The document outlines 9 stages in the decision-making process: 1) understanding the scenario, 2) identifying information and sources, 3) analyzing factors that affect information quality, 4) analyzing the information, 5) identifying alternatives, 6) identifying consequences of the alternatives, 7) making a decision, 8) justifying the decision, and 9) communicating the decision to others. It then provides details about each stage, including how to identify existing and required information, factors that can impact information quality like currency and accuracy, how to analyze data and identify trends, potential alternatives, how to consider consequences of each alternative, and how to make and justify a decision.
My talk in the technical meeting "Global Burden of Diseases and Scientific Computation in Health". 25-26 September 2015. FIOCRUZ, Rio de Janeiro, Brazil
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Data Visualization 101: How to Design Charts and GraphsVisage
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Your data is only as good as your ability to understand and communicate it. The right visualization is essential to incite a desired action, whether from customers or colleagues. But most marketers aren’t mathematicians or adept at data visualization. Fortunately, you don’t need a PhD in statistics to crack the data visualization code.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
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Exploratory Data Analysis - A Comprehensive Guide to EDA.pdfJamieDornan2
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This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
3 Frequent Mistakes in Healthcare Data AnalyticsHealth Catalyst
Healthcare organizations are recognizing the value of healthcare analytics, especially in their Big Data, population health management, or accountable care initiatives. This is good because without analytics it is difficult to impossible to run these programs successfully. That said, analytics are not the magic bullet and proper process must be in place. The three most common mistakes health systems makes with their healthcare analytics are: 1. Analytics Whiplash- when the analytics goes from one project to another without being able to fully understand the data and what it’s saying. 2. Coloring the Truth- When analysts don’t feel like they can be completely forthcoming with information and only give leadership the news they want to hear. 3. Deceitful Visualizations- Manipulating charts, graphs, and the like to reflect what the analyst or leadership wants the data to say, rather than what it actually says.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
3 Frequent Mistakes in Healthcare Data AnalyticsHealth Catalyst
Healthcare organizations are recognizing the value of healthcare analytics, especially in their Big Data, population health management, or accountable care initiatives. This is good because without analytics it is difficult to impossible to run these programs successfully. That said, analytics are not the magic bullet and proper process must be in place. The three most common mistakes health systems makes with their healthcare analytics are: 1. Analytics Whiplash- when the analytics goes from one project to another without being able to fully understand the data and what it’s saying. 2. Coloring the Truth- When analysts don’t feel like they can be completely forthcoming with information and only give leadership the news they want to hear. 3. Deceitful Visualizations- Manipulating charts, graphs, and the like to reflect what the analyst or leadership wants the data to say, rather than what it actually says.
data science course with placement in hyderabadmaneesha2312
360DigiTMG delivers data science course with placement in hyderabad, where you can gain practical experience in key methods and tools through real-world projects. Study under skilled trainers and transform into a skilled Data Scientist. Enroll today!
Do you spend hours struggling to manually produce the reports management demands? Are you working with disparate islands of outdated data? And, after all that hard work, are the reports produced inaccurate and untrustworthy?
One of the easiest ways to improve the quality of information that you are able to provide is by simply sourcing good data. This presentation will show you the best practices for sourcing data to ensure that it is trusted, credible and reliable.
Statistical DistributionsEvery statistics book provides a list.pdfhtanandpalace
Statistical Distributions
Every statistics book provides a listing of statistical distributions, with their properties, but
browsing through these choices can be frustrating to anyone without a statistical background, for
two reasons. First, the choices seem endless, with dozens of distributions competing for your
attention, with little or no intuitive basis for differentiating between them. Second, the
descriptions tend to be abstract and emphasize statistical properties such as the moments,
characteristic functions and cumulative distributions. In this appendix, we will focus on the
aspects of distributions that are most useful when analyzing raw data and trying to fit the right
distribution to that data.
Fitting the Distribution
When confronted with data that needs to be characterized by a distribution, it is best to start with
the raw data and answer four basic questions about the data that can help in the characterization.
The first relates to whether the data can take on only discrete values or whether the data is
continuous; whether a new pharmaceutical drug gets FDA approval or not is a discrete value but
the revenues from the drug represent a continuous variable. The second looks at the symmetry of
the data and if there is asymmetry, which direction it lies in; in other words, are positive and
negative outliers equally likely or is one more likely than the other. The third question is whether
there are upper or lower limits on the data;; there are some data items like revenues that cannot
be lower than zero whereas there are others like operating margins that cannot exceed a value
(100%). The final and related question relates to the likelihood of observing extreme values in
the distribution; in some data, the extreme values occur very infrequently whereas in others, they
occur more often.
Is the data discrete or continuous?
The first and most obvious categorization of data should be on whether the data is restricted to
taking on only discrete values or if it is continuous. Consider the inputs into a typical project
analysis at a firm. Most estimates that go into the analysis come from distributions that are
continuous; market size, market share and profit margins, for instance, are all continuous
variables. There are some important risk factors, though, that can take on only discrete forms,
including regulatory actions and the threat of a terrorist attack; in the first case, the regulatory
authority may dispense one of two or more decisions which are specified up front and in the
latter, you are subjected to a terrorist attack or you are not.
With discrete data, the entire distribution can either be developed from scratch or the data can be
fitted to a pre-specified discrete distribution. With the former, there are two steps to building the
distribution. The first is identifying the possible outcomes and the second is to estimate
probabilities to each outcome. As we noted in the text, we can draw on historical data or
experience as .
MedTech clinical data collection problems have been found throughout our ten years of work with over 250 medical device studies from across the globe. We keep running across these seven hazards while working in the MedTech business and clinical operations.
Statistical Processes
Can descriptive statistical processes be used in determining relationships, differences, or effects in your research question and testable null hypothesis? Why or why not? Also, address the value of descriptive statistics for the forensic psychology research problem that you have identified for your course project. read an article for additional information on descriptive statistics and pictorial data presentations.
300 words APA rules for attributing sources.
Computing Descriptive Statistics
Computing Descriptive Statistics: “Ever Wonder What Secrets They Hold?” The Mean, Mode, Median, Variability, and Standard Deviation
Introduction
Before gaining an appreciation for the value of descriptive statistics in behavioral science environments, one must first become familiar with the type of measurement data these statistical processes use. Knowing the types of measurement data will aid the decision maker in making sure that the chosen statistical method will, indeed, produce the results needed and expected. Using the wrong type of measurement data with a selected statistic tool will result in erroneous results, errors, and ineffective decision making.
Measurement, or numerical, data is divided into four types: nominal, ordinal, interval, and ratio. The businessperson, because of administering questionnaires, taking polls, conducting surveys, administering tests, and counting events, products, and a host of other numerical data instrumentations, garners all the numerical values associated with these four types.
Nominal Data
Nominal data is the simplest of all four forms of numerical data. The mathematical values are assigned to that which is being assessed simply by arbitrarily assigning numerical values to a characteristic, event, occasion, or phenomenon. For example, a human resources (HR) manager wishes to determine the differences in leadership styles between managers who are at different geographical regions. To compute the differences, the HR manager might assign the following values: 1 = West, 2 = Midwest, 3 = North, and so on. The numerical values are not descriptive of anything other than the location and are not indicative of quantity.
Ordinal Data
In terms of ordinal data, the variables contained within the measurement instrument are ranked in order of importance. For example, a product-marketing specialist might be interested in how a consumer group would respond to a new product. To garner the information, the questionnaire administered to a group of consumers would include questions scaled as follows: 1 = Not Likely, 2 = Somewhat Likely, 3 = Likely, 4 = More Than Likely, and 5 = Most Likely. This creates a scale rank order from Not Likely to Most Likely with respect to acceptance of the new consumer product.
Interval Data
Oftentimes, in addition to being ordered, the differences (or intervals) between two adjacent measurement values on a measurement scale are identical. For example, the di ...
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U5 a1 stages in the decision making process
1. Unit 5 Data Modelling
A1 STAGES IN THE DECISION-MAKING PROCESS
1
2. This section covers :
1. Understanding the scenario.
2. Identifying information and sources.
3. Factors affecting the quality of information.
4. Analysing the information.
5. Identifying alternatives.
6. Identifying consequences of implementing the alternatives.
7. Making a decision.
8. Justifying the decision.
9. Communicating decision(s) to others
2
3. Understanding the scenario.
A scenario describes a situation where a problem exists that needs to be
addressed. It will include some details on the problem, a required
solution and a time frame.
Most scenarios are incomplete. This may not be deliberate, but due to a
lack of information, or a lack of clarity on the required solution.
A key skill in developing a solution is Systems Analysis. This can be
summarised as:
Identifying what is currently done
How the task is currently done
What the problem is
What is the proposed logical solution
What is the proposed physical solution
3
4. Identifying information and sources.
Information required
Information that is already available
Additional information needed
Sources of additional information
Requirements for verifying the information sources.
4
5. Factors affecting the quality of information.
Currency - time sensitivity of the data
On August 1st 2021 the price of Gold was £1306.08 per oz.
On July 1st 2021 the price of Gold was £1296.27 per oz
On June 1st 2021 the price of Gold was £1348.96 per oz
This asset price is governed by pure supply and demand (excluding
speculation).
Consider the implication of buying and selling items at different
prices if you use the wrong purchase and sale price figures.
5
6. Factors affecting the quality of information.
Accuracy
1) Accuracy relates to the difference between the data used and
the actual data.
If the data used is 14, 18, 23 and the real data is 11, 15, 20 the data being
used is inaccurate. In this example it is consistently inaccurate, all values
are 3 more than they should be.
If we look at data being used as 7.2, 12, 14.4 when the real data is 6, 10,
12 the data is consistently inaccurate by 20% over actual.
These 2 cases can be rectified mathematically by reversing the
discrepancy.
If the values are different by a varying margin, this is unable to be
rectified.
6
7. Factors affecting the quality of information.
Accuracy
2) Precision is how “precise” the data is – the number of
significant digits.
As an example a tray of 24 tins is purchased for £37.50 which gives a price
per can of £1.5625.
If we use a mark-up of 20% the selling price per tin is £1.875.
Precise, (and accurate), but of no real-world use.
Scientific notation, e.g. 2.563 *10^6 is really 2563000 are the last 3 digits
really all 0? This can result in approximation, but on certain values this is
acceptable.
7
8. Factors affecting the quality of information.
External factors
SWOT analysis.
Strengths and Weaknesses are internal, you can control.
Opportunities and Threats are external, you have no control.
Consider each of the following and how they can have an impact:
Government changes a rate of tax (NI, VAT, Income Tax)
You import/export goods and are unable to do so (9/11 grounded all flights
for an extended period, the current Pandemic)
Exchange rates move up/down
Asset values change due to changes in demand.
Health and Safety legislation changes
8
9. Analysing the data.
Data is the values in the spreadsheet, Information has context.
Looking at the data in context:
What can we see at the current time?
What trends are there ?
Can we identify e.g. the good and weak sales and is there a reason?
Is it best displayed as numbers, or graphically?
Does statistical analysis show anything
9
10. Identifying alternatives.
In programming we have different PARADIGMS
Object oriented, Structures, visual etc
Each results in a different approach to the task.
What different alternatives are there in spreadsheets
Organisation of the spreadsheet
“efficiency” of calculations
Data entry by “keyboard”, dropdown or slider.
Internal/External access to data
Each different point you consider could generate an alternative
10
11. Identifying consequences of
implementing the alternatives.
Each alternative will have its own benefits and drawbacks
This is about identifying the differences of each alternative
You must compare like to like, so the list of points is the same
What is the degree of difference
Is 2 days significant on a 6 month contract?
Is £150 significant on a £5000 contract
Each point could have a variance – difference from that of the others
Here you are only identifying the consequences
11
12. Making a decision.
Decisions have to be based on justification of a number of points.
What are the key points,
How do they apply to each alternative
How do you “score” them to identify the best to worst.
Consider how I could put each of you in order of “best” to “worst”
12
13. Making/Justifying the decision.
The decision you make could be challenged – WHY do this?
What data are you working with
What options were considered?
For each option what were the characteristics you considered
How were these weighted
How did you come up with the weightings
What was the ranking of “best” to “worst”
If yours isn’t “best” Why
13
14. Assessment note
GENERALLY
Gathering the data and explaining it equates to PASS criteria
Comparing or Analysing data equates to Merit criteria
Evaluation of options and justification equates to Distinction criteria
14
15. Communicating decision(s) to others
It is unusual for you to be the sole member of the team
Your colleagues need to know what is happening
Plans for all to follow
Understanding of individual roles and deadlines
You work with a team leader who holds responsibility
Needs to know what is going on, and when, and by whom
Your work is for a client
Who needs to be kept informed of progress and problems
15
16. Task
This link opens a Moodle task.
There is insufficient detail to produce a “solution”.
Using the previous work, identify, using the 9 points, what further
information you need to be able to address all of the points
16
Editor's Notes
A presentation as part of the BTEC Level 3 IT qualification. This is for Unit 5 Data Modelling , and deals with Learning aim A section 1 The stages in the decision making process.
There are 9 sections to this although 7, Making a decision and 8 Justifying a decision are linked and dealt with as a single item.
Information Required
When the “process has a problem”, what data is being processed, where does it come from, how is it processed and what is the problem? These are repeated in most programming type scenarios, weather it is spreadsheets, databases, or programming languages.
For the information required you have to consider
Information already available. What is it, where does it come from and how reliable is it? Remember at this point we are looking at the process as a whole, and the how of processing is needed as well as the what is processed.
There may be an existing operational specification that identifies what the data is and where it comes from coupled with how it is processed. This documentation Review should clarify the existing data and process
Additional information needed / Sources of additional information
If you identify something is missing in understanding the existing system or what is required in the “new” system, where is it available from?
Looking at the paperwork as above is a good start, but you may need to ask users what they are doing as well as watching what they do. Do the users understand what they are doing and why they are doing it the way they are?
It may be that part of the “new” system is only with the managers and is an idea. Does this make sense with the existing ways of working, does it require a new way of working or is there scope for discussion over the how of the new system.
Requirements for veryifying the information sources
Everyone uses Google. This is not a verifiable source.
Look at Official documents such as the annual report and accounts if it is relevant
Review the operations manuals etc that may indicate a specific process in the organisation has to be done in a certain way (think chemicals and adding them in the wrong order) Changing the control flow may have unforeseen consequences.
When the “process has a problem”, what data is being processed, where does it come from and how is it processed and what is the problem?.
