
We do not collect the data you input to this tool. Only you have access to it
http://www.normalizationprocess.org/npt-toolkit/how-to-use-the-npt-toolkit.aspx
Taken from www.normalizationprocess.org