This document provides an overview of key concepts in statistics as they relate to environmental sampling and analysis. It defines common statistical terms like mean, median, mode, variance, standard deviation, and normal distribution. It discusses population vs. sample, random variables, and the use of histograms and box plots to visualize data. Key aspects of accuracy, precision, and experimental error are covered. The document also introduces concepts like linear regression, correlation, and their uses in environmental analysis. Estimating mean and variance from a sample is discussed along with the use of α values in determining confidence intervals for probability distributions.
Analysis of Variance - Meaning and TypesSundar B N
This vides briefed the meaning, Introduction, Definition, Application, Classification and Types of ANOVA.
Video link https://youtu.be/YLHGYVMH2T4
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
It is very difficult to distinguish the differences between ANOVA and regression. This is because both terms have more similarities than differences. It can be said that ANOVA and regression are the two sides of the same coin.
Wiring a pH and Conductivity Probe to a Zeno3200TAMUK
Short guide explaining how wire a pH and conductivity sensor to a Zeno 3200 data logger. The models mentioned may not be manufactured anymore at this time.
A user guide for setting up a stock Zeno 3200 data logger to work with wind speed, wind direction, and input voltages from an air monitor. Includes how to log in to Zeno 3200 with Hyperterminal and building the serial cable.
Analysis of Variance - Meaning and TypesSundar B N
This vides briefed the meaning, Introduction, Definition, Application, Classification and Types of ANOVA.
Video link https://youtu.be/YLHGYVMH2T4
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
It is very difficult to distinguish the differences between ANOVA and regression. This is because both terms have more similarities than differences. It can be said that ANOVA and regression are the two sides of the same coin.
Wiring a pH and Conductivity Probe to a Zeno3200TAMUK
Short guide explaining how wire a pH and conductivity sensor to a Zeno 3200 data logger. The models mentioned may not be manufactured anymore at this time.
A user guide for setting up a stock Zeno 3200 data logger to work with wind speed, wind direction, and input voltages from an air monitor. Includes how to log in to Zeno 3200 with Hyperterminal and building the serial cable.
Draft of a lab manual. Talks about the use of tape, using a balance, DI water, care of pH probes, pictures and descriptions of equipment. Still working on it.
This model is probably not made anymore. This user guide explained how to assemble some hardware and use the software to set up an SDI water depth sensor.
A short slide presentation showing how to calibrate a Dasibi 1008 series ozone monitor and some numbers to look for to determine whether it is a leak or valve issue.
A short document describing how to set up a datalogger, ozone generator, and ozone monitor to expose rats to ozone. I had help set this up some time ago for a pharmacy research project. It is not necessarily the only way or best way since there many other similar items available, but it did work.
It is a multi-element analysis technique that will separate a sample into its constituent atoms and ions and excite it to a higher energy level.
Cause them to emit light with a distinct wavelength, which will be analyzed.
Many factors impacting the measurement precision of ICP-OES and ICP-MS are still often neglected for everyday operation, however. Sample preparation is one of the factors that play a crucial role in the success of high-quality sample analysis. In this webinar, our experts will discuss sample preparation to: 1) improve analysis precision 2) make difficult samples easy to be analyzed 3) eliminate sample dilution to minimize error introduction.
For more information, please visit here: http://chrom.ms/CtRtKpw
10 Things your Audience Hates About your PresentationStinson
See it with animations! https://vimeo.com/179236019
It’s impossible to win over an audience with a bad presentation. You might have the next big thing, but if your presentation falls flat, then so will your idea. While every audience is different, there are some universal cringe-worthy presentation mistakes that are all too common. Whether you’re an amateur or a seasoned presenter, you should always avoid this list of top 10 things your audience hates. Are you committing any of these 10 fatal presentation sins?
For more presentation help, visit stinsondesign.com/blog
The ppt gives an idea about basic concept of Estimation. point and interval. Properties of good estimate is also covered. Confidence interval for single means, difference between two means, proportion and difference of two proportion for different sample sizes are included along with case studies.
Quantitative Analysis for Emperical ResearchAmit Kamble
Overview for Approach Methods for quantitative analysis; which includes
1) Planning of Experiments
2) Data Generation
3) presentation of report
some numerical approach methods; data modeling; hypothesis methods
Voltage drop calculator for street lights. Works with Excel 365. Get file by downloading presentation.
Open object presentation. Click 'Enable Content'. Right click over object for "Package Shell Object"->"Activate Content"->Press "OPEN" button to see VDROP folder. Open folder for the files.
This is provided "AS IS" with no warranty or guarantees or liability.It is accurate, but make sure to check with your standards to be sure.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
1. 9/3/2013
1
Lab #1 Basic Statistics
EVEN 3321
• Definition of STATISTICS
• 1: a branch of mathematics dealing with the collection, analysis,
interpretation, and presentation of masses of numerical data
• 2: a collection of quantitative data
• Origin of STATISTICS: German Statistik study of political facts and figures,
from New Latin statisticus of politics, from Latin status state
• First Known Use: 1770
• Rhymes with STATISTICS: ballistics, ekistics, linguistics, logistics, patristics,
stylistics
• http://www.merriam-webster.com/dictionary/statistics
Statistics
2. 9/3/2013
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Why is this important?
Environmental Sampling
∗ Need to know relationships
between quantities
∗ Parameters (examples):
PH
Conductivity
Particle concentration
Amount of a chemical or other
material in air, water, soil
Bacteria counts
Instrumentation
∗ PH Meter
∗ Micro-balance
∗ Gas Chromatography
∗ Ozone monitor
∗ ICPMS
∗ TOC
Morning Session of FE Exam
Engineering Probability and Statistics Topic Area
The following subtopics are covered in the Engineering Probability and
Statistics portion of the FE Examination:
A. Measures of central tendencies and dispersions (e.g., mean, mode, standard
deviation)
B. Probability distributions (e.g., discrete, continuous, normal, binomial)
C. Conditional probabilities
D. Estimation (e.g., point, confidence intervals) for a single mean
E. Regression and curve fitting
F. Expected value (weighted average) in decision-making
G. Hypothesis testing
The Engineering Probability and Statistics portion covers approximately 7% of
the morning session test content.
Reference: http://www.feexam.org/ProbStats.html
FE Exam
3. 9/3/2013
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• “Sample” versus “population”
• Random variables
• Population mean (μ), variance (σ2) & standard
deviation (s), kurtosis, skewness
• Also expressed as: Sample mean (y), variance (s2), and
standard deviation (s)
• Frequency distribution/histogram (relates to skewness)
• Boxplots
• Precision and accuracy, Confidence interval
• Linear regression
Some Key Ideas
• It is impossible to determine the concentrations of a
given pollutant at every possible location at a site.
• Statistical methods allow us to use a small number of
samples to make inferences about the entire site.
• A single sample is a subset of all the possible samples (n)
that could be taken from a given site.
–Multivariate data sets have several data values
generated for each location and time.
–As opposed to univariate data sets.
• The hypothetical set of all possible values is referred to
as the population.
Key Ideas: continued
4. 9/3/2013
4
• Number of samples collected is the sample size (n).
• A random variable is a variable that is random.
• Experimental observations are considered random
variables.
• Experimental errors
Key Ideas continued
∗ Experimental measurements are always imperfect:
∗ Measured value = true value ± error
∗ The error is a combined measure of the inherent variation
of the phenomenon we are observing and the numerous
factors that interfere with the measurement.
∗ Any quantitative result should be reported with an
accompanying estimate of its error.
∗ Systematic errors (or determinate errors) can be traced to
their source (e.g., improper sampling or analytical
methods).
∗ Random errors (or indeterminate errors) are random
fluctuations and cannot be identified or corrected for.
Experimental Errors
5. 9/3/2013
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Example: Population versus Sample
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Ozone[ppb]
April 2013
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1 2 3 4 5 6 7 8 9 101112131415161718192021222324
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24 Hours
• Accuracy is the degree of agreement of a measured
value with the true or expected value.
• Precision is the degree of mutual agreement among
individual measurements (x1, x2, …xn) made under the
same conditions.
• Precision measures the variation among measurements
and may be expressed as sample standard deviation (s):
Accuracy and Precision
( )
2
1
1
n
i
i
y y
s
n
=
−
=
−
∑
6. 9/3/2013
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Accuracy and Precision
Example: Five analysts were each given five samples that were
prepared to have a known concentration of 8.0 mg/L. The results are
summarized in the figure below.
Accuracy and Precision
7. 9/3/2013
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• A random variable, y is characterized by:
• A set of possible values.
An associated set of relative likelihoods (this is called a
probability distribution).
• Random variables can be discrete or continuous.
e.g., a die toss is a discrete random variable.
e.g., ozone conc. is a continuous random variable.
• Experimental observations are considered random
variables.
Random Variables
• When we sample the environment, the sample values are
known, but not the population values.
• For a sample size n, the number of times a specific value
occurs is call the frequency.
• The frequency divided by the sample size n is the relative
frequency.
• The relative frequency is an estimate of the probability
that given value occurs in the population.
• If we compute the relative frequencies for each possible
value of a random variable, we have an estimate of the
probability distribution of the random variable (see next
slide).
Frequency Distribution
8. 9/3/2013
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• For continuous random variables, we can group the
measured values into intervals (or “bins”).
• Plotting the number of values measured in each interval
gives a frequency histogram (see next slide).
• Plotting the total number of measured values in or below
a given interval gives a cumulative frequency distribution
(see next slide).
• To obtain the relative frequency, the number of measured
values falling within a given interval is divided by the
sample size n.
• The shape of a histogram can allow us to infer the
distribution of the population.
Continuous Frequency Distributions
Histograms
Normal (Gaussian) and skewed
9. 9/3/2013
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Histograms (cont.)
Bimodal and Uniform
∗ In general, we do not know the mean and standard
deviation of the underlying population.
∗ The population mean can be estimated from the
sample mean and sample standard deviation s:
∗ Note that in environmental monitoring, the standard
deviation s for the sample depends on the amount of
sample collected
Sample Mean and Standard Deviation
1
1 n
i
i
y x
n =
= ∑ ( )
2
1
1
n
i
i
y y
s
n
=
−
=
−
∑
10. 9/3/2013
10
In many situations, environmental data involves working with a small sample set.
Also known as Bessel’s correction or unbiased estimate.
http://en.wikipedia.org/wiki/Bessel%27s_correction
Another way of looking at it:
The POPULATION VARIANCE (σ2) is a PARAMETER of the population.
s2 The SAMPLE VARIANCE is a STATISTIC of the sample.
We use the sample statistic to estimate the population parameter.
The sample variance s2 is an estimate of the population variance σ2.
Note: Excel 2010 has a couple functions for standard deviation. One for population (=STD.P(range))
and the other based on sample (=STD.S(range)).
Short video:
https://www.khanacademy.org/math/probability/descriptive-
statistics/variance_std_deviation/v/review-and-intuition-why-we-divide-by-n-1-for-the-unbiased-
sample-variance
A note about (n-1)
• Most random variables have two important characteristic
values: the mean (μ) and the variance (s2).
• Square-root of the variance is the standard deviation (s).
• The mean is also called the expected value of the random
variable xi.
• The mean represents balance point on graph.
• The variance & standard deviation both quantify how
much the possible values disperse away from the mean.
• For a normal distribution, 68% of values lies within µ ±
σ, 95% within µ ± 2σ, and 99.7% within µ ± 3σ.
Mean, Variance, Standard Deviation
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Mean, Median, Mode
∗ Covariance is a simplistic test to determine whether the
data can be characterized by a normal distribution. The
formula for covariance is the standard deviation divided by
the mean. The closer the ratio is to zero, the better the
possibility that the data has a normal distribution. A
number greater than unity indicates a non- normal
distribution.
∗ Skewness is a measure of symmetry or lack of it and can be
normal, negative, or positive.
∗ Kurtosis is a measure whether the data are flat relative to a
normal distribution.
Covariance, Skewness, Kurtosis
13. 9/3/2013
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Normal Distribution at 68%, 95%, 99%
The value is the probability that a random variable will
fall in the upper or lower tail of a probability
distribution.
For example, α = 0.05 implies that there is a 0.95
probability that a random variable will not fall in the
upper or lower tail of the probability distribution.
Statistical tables of probability distributions (e.g.,
normal and “student t”) list probabilities that a random
variable will fall in the upper tail only.
α Values for Probability Distributions
14. 9/3/2013
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• We typically want to determine a confidence interval
for which we are 90% confident that a random
variable will not fall in either tail.
• In this case, we use an α/2 = 0.05.
• Similarly, to determine 95% and 99% confidence
intervals, we would use α/2 = 0.025 and 0.005,
respectively.
α values and confidence intervals
= ±
√
= ± ( )( )
Regression analysis (dependency) – an analysis focused
on the degree to which one variable (the dependent
variable) is dependent upon one or more other
variables (independent variable).
(examples: ozone vs. temperature, bacteria counts
versus chlorination treatment)
Correlation analysis – neither variable is identified as
more important than the other, but the investigator is
interested in their interdependence or joint behavior
NOTE: Correlation or association is not causation.
Linear Regression
15. 9/3/2013
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Linear Regression Examples
• Slope formula: y = mx + b
• coefficient of determination, R2 is used in the context of statistical models whose main
purpose is the prediction of future outcomes on the basis of other related
information. It is the proportion of variability in a data set that is accounted for by the
statistical model. It provides a measure of how well future outcomes are likely to be
predicted by the model.
R2 does NOT tell whether:
the independent variables are a true cause of the changes in the
dependent variable
omitted-variable bias exists
the correct regression was used
the most appropriate set of independent variables has been chosen
there is co-linearity present in the data
the model might be improved by using transformed versions of the
existing set of independent variables
R2, Slope Equation
16. 9/3/2013
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Statistics Excel 2010
Summary Statistics
http://academic.brooklyn.cuny.edu/economic/friedman/
descstatexcel.htm
Column1
Mean 74.92857143
Standard Error 5.013678308
Median 78.5
Mode 80
Standard Deviation 18.75946647
Sample Variance 351.9175824
Kurtosis 1.923164749
Skewness -1.31355395
Range 71
Minimum 29
Maximum 100
Sum 1049
Count 14
Confidence Level(95.0%) 10.83139138
Ozone April 2013
Histogram and Summary Statistics
Mean 35.48948
Median 35
Mode 35
Standard Dev 10.72231
Sample Variance 114.968
Kurtosis -0.20548
Skewness 0.146677
Minimum 2
Maximum 68
Sum 25304
Count 713
17. 9/3/2013
17
April 2013 Ozone
Box-Whisker
Population size: 713
Median: 35
Minimum: 2
Maximum: 68
First quartile: 28
Third quartile: 43
Interquartile Range: 15
Outliers: 2 5 5 5 6 8 10 11 11 68 65 64 62 62 61
61 60 59 58 58 58
∗ Access TCEQ web site data.
∗ Importing files into Excel and Matlab.
∗ Using Excel for statistical work, Matlab for statistics.
Plotting histograms.
∗ Read the papers posted on Blackboard: Statistics for
Analysis of Experimental Data, Errors and Limitation
Associated with Regression, and Why we divide by n-
1.
∗ Lab will be assigned.
Lab Thursday