The document discusses panel data analysis and its application to analyzing competition in the UK banking sector. It summarizes:
1) Panel data has both time series and cross-sectional dimensions, allowing examination of how variables change over time for the same objects. A fixed effects model accounts for heterogeneity across objects.
2) A study analyzed competition in UK banking from 1980-2004 using a fixed effects panel data model. It tested for market equilibrium and calculated a contestability parameter to assess degree of competition.
3) The results showed some disequilibrium for the full sample, but equilibrium in sub-samples. The contestability parameter fell from 0.78 to 0.46, suggesting weaker competition over time.
The document discusses panel data analysis and its application to analyzing competition in the UK banking sector. It summarizes:
1) Panel data has both time series and cross-sectional dimensions, allowing examination of how variables change over time for the same objects. A fixed effects model accounts for heterogeneity across objects.
2) A study analyzed competition in UK banking from 1980-2004 using a fixed effects panel data model. It tested for market equilibrium and calculated a contestability parameter to indicate the degree of competition.
3) The results found evidence of equilibrium and showed the contestability parameter fell from 0.78 to 0.46, suggesting competition weakened over the period.
The document discusses simulation methods in econometrics and finance. It covers topics such as the Monte Carlo method, conducting simulation experiments by generating data and repeating experiments, random number generation, variance reduction techniques like antithetic variates and control variates, and examples of simulations in econometrics and finance including deriving critical values for Dickey-Fuller tests and pricing financial options. Bootstrapping methods are also discussed as an alternative to simulation that samples from real data rather than creating new data.
1) The document introduces the classical linear regression model, which describes the relationship between a dependent variable (y) and one or more independent variables (x). Regression analysis aims to evaluate this relationship.
2) Ordinary least squares (OLS) regression finds the linear combination of variables that best predicts the dependent variable. It minimizes the sum of the squared residuals, or vertical distances between the actual and predicted dependent variable values.
3) The OLS estimator provides formulas for calculating the estimated intercept (α) and slope (β) coefficients based on the sample data. These describe the estimated linear regression line relating y and x.
The document discusses key concepts in quantitative research methods and data analytics covered in a university course. It outlines the course content, which includes topics like data visualization, the normal distribution, and hypothesis testing. It then details the course assessments, which include a mid-term assignment and final coursework report worth 30% and 70% respectively. The final report involves selecting a topic, collecting and analyzing data using R Studio, and reporting the results in a 2000 word paper with sections on introduction, data, results, and conclusion.
This document provides an overview of how to conduct an event study in finance. It discusses why event studies are commonly used, how to define the event window and calculate abnormal returns, how to test hypotheses about the impact of events using standardized abnormal returns and cumulative abnormal returns, and how to average returns across firms and time periods. It also covers potential issues like cross-sectional dependence between firms' returns and changing return variances over the event window.
This document discusses the assumptions of the classical linear regression model (CLRM) and methods for testing violations of those assumptions. It covers the assumptions of no autocorrelation between error terms, homoscedasticity or constant error variance, and the errors being normally distributed. Tests for heteroscedasticity discussed include the Goldfeld-Quandt test and White's test. Tests for autocorrelation examined are the Durbin-Watson test and Breusch-Godfrey test. Consequences of violations include inefficient coefficient estimates and invalid inference. Potential remedies discussed are generalized least squares or using heteroscedasticity-robust standard errors.
This document discusses the assumptions of the classical linear regression model (CLRM) and methods for testing violations of those assumptions. It covers the assumptions of no autocorrelation between error terms, homoscedasticity or constant error variance, and the errors being normally distributed. Tests for heteroscedasticity discussed include the Goldfeld-Quandt test and White's test. Tests for autocorrelation examined are the Durbin-Watson test and Breusch-Godfrey test. Consequences of violations include inefficient coefficient estimates and invalid inferences. Potential remedies discussed are generalized least squares or using robust standard errors.
The document discusses panel data analysis and its application to analyzing competition in the UK banking sector. It summarizes:
1) Panel data has both time series and cross-sectional dimensions, allowing examination of how variables change over time for the same objects. A fixed effects model accounts for heterogeneity across objects.
2) A study analyzed competition in UK banking from 1980-2004 using a fixed effects panel data model. It tested for market equilibrium and calculated a contestability parameter to assess degree of competition.
3) The results showed some disequilibrium for the full sample, but equilibrium in sub-samples. The contestability parameter fell from 0.78 to 0.46, suggesting weaker competition over time.
The document discusses panel data analysis and its application to analyzing competition in the UK banking sector. It summarizes:
1) Panel data has both time series and cross-sectional dimensions, allowing examination of how variables change over time for the same objects. A fixed effects model accounts for heterogeneity across objects.
2) A study analyzed competition in UK banking from 1980-2004 using a fixed effects panel data model. It tested for market equilibrium and calculated a contestability parameter to indicate the degree of competition.
3) The results found evidence of equilibrium and showed the contestability parameter fell from 0.78 to 0.46, suggesting competition weakened over the period.
The document discusses simulation methods in econometrics and finance. It covers topics such as the Monte Carlo method, conducting simulation experiments by generating data and repeating experiments, random number generation, variance reduction techniques like antithetic variates and control variates, and examples of simulations in econometrics and finance including deriving critical values for Dickey-Fuller tests and pricing financial options. Bootstrapping methods are also discussed as an alternative to simulation that samples from real data rather than creating new data.
1) The document introduces the classical linear regression model, which describes the relationship between a dependent variable (y) and one or more independent variables (x). Regression analysis aims to evaluate this relationship.
2) Ordinary least squares (OLS) regression finds the linear combination of variables that best predicts the dependent variable. It minimizes the sum of the squared residuals, or vertical distances between the actual and predicted dependent variable values.
3) The OLS estimator provides formulas for calculating the estimated intercept (α) and slope (β) coefficients based on the sample data. These describe the estimated linear regression line relating y and x.
The document discusses key concepts in quantitative research methods and data analytics covered in a university course. It outlines the course content, which includes topics like data visualization, the normal distribution, and hypothesis testing. It then details the course assessments, which include a mid-term assignment and final coursework report worth 30% and 70% respectively. The final report involves selecting a topic, collecting and analyzing data using R Studio, and reporting the results in a 2000 word paper with sections on introduction, data, results, and conclusion.
This document provides an overview of how to conduct an event study in finance. It discusses why event studies are commonly used, how to define the event window and calculate abnormal returns, how to test hypotheses about the impact of events using standardized abnormal returns and cumulative abnormal returns, and how to average returns across firms and time periods. It also covers potential issues like cross-sectional dependence between firms' returns and changing return variances over the event window.
This document discusses the assumptions of the classical linear regression model (CLRM) and methods for testing violations of those assumptions. It covers the assumptions of no autocorrelation between error terms, homoscedasticity or constant error variance, and the errors being normally distributed. Tests for heteroscedasticity discussed include the Goldfeld-Quandt test and White's test. Tests for autocorrelation examined are the Durbin-Watson test and Breusch-Godfrey test. Consequences of violations include inefficient coefficient estimates and invalid inference. Potential remedies discussed are generalized least squares or using heteroscedasticity-robust standard errors.
This document discusses the assumptions of the classical linear regression model (CLRM) and methods for testing violations of those assumptions. It covers the assumptions of no autocorrelation between error terms, homoscedasticity or constant error variance, and the errors being normally distributed. Tests for heteroscedasticity discussed include the Goldfeld-Quandt test and White's test. Tests for autocorrelation examined are the Durbin-Watson test and Breusch-Godfrey test. Consequences of violations include inefficient coefficient estimates and invalid inferences. Potential remedies discussed are generalized least squares or using robust standard errors.
This document discusses the assumptions of the classical linear regression model (CLRM) and methods for testing for violations of those assumptions. It covers the assumptions of no autocorrelation between error terms, homoscedasticity or constant error variance, and the errors having a normal distribution. Tests for heteroscedasticity discussed include the Goldfeld-Quandt test and White's test. Tests for autocorrelation examined are the Durbin-Watson test and Breusch-Godfrey test. Consequences of violations include inefficient coefficient estimates and invalid inferences. Potential remedies discussed include transforming variables or using heteroscedasticity-robust standard errors.
This document provides an overview of time series analysis and forecasting using neural networks. It discusses key concepts like time series components, smoothing methods, and applications. Examples are provided on using neural networks to forecast stock prices and economic time series. The agenda covers introduction to time series, importance, components, smoothing methods, applications, neural network issues, examples, and references.
The document discusses key concepts in statistics and risk management including probability, sampling, measures of central tendency, dispersion, and graphical presentation of data. It covers probability distributions like Poisson and exponential that can be applied to business continuity and risk analysis. Forecasting techniques like moving average and exponential smoothing are also summarized.
Sensitivity analysis is the study of how uncertainty in the inputs of a mathematical model propagates to uncertainty in the model's outputs. It is useful for understanding relationships between inputs and outputs, identifying important inputs, and reducing uncertainty. Sensitivity analysis typically involves running the model many times while varying inputs, and calculating sensitivity measures from the resulting outputs to determine which inputs most influence uncertainty in the outputs. Common methods include variance-based approaches and screening methods.
This document discusses building technically sound simulation models in Crystal Ball. It covers:
- Common applications of simulation modeling and Crystal Ball software.
- The ModelAssist reference tool for simulation best practices.
- Key technical considerations like properly modeling multiplications as sums, distinguishing variability from uncertainty, and accounting for dependencies between variables.
- A checklist of best practices such as engaging decision-makers, keeping models simple, and clearly communicating results.
This document provides an overview of classical linear regression models. It defines regression analysis as describing the relationship between a dependent variable (y) and one or more independent variables (x). Ordinary least squares (OLS) regression fits a linear model to data by minimizing the sum of squared residuals. The OLS estimator for the slope coefficient β is derived. For the model to be estimated using OLS, it must be linear in parameters. The assumptions required for the classical linear regression model are listed.
Regression analysis is a statistical technique used to determine the relationship between variables. It allows one to quantify the strength and character of the association between a dependent variable and one or more independent variables. Regression models are used across various disciplines like finance, economics, and investing to help explain phenomena and predict outcomes.
The document discusses various concepts related to time series analysis and volatility modeling:
1) It defines volatility, risk, and the difference between the two. It also describes how volatility can be measured.
2) It covers the concepts of historical volatility, implied volatility from options prices, and volatility indices. It also defines intraday volatility.
3) It discusses the concept of stationarity in time series and various tests to check for stationarity like the Dickey-Fuller test, Phillips-Perron test, and KPSS test.
4) It introduces the ARCH and GARCH models for modeling conditional heteroscedasticity or time-varying volatility observed in financial time series.
Approaches to gather business requirements, defining problem statements, business requirements for
use case development, Assets for development of IoT solutions
1. The document introduces statistics and probability concepts relevant to engineering problems including collecting and analyzing data.
2. Key methods of collecting engineering data are retrospective studies, observational studies, and designed experiments, with advantages and disadvantages of each.
3. Statistical concepts such as populations, samples, variables, and probability are defined and related to engineering applications.
This document discusses assumptions and diagnostics of the classical linear regression model (CLR). It outlines five assumptions of the CLR model: 1) the mean of disturbance terms is zero, 2) the variance of disturbance terms is finite and constant, 3) disturbance terms are uncorrelated, 4) the X matrix is non-stochastic, and 5) disturbance terms are normally distributed. It then discusses how to test for violations of these assumptions, including heteroscedasticity using the Goldfeld-Quandt and White tests, and autocorrelation using the Durbin-Watson and Breusch-Godfrey tests. Violations of the assumptions can lead to incorrect coefficient estimates, standard errors, and test statistics.
This document discusses testing for non-stationarity and unit roots in time series data. It introduces the Augmented Dickey-Fuller (ADF) test and Phillips-Perron test for determining if a time series is integrated of order zero (I(0)), one (I(1)), or two (I(2)). The ADF test regressions the change in a variable on its lag and lags of the change to test for a unit root. If the null of a unit root is not rejected, further tests are needed to determine higher orders of integration. While ADF and Phillips-Perron tests are commonly used, their power is low if the process is near but not at the non-station
This document discusses testing for non-stationarity and unit roots in time series data. It introduces the Augmented Dickey-Fuller (ADF) test and Phillips-Perron test for determining if a time series is integrated of order zero (I(0)), one (I(1)), or two (I(2)). The ADF test regressions the change in a variable on its lag and lags of the change to test for a unit root. If the null of a unit root is not rejected, further tests are needed to determine higher orders of integration. While ADF and Phillips-Perron tests are commonly used, their power is low if the process is near but not at the non-station
This document discusses heteroscedasticity, or non-constant error variance, in regression analysis. It begins by defining heteroscedasticity and explaining how it violates assumptions of the classical linear regression model. The nature and potential causes of heteroscedasticity are then explored through various examples. The document introduces the method of generalized least squares (GLS) as a way to produce best linear unbiased estimators when heteroscedasticity is present. GLS transforms the data so that error variances are constant, allowing standard least squares to be applied. The consequences of using ordinary least squares in the presence of heteroscedasticity are then discussed.
This document outlines the steps for conducting a Bayesian analysis to estimate default probabilities using both empirical data and expert elicitation. It presents three statistical models of increasing complexity to model default, applies the analysis to Moody's corporate bond default data from 1999-2009, and elicits expert opinions to specify prior distributions. The results provide posterior distributions over model parameters and show that the data favors a lower level of default rate autocorrelation than assumed priorly. The Bayesian approach allows formal incorporation of both hard data and soft expert knowledge.
This document provides an introduction to financial econometrics. It defines econometrics as the application of statistical techniques to economic and financial problems. The key aspects of econometrics discussed include establishing mathematical models of economic theories, collecting and testing data, and using models for forecasting, prediction, and policy purposes. The document also distinguishes between econometrics and financial econometrics, noting that the latter focuses more on financial data and variables like stock and index prices and returns. It outlines some common financial data characteristics and approaches to modeling financial data.
May 2015 talk to SW Data Meetup by Professor Hendrik Blockeel from KU Leuven & Leiden University.
With increasing amounts of ever more complex forms of digital data becoming available, the methods for analyzing these data have also become more diverse and sophisticated. With this comes an increased risk of incorrect use of these methods, and a greater burden on the user to be knowledgeable about their assumptions. In addition, the user needs to know about a wide variety of methods to be able to apply the most suitable one to a particular problem. This combination of broad and deep knowledge is not sustainable.
The idea behind declarative data analysis is that the burden of choosing the right statistical methodology for answering a research question should no longer lie with the user, but with the system. The user should be able to simply describe the problem, formulate a question, and let the system take it from there. To achieve this, we need to find answers to questions such as: what languages are suitable for formulating these questions, and what execution mechanisms can we develop for them? In this talk, I will discuss recent and ongoing research in this direction. The talk will touch upon query languages for data mining and for statistical inference, declarative modeling for data mining, meta-learning, and constraint-based data mining. What connects these research threads is that they all strive to put intelligence about data analysis into the system, instead of assuming it resides in the user.
Hendrik Blockeel is a professor of computer science at KU Leuven, Belgium, and part-time associate professor at Leiden University, The Netherlands. His research interests lie mostly in machine learning and data mining. He has made a variety of research contributions in these fields, including work on decision tree learning, inductive logic programming, predictive clustering, probabilistic-logical models, inductive databases, constraint-based data mining, and declarative data analysis. He is an action editor for Machine Learning and serves on the editorial board of several other journals. He has chaired or organized multiple conferences, workshops, and summer schools, including ILP, ECMLPKDD, IDA and ACAI, and he has been vice-chair, area chair, or senior PC member for ECAI, IJCAI, ICML, KDD, ICDM. He was a member of the board of the European Coordinating Committee for Artificial Intelligence from 2004 to 2010, and currently serves as publications chair for the ECMLPKDD steering committee.
Optimal design & Population mod pyn.pptxPawanDhamala1
This document discusses optimal design and population modeling. It begins with an introduction to optimal design, noting that it allows parameters to be estimated without bias and with minimum variance. The advantages of optimal design are that it reduces experimentation costs by allowing statistical models to be estimated with fewer runs. It then describes different types of optimal designs such as A, C, D, and E optimality. The document next discusses population modeling, explaining that it is a tool for integrating data to aid drug development decisions. It notes the key components of population models are structural models, stochastic models, and covariate models. Structural models describe the response over time using algebraic or differential equations, while stochastic models describe variability and covariate models influence factors like dem
This document provides an overview of data mining concepts and techniques. It discusses topics such as predictive analytics, machine learning, pattern recognition, and artificial intelligence as they relate to data mining. It also covers specific data mining algorithms like decision trees, neural networks, and association rules. The document discusses supervised and unsupervised learning approaches and explains model evaluation techniques like accuracy, ROC curves, gains/lift curves, and cross-entropy. It emphasizes the importance of evaluating models on test data and monitoring performance over time as patterns change.
This document discusses the assumptions of the classical linear regression model (CLRM) and methods for testing for violations of those assumptions. It covers the assumptions of no autocorrelation between error terms, homoscedasticity or constant error variance, and the errors having a normal distribution. Tests for heteroscedasticity discussed include the Goldfeld-Quandt test and White's test. Tests for autocorrelation examined are the Durbin-Watson test and Breusch-Godfrey test. Consequences of violations include inefficient coefficient estimates and invalid inferences. Potential remedies discussed include transforming variables or using heteroscedasticity-robust standard errors.
This document provides an overview of time series analysis and forecasting using neural networks. It discusses key concepts like time series components, smoothing methods, and applications. Examples are provided on using neural networks to forecast stock prices and economic time series. The agenda covers introduction to time series, importance, components, smoothing methods, applications, neural network issues, examples, and references.
The document discusses key concepts in statistics and risk management including probability, sampling, measures of central tendency, dispersion, and graphical presentation of data. It covers probability distributions like Poisson and exponential that can be applied to business continuity and risk analysis. Forecasting techniques like moving average and exponential smoothing are also summarized.
Sensitivity analysis is the study of how uncertainty in the inputs of a mathematical model propagates to uncertainty in the model's outputs. It is useful for understanding relationships between inputs and outputs, identifying important inputs, and reducing uncertainty. Sensitivity analysis typically involves running the model many times while varying inputs, and calculating sensitivity measures from the resulting outputs to determine which inputs most influence uncertainty in the outputs. Common methods include variance-based approaches and screening methods.
This document discusses building technically sound simulation models in Crystal Ball. It covers:
- Common applications of simulation modeling and Crystal Ball software.
- The ModelAssist reference tool for simulation best practices.
- Key technical considerations like properly modeling multiplications as sums, distinguishing variability from uncertainty, and accounting for dependencies between variables.
- A checklist of best practices such as engaging decision-makers, keeping models simple, and clearly communicating results.
This document provides an overview of classical linear regression models. It defines regression analysis as describing the relationship between a dependent variable (y) and one or more independent variables (x). Ordinary least squares (OLS) regression fits a linear model to data by minimizing the sum of squared residuals. The OLS estimator for the slope coefficient β is derived. For the model to be estimated using OLS, it must be linear in parameters. The assumptions required for the classical linear regression model are listed.
Regression analysis is a statistical technique used to determine the relationship between variables. It allows one to quantify the strength and character of the association between a dependent variable and one or more independent variables. Regression models are used across various disciplines like finance, economics, and investing to help explain phenomena and predict outcomes.
The document discusses various concepts related to time series analysis and volatility modeling:
1) It defines volatility, risk, and the difference between the two. It also describes how volatility can be measured.
2) It covers the concepts of historical volatility, implied volatility from options prices, and volatility indices. It also defines intraday volatility.
3) It discusses the concept of stationarity in time series and various tests to check for stationarity like the Dickey-Fuller test, Phillips-Perron test, and KPSS test.
4) It introduces the ARCH and GARCH models for modeling conditional heteroscedasticity or time-varying volatility observed in financial time series.
Approaches to gather business requirements, defining problem statements, business requirements for
use case development, Assets for development of IoT solutions
1. The document introduces statistics and probability concepts relevant to engineering problems including collecting and analyzing data.
2. Key methods of collecting engineering data are retrospective studies, observational studies, and designed experiments, with advantages and disadvantages of each.
3. Statistical concepts such as populations, samples, variables, and probability are defined and related to engineering applications.
This document discusses assumptions and diagnostics of the classical linear regression model (CLR). It outlines five assumptions of the CLR model: 1) the mean of disturbance terms is zero, 2) the variance of disturbance terms is finite and constant, 3) disturbance terms are uncorrelated, 4) the X matrix is non-stochastic, and 5) disturbance terms are normally distributed. It then discusses how to test for violations of these assumptions, including heteroscedasticity using the Goldfeld-Quandt and White tests, and autocorrelation using the Durbin-Watson and Breusch-Godfrey tests. Violations of the assumptions can lead to incorrect coefficient estimates, standard errors, and test statistics.
This document discusses testing for non-stationarity and unit roots in time series data. It introduces the Augmented Dickey-Fuller (ADF) test and Phillips-Perron test for determining if a time series is integrated of order zero (I(0)), one (I(1)), or two (I(2)). The ADF test regressions the change in a variable on its lag and lags of the change to test for a unit root. If the null of a unit root is not rejected, further tests are needed to determine higher orders of integration. While ADF and Phillips-Perron tests are commonly used, their power is low if the process is near but not at the non-station
This document discusses testing for non-stationarity and unit roots in time series data. It introduces the Augmented Dickey-Fuller (ADF) test and Phillips-Perron test for determining if a time series is integrated of order zero (I(0)), one (I(1)), or two (I(2)). The ADF test regressions the change in a variable on its lag and lags of the change to test for a unit root. If the null of a unit root is not rejected, further tests are needed to determine higher orders of integration. While ADF and Phillips-Perron tests are commonly used, their power is low if the process is near but not at the non-station
This document discusses heteroscedasticity, or non-constant error variance, in regression analysis. It begins by defining heteroscedasticity and explaining how it violates assumptions of the classical linear regression model. The nature and potential causes of heteroscedasticity are then explored through various examples. The document introduces the method of generalized least squares (GLS) as a way to produce best linear unbiased estimators when heteroscedasticity is present. GLS transforms the data so that error variances are constant, allowing standard least squares to be applied. The consequences of using ordinary least squares in the presence of heteroscedasticity are then discussed.
This document outlines the steps for conducting a Bayesian analysis to estimate default probabilities using both empirical data and expert elicitation. It presents three statistical models of increasing complexity to model default, applies the analysis to Moody's corporate bond default data from 1999-2009, and elicits expert opinions to specify prior distributions. The results provide posterior distributions over model parameters and show that the data favors a lower level of default rate autocorrelation than assumed priorly. The Bayesian approach allows formal incorporation of both hard data and soft expert knowledge.
This document provides an introduction to financial econometrics. It defines econometrics as the application of statistical techniques to economic and financial problems. The key aspects of econometrics discussed include establishing mathematical models of economic theories, collecting and testing data, and using models for forecasting, prediction, and policy purposes. The document also distinguishes between econometrics and financial econometrics, noting that the latter focuses more on financial data and variables like stock and index prices and returns. It outlines some common financial data characteristics and approaches to modeling financial data.
May 2015 talk to SW Data Meetup by Professor Hendrik Blockeel from KU Leuven & Leiden University.
With increasing amounts of ever more complex forms of digital data becoming available, the methods for analyzing these data have also become more diverse and sophisticated. With this comes an increased risk of incorrect use of these methods, and a greater burden on the user to be knowledgeable about their assumptions. In addition, the user needs to know about a wide variety of methods to be able to apply the most suitable one to a particular problem. This combination of broad and deep knowledge is not sustainable.
The idea behind declarative data analysis is that the burden of choosing the right statistical methodology for answering a research question should no longer lie with the user, but with the system. The user should be able to simply describe the problem, formulate a question, and let the system take it from there. To achieve this, we need to find answers to questions such as: what languages are suitable for formulating these questions, and what execution mechanisms can we develop for them? In this talk, I will discuss recent and ongoing research in this direction. The talk will touch upon query languages for data mining and for statistical inference, declarative modeling for data mining, meta-learning, and constraint-based data mining. What connects these research threads is that they all strive to put intelligence about data analysis into the system, instead of assuming it resides in the user.
Hendrik Blockeel is a professor of computer science at KU Leuven, Belgium, and part-time associate professor at Leiden University, The Netherlands. His research interests lie mostly in machine learning and data mining. He has made a variety of research contributions in these fields, including work on decision tree learning, inductive logic programming, predictive clustering, probabilistic-logical models, inductive databases, constraint-based data mining, and declarative data analysis. He is an action editor for Machine Learning and serves on the editorial board of several other journals. He has chaired or organized multiple conferences, workshops, and summer schools, including ILP, ECMLPKDD, IDA and ACAI, and he has been vice-chair, area chair, or senior PC member for ECAI, IJCAI, ICML, KDD, ICDM. He was a member of the board of the European Coordinating Committee for Artificial Intelligence from 2004 to 2010, and currently serves as publications chair for the ECMLPKDD steering committee.
Optimal design & Population mod pyn.pptxPawanDhamala1
This document discusses optimal design and population modeling. It begins with an introduction to optimal design, noting that it allows parameters to be estimated without bias and with minimum variance. The advantages of optimal design are that it reduces experimentation costs by allowing statistical models to be estimated with fewer runs. It then describes different types of optimal designs such as A, C, D, and E optimality. The document next discusses population modeling, explaining that it is a tool for integrating data to aid drug development decisions. It notes the key components of population models are structural models, stochastic models, and covariate models. Structural models describe the response over time using algebraic or differential equations, while stochastic models describe variability and covariate models influence factors like dem
This document provides an overview of data mining concepts and techniques. It discusses topics such as predictive analytics, machine learning, pattern recognition, and artificial intelligence as they relate to data mining. It also covers specific data mining algorithms like decision trees, neural networks, and association rules. The document discusses supervised and unsupervised learning approaches and explains model evaluation techniques like accuracy, ROC curves, gains/lift curves, and cross-entropy. It emphasizes the importance of evaluating models on test data and monitoring performance over time as patterns change.
Similar to Ch9_slides.ppt chemistry lectures for UG (20)
Kadwel Sixth Form College is a school in Kaduna, Nigeria that offers advanced level academic and test preparation programmes to students seeking undergraduate or graduate admissions locally and abroad. The school provides courses and exams like IJMB, A-Levels, SAT, TOEFL, IELTS, OET, PTE, IGCSE as well as remedial and graduate exams like GRE and GMAT. Students have access to boarding facilities, classrooms, language and science labs, WiFi, and sports facilities. The curriculum is designed to be broad, balanced, relevant and personalized. Extra-curricular activities include excursions, fitness, seminars and career talks.
This document summarizes a seminar on classroom management and teaching practices for A-level teachers. The seminar addressed topics such as knowing your content expertise while acknowledging what students know, handling mistakes openly, focusing on what students need rather than want to know, engaging today's students authentically, establishing clear expectations and routines, demonstrating care for students, and advocating for them. Effective strategies discussed included meeting students at the door, setting up "Do Now" activities, working the entire classroom, maintaining an unbiased approach, facilitating group work, and enforcing policies respectfully. The seminar emphasized passion, humor, treating students with respect, and caring about their success.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
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.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
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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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
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