The document discusses various statistical tests used to analyze quantitative and qualitative data, including the t-test, chi-square test, and tests of significance. It explains that the t-test is used to compare the means of two groups when the sample size is less than 30, while the z-test is used when the sample size is 30 or more. The chi-square test can be used to determine if there is an association between two variables and to compare proportions between groups. Key aspects like type I and type II errors in hypothesis testing and interpreting p-values are also summarized.
This document provides an overview of basic probability concepts and probability distributions. It defines probability as the likelihood of an event occurring between 0 and 1, and explains the roles of probability and statistics. It then covers various ways to calculate probability using classical, relative frequency and subjective approaches. Other topics discussed include events, sample spaces, conditional probabilities, the law of large numbers, rules of probability, and random variables.
The document discusses key concepts in probability, including:
- Probability is a measure of uncertainty or likelihood of outcomes ranging from 0 to 1.
- Events can be mutually exclusive, independent, or dependent. Laws of probability like addition and multiplication rules are used based on the relationship between events.
- Conditional probability is the probability of one event occurring given that another event has occurred or is known.
- Examples are given to illustrate calculating probabilities of outcomes from experiments or surveys using relative frequency definitions of probability.
The document discusses conditional probability and how odds ratios and risk ratios can be interpreted as conditional probabilities. It provides examples of how to calculate odds ratios from case-control study data and risk ratios from cohort study data. It also discusses how odds ratios from studies where the outcome is common should be interpreted as increased odds rather than risk, and how odds ratios can be converted to risk ratios using a simple formula.
This document discusses elementary probability and its applications in medicine. It provides definitions of key probability terms like chance, outcome, experiment, and event. It also covers different approaches to measuring probability, including the classical, frequentist, subjective, and axiomatic approaches. Conditional probability and independence are explained. The document emphasizes that probability theory allows physicians to assess diagnoses and draw conclusions about patient populations despite diagnostic uncertainties.
In this slide, variables types, probability theory behind the algorithms and its uses including distribution is explained. Also theorems like bayes theorem is also explained.
This document provides an introduction to probability theory and probability distributions. It defines key probability concepts like random experiments, outcomes, sample space, mutually exclusive and collectively exhaustive events. It then covers probability calculations, addition of probabilities, conditional probability, Bayes' theorem, and the binomial, Poisson and normal probability distributions. Several examples are provided to demonstrate applying these probability concepts.
The document discusses various statistical tests used to analyze quantitative and qualitative data, including the t-test, chi-square test, and tests of significance. It explains that the t-test is used to compare the means of two groups when the sample size is less than 30, while the z-test is used when the sample size is 30 or more. The chi-square test can be used to determine if there is an association between two variables and to compare proportions between groups. Key aspects like type I and type II errors in hypothesis testing and interpreting p-values are also summarized.
This document provides an overview of basic probability concepts and probability distributions. It defines probability as the likelihood of an event occurring between 0 and 1, and explains the roles of probability and statistics. It then covers various ways to calculate probability using classical, relative frequency and subjective approaches. Other topics discussed include events, sample spaces, conditional probabilities, the law of large numbers, rules of probability, and random variables.
The document discusses key concepts in probability, including:
- Probability is a measure of uncertainty or likelihood of outcomes ranging from 0 to 1.
- Events can be mutually exclusive, independent, or dependent. Laws of probability like addition and multiplication rules are used based on the relationship between events.
- Conditional probability is the probability of one event occurring given that another event has occurred or is known.
- Examples are given to illustrate calculating probabilities of outcomes from experiments or surveys using relative frequency definitions of probability.
The document discusses conditional probability and how odds ratios and risk ratios can be interpreted as conditional probabilities. It provides examples of how to calculate odds ratios from case-control study data and risk ratios from cohort study data. It also discusses how odds ratios from studies where the outcome is common should be interpreted as increased odds rather than risk, and how odds ratios can be converted to risk ratios using a simple formula.
This document discusses elementary probability and its applications in medicine. It provides definitions of key probability terms like chance, outcome, experiment, and event. It also covers different approaches to measuring probability, including the classical, frequentist, subjective, and axiomatic approaches. Conditional probability and independence are explained. The document emphasizes that probability theory allows physicians to assess diagnoses and draw conclusions about patient populations despite diagnostic uncertainties.
In this slide, variables types, probability theory behind the algorithms and its uses including distribution is explained. Also theorems like bayes theorem is also explained.
This document provides an introduction to probability theory and probability distributions. It defines key probability concepts like random experiments, outcomes, sample space, mutually exclusive and collectively exhaustive events. It then covers probability calculations, addition of probabilities, conditional probability, Bayes' theorem, and the binomial, Poisson and normal probability distributions. Several examples are provided to demonstrate applying these probability concepts.
This document provides an overview of key concepts in probability, including classical, frequentist, and subjective definitions of probability. It discusses sample spaces, events, mutually exclusive and independent events, and the rules of addition, multiplication, total probability, and Bayes' theorem. It also covers applications of probability, such as screening tests, and how concepts like sensitivity, specificity, and predictive values are used to evaluate screening tests using Bayesian reasoning.
Epidemiological method to determine utility of a diagnostic testBhoj Raj Singh
The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value (NPV). Many clinicians are frequently unclear about the practical application of these terms (1). The traditional method for teaching these concepts is based on the 2 × 2 table (Table 1). A 2 × 2 table shows results after both a diagnostic test and a definitive test (gold standard) have been performed on a pre-determined population consisting of people with the disease and those without the disease. The definitions of sensitivity, specificity, positive predictive value and NPV as expressed by letters are provided in Table 1. While 2 × 2 tables allow the calculations of sensitivity, specificity and predictive values, many clinicians find it too abstract and it is difficult to apply what it tries to teach into clinical practice as patients do not present as ‘having disease’ and ‘not having disease’. The use of the 2 × 2 table to teach these concepts also frequently creates the erroneous impression that the positive and NPVs calculated from such tables could be generalized to other populations without regard being paid to different disease prevalence. New ways of teaching these concepts have therefore been suggested.
Here are the calculations for the predictive values of a positive HIV test with 95% sensitivity and 98% specificity in populations with different prevalence rates:
1) Prevalence of HIV in blood donors = 2%
Total tested = 1000
With HIV = 1000 * 0.02 = 20
Without HIV = 1000 - 20 = 980
Sensitivity = 95%
Specificity = 98%
True Positives (a) = Sensitivity * With HIV = 0.95 * 20 = 19
False Positives (b) = (1 - Specificity) * Without HIV = 0.02 * 980 = 20
False Negatives (c) = (1 - Sensitivity) * With HIV = 0
Here are the calculations for the predictive values of a positive HIV test with 95% sensitivity and 98% specificity in populations with different prevalence rates:
1) Prevalence of HIV in blood donors = 2%
Total tested = 1000
With HIV = 1000 * 0.02 = 20
Without HIV = 1000 - 20 = 980
Sensitivity = 95%
Specificity = 98%
True Positives (a) = Sensitivity * With HIV = 0.95 * 20 = 19
False Positives (b) = (1 - Specificity) * Without HIV = 0.02 * 980 = 20
False Negatives (c) = (1 - Sensitivity) * With HIV = 0
Here are the answers to the selected questions from Daniel WW page 76-81:
4. P(A|B) = P(A and B)/P(B) = 0.4/0.5 = 0.8
6. P(A|B') = P(A and B')/P(B') = 0.2/0.5 = 0.4
8. P(A|C) = P(A and C)/P(C) = 0.3/0.4 = 0.75
10. P(B|A) = P(B and A)/P(A) = 0.4/0.6 = 0.667
This document provides guidance on how to read, analyze, and critique a scientific study. It discusses key concepts like the null hypothesis, statistical significance, and types of data and appropriate statistical tests. It also outlines important steps to follow when reviewing a study, including understanding the study design, evaluating the data collection and analysis, interpreting graphs and statistics, and carefully considering the discussion and conclusions. Finally, it identifies several common pitfalls to watch out for related to statistical analysis and presentation of results.
This document discusses epidemiologic measures of association used to determine the relationship between an exposure and disease. It defines key terms like association, risk, and absolute risk. It explains how to calculate measures of association like relative risk (RR) and odds ratio (OR) using data from cohort and case-control studies. The RR compares the risk of disease between exposed and unexposed groups. The OR makes similar comparisons but is used when data is from a case-control rather than cohort study. It provides examples of calculating these measures and discusses when the OR can approximate the RR.
Mathematics in Epidemiology and Biostatistics (Medical Booklet Series by Dr. ...Dr. Aryan (Anish Dhakal)
Basic mathematics needed for epidemiology and bio statistics. Slides include formulas and conceptual understanding of sensitivity, specificity, predictive values, likelihood ratios, odds, probability and many more.
Elementary Statistics Chapter 4 covers probability. Section 4.2 discusses the addition rule and multiplication rule for finding probabilities of compound events. The addition rule states that the probability of event A or B occurring is equal to the probability of A plus the probability of B minus the probability of both A and B occurring. The multiplication rule is used to find the probability of two events both occurring, which is the probability of the first event multiplied by the probability of the second event given that the first has occurred. Examples demonstrate how to use these rules to calculate probabilities of compound events.
The document discusses various study designs used in epidemiology and statistics, including observational and experimental designs. It provides details on descriptive and analytical observational studies. Descriptive studies generate hypotheses, while analytical studies allow determination of causal associations by including a comparison or control group. Experimental designs are randomized studies that can establish causal relationships. The document also covers topics like odds ratios, relative risks, attributable risks, chi-square tests, sensitivity and specificity in diagnostic testing.
This document discusses basic concepts of probability, including:
- The addition rule and multiplication rule for calculating probabilities of compound events.
- Events can be disjoint (mutually exclusive) or not disjoint.
- The probability of an event occurring or its complement must equal 1.
- How to calculate the probability of at least one occurrence of an event using the complement.
- When applying the multiplication rule, you must consider whether events are independent or dependent.
Biostatistics-MDS(Sampling techniques, Probabaility) Dr. Kanwal Preet K Gill....DrSandeepKaur4
This document discusses concepts related to sampling and populations in biostatistics. It defines key terms like population, sample, sampling unit, target population, study population, and sample. It describes different types of sampling techniques including probability sampling (simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, PPS sampling, multistage sampling, and multiphase sampling) and non-probability sampling (volunteer sampling, convenience sampling, quota sampling, snowball sampling). It also discusses concepts like sample statistic, population parameter, sampling error, and the importance of an adequate sample size.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 4: Probability
4.3: Complements and Conditional Probability, and Bayes' Theorem
This document provides an overview of probability theory, including key definitions, concepts, and calculations. It discusses:
1. Definitions of probability, including the frequency and subjective concepts. It also defines basic terminology like experiments, trials, outcomes, and events.
2. Methods of calculating probability, including classical and empirical approaches. It presents the classical probability formula.
3. Common probability distributions like the binomial distribution and normal distribution. It provides examples of calculating probabilities using these distributions.
4. Additional probability concepts like independent and conditional probability, random variables, and transformations to the standardized normal distribution.
5. The importance of the normal distribution in applications like medicine, sampling, and statistical significance testing. It
Probability is the branch of mathematics concerning events and numerical descriptions of how likely they are to occur. The probability of an event is a number between 0 and 1; the larger the probability, the more likely an event is to occur.[note 1][1][2] The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ('heads' and 'tails') are both equally probable; the probability of 'heads' equals the probability of 'tails'; and since no other outcomes are possible, the probability of either 'heads' or 'tails' is 1/2 (which could also be written as 0.5 or 50%).
These concepts have been given an axiomatic mathematical formalization in probability theory, which is used widely in areas of study such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems.
i. Probabilities range from 0 to 1 and reflect the likelihood of events occurring. Statistical inference involves making generalizations about unknown population parameters based on sample data.
ii. A probability of 0 means an event cannot occur, while 1 means it is certain. Probabilities can be expressed as proportions from 0 to 1 or percentages from 0% to 100%.
iii. Events in probability are outcomes of random processes. Simple events have a single outcome, while compound events involve probabilities of multiple outcomes or combinations of simple events.
1. The document discusses basic probability concepts including classical, relative frequency, subjective probability, and properties of probability.
2. It provides an example to calculate unconditional, conditional, and joint probabilities using a table of frequency data.
3. The multiplication rule states that joint probability can be calculated as the product of marginal and conditional probabilities.
This document provides an introduction and overview of probability concepts for a biostatistics course. It includes the instructor's background and qualifications, defines probability, provides examples of calculating probability for different scenarios, and describes the key concepts of frequentist and Bayesian probability, mutually exclusive and independent events, and the additive and multiplicative rules of probability. Examples are provided to demonstrate how to calculate probabilities for multiple events.
What is the significance of p value while reporting statistical analysis. Is there an alternate approach for Fisher, if so what is that approach. These are some of the issues addressed here.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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Similar to FALLSEM2023-24_MSM5001_ETH_VL2023240106334_2023-09-19_Reference-Material-I (3).pptx
This document provides an overview of key concepts in probability, including classical, frequentist, and subjective definitions of probability. It discusses sample spaces, events, mutually exclusive and independent events, and the rules of addition, multiplication, total probability, and Bayes' theorem. It also covers applications of probability, such as screening tests, and how concepts like sensitivity, specificity, and predictive values are used to evaluate screening tests using Bayesian reasoning.
Epidemiological method to determine utility of a diagnostic testBhoj Raj Singh
The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value (NPV). Many clinicians are frequently unclear about the practical application of these terms (1). The traditional method for teaching these concepts is based on the 2 × 2 table (Table 1). A 2 × 2 table shows results after both a diagnostic test and a definitive test (gold standard) have been performed on a pre-determined population consisting of people with the disease and those without the disease. The definitions of sensitivity, specificity, positive predictive value and NPV as expressed by letters are provided in Table 1. While 2 × 2 tables allow the calculations of sensitivity, specificity and predictive values, many clinicians find it too abstract and it is difficult to apply what it tries to teach into clinical practice as patients do not present as ‘having disease’ and ‘not having disease’. The use of the 2 × 2 table to teach these concepts also frequently creates the erroneous impression that the positive and NPVs calculated from such tables could be generalized to other populations without regard being paid to different disease prevalence. New ways of teaching these concepts have therefore been suggested.
Here are the calculations for the predictive values of a positive HIV test with 95% sensitivity and 98% specificity in populations with different prevalence rates:
1) Prevalence of HIV in blood donors = 2%
Total tested = 1000
With HIV = 1000 * 0.02 = 20
Without HIV = 1000 - 20 = 980
Sensitivity = 95%
Specificity = 98%
True Positives (a) = Sensitivity * With HIV = 0.95 * 20 = 19
False Positives (b) = (1 - Specificity) * Without HIV = 0.02 * 980 = 20
False Negatives (c) = (1 - Sensitivity) * With HIV = 0
Here are the calculations for the predictive values of a positive HIV test with 95% sensitivity and 98% specificity in populations with different prevalence rates:
1) Prevalence of HIV in blood donors = 2%
Total tested = 1000
With HIV = 1000 * 0.02 = 20
Without HIV = 1000 - 20 = 980
Sensitivity = 95%
Specificity = 98%
True Positives (a) = Sensitivity * With HIV = 0.95 * 20 = 19
False Positives (b) = (1 - Specificity) * Without HIV = 0.02 * 980 = 20
False Negatives (c) = (1 - Sensitivity) * With HIV = 0
Here are the answers to the selected questions from Daniel WW page 76-81:
4. P(A|B) = P(A and B)/P(B) = 0.4/0.5 = 0.8
6. P(A|B') = P(A and B')/P(B') = 0.2/0.5 = 0.4
8. P(A|C) = P(A and C)/P(C) = 0.3/0.4 = 0.75
10. P(B|A) = P(B and A)/P(A) = 0.4/0.6 = 0.667
This document provides guidance on how to read, analyze, and critique a scientific study. It discusses key concepts like the null hypothesis, statistical significance, and types of data and appropriate statistical tests. It also outlines important steps to follow when reviewing a study, including understanding the study design, evaluating the data collection and analysis, interpreting graphs and statistics, and carefully considering the discussion and conclusions. Finally, it identifies several common pitfalls to watch out for related to statistical analysis and presentation of results.
This document discusses epidemiologic measures of association used to determine the relationship between an exposure and disease. It defines key terms like association, risk, and absolute risk. It explains how to calculate measures of association like relative risk (RR) and odds ratio (OR) using data from cohort and case-control studies. The RR compares the risk of disease between exposed and unexposed groups. The OR makes similar comparisons but is used when data is from a case-control rather than cohort study. It provides examples of calculating these measures and discusses when the OR can approximate the RR.
Mathematics in Epidemiology and Biostatistics (Medical Booklet Series by Dr. ...Dr. Aryan (Anish Dhakal)
Basic mathematics needed for epidemiology and bio statistics. Slides include formulas and conceptual understanding of sensitivity, specificity, predictive values, likelihood ratios, odds, probability and many more.
Elementary Statistics Chapter 4 covers probability. Section 4.2 discusses the addition rule and multiplication rule for finding probabilities of compound events. The addition rule states that the probability of event A or B occurring is equal to the probability of A plus the probability of B minus the probability of both A and B occurring. The multiplication rule is used to find the probability of two events both occurring, which is the probability of the first event multiplied by the probability of the second event given that the first has occurred. Examples demonstrate how to use these rules to calculate probabilities of compound events.
The document discusses various study designs used in epidemiology and statistics, including observational and experimental designs. It provides details on descriptive and analytical observational studies. Descriptive studies generate hypotheses, while analytical studies allow determination of causal associations by including a comparison or control group. Experimental designs are randomized studies that can establish causal relationships. The document also covers topics like odds ratios, relative risks, attributable risks, chi-square tests, sensitivity and specificity in diagnostic testing.
This document discusses basic concepts of probability, including:
- The addition rule and multiplication rule for calculating probabilities of compound events.
- Events can be disjoint (mutually exclusive) or not disjoint.
- The probability of an event occurring or its complement must equal 1.
- How to calculate the probability of at least one occurrence of an event using the complement.
- When applying the multiplication rule, you must consider whether events are independent or dependent.
Biostatistics-MDS(Sampling techniques, Probabaility) Dr. Kanwal Preet K Gill....DrSandeepKaur4
This document discusses concepts related to sampling and populations in biostatistics. It defines key terms like population, sample, sampling unit, target population, study population, and sample. It describes different types of sampling techniques including probability sampling (simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, PPS sampling, multistage sampling, and multiphase sampling) and non-probability sampling (volunteer sampling, convenience sampling, quota sampling, snowball sampling). It also discusses concepts like sample statistic, population parameter, sampling error, and the importance of an adequate sample size.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 4: Probability
4.3: Complements and Conditional Probability, and Bayes' Theorem
This document provides an overview of probability theory, including key definitions, concepts, and calculations. It discusses:
1. Definitions of probability, including the frequency and subjective concepts. It also defines basic terminology like experiments, trials, outcomes, and events.
2. Methods of calculating probability, including classical and empirical approaches. It presents the classical probability formula.
3. Common probability distributions like the binomial distribution and normal distribution. It provides examples of calculating probabilities using these distributions.
4. Additional probability concepts like independent and conditional probability, random variables, and transformations to the standardized normal distribution.
5. The importance of the normal distribution in applications like medicine, sampling, and statistical significance testing. It
Probability is the branch of mathematics concerning events and numerical descriptions of how likely they are to occur. The probability of an event is a number between 0 and 1; the larger the probability, the more likely an event is to occur.[note 1][1][2] The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ('heads' and 'tails') are both equally probable; the probability of 'heads' equals the probability of 'tails'; and since no other outcomes are possible, the probability of either 'heads' or 'tails' is 1/2 (which could also be written as 0.5 or 50%).
These concepts have been given an axiomatic mathematical formalization in probability theory, which is used widely in areas of study such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems.
i. Probabilities range from 0 to 1 and reflect the likelihood of events occurring. Statistical inference involves making generalizations about unknown population parameters based on sample data.
ii. A probability of 0 means an event cannot occur, while 1 means it is certain. Probabilities can be expressed as proportions from 0 to 1 or percentages from 0% to 100%.
iii. Events in probability are outcomes of random processes. Simple events have a single outcome, while compound events involve probabilities of multiple outcomes or combinations of simple events.
1. The document discusses basic probability concepts including classical, relative frequency, subjective probability, and properties of probability.
2. It provides an example to calculate unconditional, conditional, and joint probabilities using a table of frequency data.
3. The multiplication rule states that joint probability can be calculated as the product of marginal and conditional probabilities.
This document provides an introduction and overview of probability concepts for a biostatistics course. It includes the instructor's background and qualifications, defines probability, provides examples of calculating probability for different scenarios, and describes the key concepts of frequentist and Bayesian probability, mutually exclusive and independent events, and the additive and multiplicative rules of probability. Examples are provided to demonstrate how to calculate probabilities for multiple events.
What is the significance of p value while reporting statistical analysis. Is there an alternate approach for Fisher, if so what is that approach. These are some of the issues addressed here.
Similar to FALLSEM2023-24_MSM5001_ETH_VL2023240106334_2023-09-19_Reference-Material-I (3).pptx (20)
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Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
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ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
3. Theory of probability provides the foundation of statistical inference
Theory of probability: branch of mathematics dealing with analysis of
random phenomena
Not the subject of this course
Aim to discuss the application of probability in biostats
A patient has 50-50% chances of survival; 95% recovery of patients upon
treatment
Statisticians use fractions instead of percentage
Probability in statistics
4. Event: is an outcome of an experiment/trial. For eg: getting a “head” by
tossing the coin is an event. If we are looking for that event, and if the
event is happening, it becomes a ‘successful event (success)’
Experiment/Trial: is an action to calculate the probability of occurrence of
an event. For eg: tossing the coin, rolling the dice
Mutually exclusive events: Can not occur simultaneously
Equally likely events: The chances are same for all outcomes
Independent events: No effect of the events on each other in subsequent
trials
Dependent events: The probability of one event depends on the other in
subsequent trials
Basic concepts in probability
5. Probability of a successful event (simply event…) ranges between 0 (will
not occur) to 1 (always occur)
Most biological events range between 0 and 1 (least likely to most likely)
6. “Absolute” and “No”
Absolute: Sun rises in the East. Or if I throw a stone up, it will come down
No probability: If I make a statement that this evening sun will set in the
east; the probability is zero, or no probability
7.
8. Types of probabilities
Theoretical/ Classical/ Mathematical/ Apriori probability:
We assume equal likelihood of all events
prior information is not required
Generally used to make mathematical predictions
Some established biological phenomena (Mendelian genetics)
Relative/ Statistical/ Posteriori probability:
Information is needed to know if there is mutual exclusiveness and equal
likelihood of an event (no assumptions)
Determined by actual observations and trials (many trials required first)
Revising the prior probability according to trials
Generally for statistical analyses
Most biological phenomena
9. Subjective probability:
Probability derived from an individual's personal judgment or own
experience about whether a specific outcome is likely to occur
Assign numbers from 0 to 1 (0-100%) only based upon the subjective
view/experience
It contains no formal calculations and only reflects the subject's opinions
and past experience
An example of subjective probability is a "gut instinct" when making a
trade
10. EXAMPLE
Deck of cards: 52; four sets of 13 cards each
What is the probability that I would pick a clubs king with one pick – 1 out
of 52
What is the probability that I would pick a king with one pick – 4/52=1/13
I am picking a king from a pack of cards. Without replacement, I am
picking one more king. What is the probability that I would get 1 more
king (dependent; no longer
1st pick – 1/13; 2nd pick – 3/51 = 1/17
11. Elementary properties of probability
For events that are mutually exclusive and equally likely
1. Probability of any trial with n mutually exclusive outcomes (E1, E2, ….
En) is a non-negative number
2. Sum of the probabilities of all mutually exclusive outcomes is 1
3. Addition rule – For two mutually exclusive events, Ei and Ej, the
probability of occurrence of either Ei or Ej is given by the sum of the each
of probabilities
For eg: the probability for getting 1 in rolling a dice is 1/6. probability for
getting 3 is also 1/6. then what is the probability of getting either 1 or 3
while rolling a dice is: 1/6 + 1/6 = 2/6 = 1/3
12. 4. Multiplication rule – For two mutually exclusive events, Ei and Ej, the
probability of occurrence of both Ei or Ej is given by the multiplication of
the each of probabilities
What is the probability of getting 1 and then 3 while rolling a dice on
successive turns (independent events)
P = 1/6 X 1/6 = 1/36
For two dice, what is the probability of getting 6 on one die and 1 on the
other (independent events)
P = 1/6 X 1/6 = 1/36
A bag contains 6 black marbles and 4 blue marbles; what is the probability
of drawing two blue marbles one by one successively? (dependent events)
P = 4/10 X 3/9 = 2/15
13. Addition rule: for OR (event Ei OR Ej occur)
Multiplication rule: AND (event Ei AND Ej occur)
BUT THESE RULES ARE ONLY FOR MUTUALLY EXCLUSIVE EVENTS
14. What is the probability that a random person picked up from the dataset will be
18 years or less? 141/318
Suppose we pick a random subject 18 years old or less; what is the probability
that he/she will have no family history of mood disorders? (Conditional
probability) 28/141 P(NF|18 yrs)
What is the probability that a random subject will be less than equal to 18 years
of age and have no family history of mood disorders? (Joint probability)
28/318
Apply multiplication rule; P of 18 yrs * P of (NF |18 years) = 141/318 * 28/141
OR P of NF * P of (18 years |NF) = 63/318 * 28/63
15. Bayes’ Theorem
Describes probability of an event, based upon prior knowledge of conditions
that are related to the event
For unknown/unestablished conditions or situations (drugs/diagnostic tests)
16. Take the example of evaluation of screening tests and diagnostic criteria
Bayer’s theorem will accurately predict the presence or absence of a
particular disease from the knowledge of the test results (positive or
negative) and the status of a particular symptom (present or absent)
Screening testing results: not infallible (can yield false +ve or false –ve)
A particular symptom may also be present or absent in a disease
17. Following questions must be answered in order to evaluate the usefulness
of test results and status of symptoms in determining whether or not a
person has a disease
1. P(+ve|disease)
2. P(-ve|no disease)
3. P(disease|+ve)
4. P(no disease|-ve)
18. a: no. of subjects who have the disease and show a +ve result in screening
test (true positive)
b: no. of subjects who have the disease but show a –ve test results (false
positive)
True negative: d
False negative: c
19. Probability of diseased subjects showing +ve test = true positive / total
diseased cases = a/(a+c) (Sensitivity of the test) P(+ve|disease)
Probability of normal subjects showing –ve test = true negative / total normal
cases = d/(b+d) (Specificity of the test) P(-ve|no disease)
20. Probability of diseased subjects showing +ve test = true positive / total
diseased cases = a/(a+c) (Sensitivity of the test)
P(+ve|disease) i.e. P of getting +ve results, given the person is diseased
Probability of normal subjects showing –ve test = true negative / total normal
cases = d/(b+d) (Specificity of the test)
P(-ve|no disease) i.e. P of getting –ve results, given the person is non-
diseased
21. Probability of subjects having the disease upon showing a +ve test = true
positive /all tested positive = a/(a+b)
(Predictive value positive of the test)
P(disease|+ve) i.e. P of having the disease given the result is +ve
Probability of subjects not having the disease upon showing –ve test =
true negative /all tested negative = d/(c+d)
(Predictive value negative of the test)
P(no disease|-ve) i.e. P of not having the disease given the result is -ve
22.
23. P (disease) = 0.05
P (+ve | disease) = 0.9
P (+ve | not diseased) = 0.05
Application of Bayes’ Theorem: Example
Find
P (disease | +ve) = ?
P (+ve) = ?
24. P (disease|+ve) = P (+ve|disease) * P(disease)] / P(+ve)
First find P (+ve)
All +ve results = +ve results with disease or +ve results without disease
P(+ve) = P (disease AND +ve) OR P (no disease AND +ve)
P(+ve) = P (disease) * P (+ve|diseased) OR P (no disease) * P (+ve|no disease)
= 0.05*0.9 OR (1-0.05)* 0.05 = (0.05*0.9) + (0.95*0.05)
= 0.045 + 0.0475 = 0.0925
P (disease) = 0.05; hence P(no disease) = 0.95
P (+ve | disease) = 0.9
P (+ve | not diseased) = 0.05
25. P (disease|+ve) = P (+ve|disease) * P(disease)] / P(+ve)
First find P (+ve)
All +ve results = +ve results with disease and +ve results without disease
P(+ve) = P (disease and +ve) OR P (no disease and +ve)
P(+ve) = P (disease) * P (diseased; +ve) OR P (no disease) * P (no disease; +ve)
= 0.05*0.9 OR (1-0.05)* 0.05 = (0.05*0.9) + (0.95*0.05)
= 0.045 + 0.0475 = 0.0925
P (disease|+ve) = P (+ve|disease) * P(disease)] / P(+ve)
= (0.9 * 0.05)/0.0925
= 0.4864
26. Application of Bayes’ Theorem: Example
A particular test for a person using cannabis is 90% sensitive. The test is
also 80% specific.
Assuming prevalence of 5% of cannabis usage, what is the probability that
a random person who tests +ve actually uses cannabis.
27. 90% sensitive; correctly identifies (+ve test) 90% of users = P(+ve|user)
Probability that a person is identified +ve given he is a user = 0.9
Users: either identified +ve or -ve
P(-ve|user) = 1-0.9
Probability that a person is identified -ve when (given) he is a user = 0.1
80% specific; correctly identifies (-ve test) 80% of non-users = P(-ve|nonuser)
Probability that a person is identified -ve given he is a nonuser = 0.8
Nonusers: either identified -ve or +ve
P(+ve|nonuser) = 1-0.8
Probability that a person is identified +ve when (given) he is a nonuser = 0.2
Assuming prevalence of 5% of cannabis usage, what is the probability that a
random person who tests +ve actually uses cannabis;
I.e. find P (user|+ve) or probability that a person is a user when (given) he is
tested +ve
28. Where do we get this term from?
All Positive cases = (user AND testing positive) OR (nonuser AND testing
positive)
Probability that someone tests positive is the probability that a user tests
positive into (AND) the probability of being a user (P of actual user being tested
positive)
OR the probability that a non-user tests positive into the probability of being a
non-user (P of non-user being tested positive)
29. Multiplication rule: (a) Probability of a user testing positive
(b) Probability of a non-user testing positive
Addition law: Probability of anybody (user or non-user)
testing positive
Also think in terms of total probability: only two outcomes for +ve
test: either user or non-user
32. Probability distributions (discrete variables)
Probability distribution: relationship between the values of a random
variable and the probabilities of their occurrence in a graphical or tabular
manner
Similar to frequency distribution: relationship between a random variable
and its frequencies
Probability distribution is a powerful tool for both describing a dataset and
for making predictions
33. Relative frequency of occurrence of any variable (X) to assume a
corresponding value (x)
34.
35. What is the probability that a
random selected family used 3
assistance programs?
What is the probability that a
randomly selected family used
either one or two assistance
programs?
38. What is the probability that a randomly selected family used 2 or less
assistance programs?
What is the probability that a randomly selected family used 6 or less
assistance programs?
39. What is the probability that a randomly selected family used 5 or more
assistance programs?
What is the probability that a randomly selected family used between 3
and 5 (inclusive) assistance programs?
Use more than cumulative frequency probability OR subtract from 1
40. What is the probability that a randomly selected family used 5 or more
assistance programs?
P (X ≥ 5) + P (X ≤ 4) = 1; P (X ≥ 5) = 1 – 0.6296
What is the probability that a randomly selected family used between 3
and 5 (inclusive) assistance programs?
P (X ≤ 5) is P for a family using 1-5 programs (inclusive), P (X ≤ 2) is P of a
family using less than 3 programs (1-2 programs)
P (3 ≤ X ≤ 5) = P (X ≤ 5) – P (X ≤ 2); = 0.8249 – 0.3670
41. Binomial distribution (Bernoulli’s distribution)
For dichotomous variables (two mutually exclusive outcomes only)
Coil flip
Red/White flowers
Dead or alive
Treated or untreated
Statisticians call it success or failure
One outcome is denoted as P; probability for P is denoted as p (remains
same during all trials)
Other outcome is denoted as Q, probability is q (= 1 - p)
Trials should be independent (outcome of one trial does not
influence the outcome of any other)
42. Death record for a disease; P (death) and Q (alive)
Probabilities are p and q, respectively
For a randomly selected set of five individuals with the disease, what is
the probability that we will have the sequence of PQPPQ (dead, alive,
dead, dead, alive)?
43. Death record for a disease; P (death) and Q (alive)
Probabilities are p and q, respectively
For a randomly selected set of five individuals with the disease, what is
the probability that we will have the sequence of PQPPQ (dead, alive,
dead, dead, alive)?
Apply multiplication rule (AND)
P = pqppq = p3q2
44. For a dichotomous variable, two trials only
p2+ 2pq + q2 = Ptot = 1
For three trials
p3+ 3p2q + 3pq2 + q3 = Ptot = 1
For four trials
p4 + 4p3q + 6p2q2 + 4pq3 + q4 = Ptot = 1
For event (P) to occur for x times in a total no. of trials (n), the number of
combinations is
Factorial of x: product of all numbers from x to 1, 0! =1
Binomial expansion
45. Death record for a disease; P (death) and Q (alive)
For a randomly selected set of five individuals with the disease, what is
the probability that we will have three death cases?
n = 5, x = 3
C =
No. of combinations is 10
PPPQQ, PQPQP, so on…
46. Finding frequency
The success is P and failure is Q, for simplicity, we write probability as f(x)
This expression is called the binomial distribution
f(x) = nCx * px *qn-x = 10p3q2
47. Toss coin 10 times
what is the probability that there will be 4 heads
heads: P (successful event); p = 50% = 0.5
tails: Q (failures); q = 1-0.5 = 0.5
No. of P events; n = 4
Total no. of events; x = 10
nCx = 10C4 = 10!/ [4! (10-4)!] = 3628800/(24*720) = 210
f (x) = 10C4 px q(n-x)
= 210 * (0.5)4 * (0.5)6
= 0.205
48. 14% of pregnant women admitted to a hospital are smokers
If a random sample of 10 women are selected from this population,
what is the probability that it will have 4 smokers
49. 14% of pregnant women admitted to a hospital are smokers
If a random sample of 10 women are selected from this population,
what is the probability that it will have 4 smokers
smoking women: P (successful event); p = 14% = 0.14
non-smoking women: Q (failures); q = 1-0.14 = 0.86
No. of P events; n = 4
Total no. of events; x = 10
nCx = 10C4 = 10!/ [4! (10-4)!] = 3628800/(24*720) = 210
f (x) = 10C4 px q(n-x)
= 210 * (0.14)4 * (0.86)6
= 210 * 0.00038416 * 0.404567
= 0.032 or 3.2%
50. Poisson distribution
Simeon Denis Poisson derived this
Also for discrete variables
Difference: distribution of the number of times a rare discrete event
occurs in a continuum of space and time
The same size N is infinitely large and uncountable; size of rare events is
finite and countable
Poisson distribution is used to predict very rare events in a given period
of time or space
E.g.. no. of radioactive particles emitted per unit time
No. of earthquakes per year
51. Poisson distribution is given by
e = Euler’s number (constant) =2.7183
= mean no. of events in an interval (in a given time/space)
x = no. of events
Assumptions:
The occurrence of events are independent (one occurrence does not
affect any other one)
Theoretically, an infinite no. of occurrences of the event is possible in the
interval
The probability of a single event is proportional to the length of the
interval
52. A 100 km stretch of road along a forest land was surveyed for assessing the
incidence of death of wild life due to accidents caused by heavy vehicles. The
total number of dead animals counted was 75. Calculate the probability of
finding: (a) no dead animals, (b) 1 dead animal, (c) 2 dead animals, (d) 3 dead
animals in a given km of the road. Assume one accident kills one animal only.
= mean no. of events/unit space = 75/100 = 0.75
x = 0 (for a); 1 (for b); 2 (for c); 3 (for d)
53. = mean no. of events/unit space = 75/100 = 0.75
x = 0 (for a); 1 (for b); 2 (for c); 3 (for d)
(a) f (x) = P (x) = [2.71(-0.75) * 0.750] / 0! = (0.4724 * 1)/1 = 0.4724
(b) f (x) = [2.71(-0.75) * 0.751] / 1! = (0.4724 * 0.75)/1 = 0.3543
(c) f (x) = [2.71(-0.75) * 0.752] / 2! = 0.133
(d) f (x) = [2.71(-0.75) * 0.753] / 3! = (0.4724 * 0.75)/1 = 0.0331
54. In a study of drug-induced anaphylaxis among patients, it was found that the
occurrence of anaphylaxis is 12 per year. Assuming that the model follows
Poisson distribution,
Find the probability that three subjects will experience anaphylaxis in the
next year.
= mean no. of events/unit time = 12/1 = 12
x = 3
f (x) = P (x) = [2.71(-12) * 123] / 3! = (0.000006144 * 1728)/6 = 0.00177
55. Poisson distribution is used when n is very large and p is very small
Differentiating Poisson and binomial distribution
56.
57. Distribution of continuous variables
For continuous random variables
Infinite values of the variable are possible
58.
59. Imagine that the number of the variables is very large and the widths of
the classes are very small
As the n increases to infinity and class widths approaches 0, the polygon is
converted to a curve
Such smooth curves represent the distribution of random continuous
variables graphically
Relative
frequency
Relative
frequency
60. The total area under the curve is equal to 1 as with the case with relative
frequency curve
Relative frequency (probability) of occurrence of values between any two
points on the x axis is equal to the area bound by the curve
The probability of finding a specific value is 0 (area above a point is 0)
Relative
frequency
61. Distribution of continuous variables: Normal
distribution
Most important distribution in statistics
First given by Abraham De Moivre
Carl Gauss contributed immensely in its understanding
Hence, also called Gaussian distribution
e and are constants
is mean and is standard deviation (use calculus to find these)
62. 1. It is symmetrical about its mean (mirror image on either side)
2. Mean, mode and median are all equal
3. Total area of the curve is one square unit (probability distribution)
4. Each side is 50%-50% on either side of mean
Characteristics of Normal distribution
63. 5. 1 SD distance either side of mean accommodates ~68% of the values (34%
each side)
6. 2 SD distance either side of mean accommodates 95% of the values
7. 3 SD distance either side of mean accommodates 99.7% of the values
64. 8. Normal distribution is hence completely described by the mean (central
tendency) and SD (dispersion)
9. Different values of mean() shift the distribution along the X-axis
is the location parameter
10. Different values of SD () shift the distribution along the Y-axis (flatness or
peakedness)
is the shape parameter
65. Consider normal distribution as a family, each member being determined by
the different mean and SD values
Standard (or unit) normal distribution is one member/kind of normal
distribution in which mean = 0 and SD = 1 unit