This document discusses numerical approximation and related concepts such as accuracy, precision, types of errors, and Euler's method. It provides examples of applying Euler's method to solve initial value problems by taking successive approximations with a fixed step size. The document outlines these topics and provides examples to illustrate key concepts in numerical analysis such as error propagation and different types of errors that can occur when performing calculations. It concludes by listing references used in the presentation.
1) The range, interquartile range, average deviation, variance, and standard deviation are common measures used to describe the distribution and variability of data.
2) The range is the difference between the greatest and least values in a data set. The interquartile range describes variability by looking at the spread between the first and third quartiles.
3) Variance and standard deviation both consider how far each observation is from the mean, with variance being the average of the squared deviations and standard deviation being the square root of the variance.
This document discusses measures of central tendency and variation for numerical data. It defines and provides formulas for the mean, median, mode, range, variance, standard deviation, and coefficient of variation. Quartiles and interquartile range are introduced as measures of spread less influenced by outliers. The relationship between these measures and the shape of a distribution are also covered at a high level.
Measures of dispersion describe how similar or different scores in a data set are from each other. The more similar the scores, the lower the measure of dispersion, and the more varied the scores, the higher the measure. Common measures include the range, semi-interquartile range (SIR), variance, and standard deviation. Variance is the average of the squared deviations from the mean, while standard deviation is the square root of variance. Skew and kurtosis also describe the shape and symmetry of a distribution.
This document discusses six measures of variation used to determine how values are distributed in a data set: range, quartile deviation, mean deviation, variance, standard deviation, and coefficient of variation. It provides definitions and examples of calculating each measure. The range is defined as the difference between the highest and lowest values. Quartile deviation uses the interquartile range (Q3-Q1). Mean deviation is the average of the absolute deviations from the mean. Variance and standard deviation measure how spread out values are from the mean, with variance using sums of squares and standard deviation taking the square root of variance.
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
Measures of Dispersion: Standard Deviation and Co- efficient of Variation RekhaChoudhary24
This document discusses measures of dispersion, specifically standard deviation and coefficient of variation. It begins by defining standard deviation as a measure of how spread out numbers are from the mean. It then provides the formula for calculating standard deviation and discusses its properties. Several examples are shown to demonstrate calculating standard deviation for individual data series using both the direct and shortcut methods. The document also discusses calculating standard deviation for discrete and continuous data series. It concludes by defining variance and coefficient of variation, and providing an example to calculate coefficient of variation and determine which of two company's share prices is more stable.
This document contains a quiz on statistical measures of central tendency and dispersion. It includes 40 multiple choice questions covering topics like mean, median, mode, range, standard deviation, variance, coefficient of variation, quartile deviation, and how these measures are affected by changes to the data. The questions ask about concepts like which measures use all observations, which are unaffected by outliers, and how the measures change if the data is shifted or scaled.
This document provides an overview of regression analysis. It defines regression as a statistical technique for finding the best-fitting straight line for a set of data. Regression allows predictions to be made based on correlations between two variables. The relationship between correlation and regression is examined, noting that correlation determines the relationship between variables while regression is used to make predictions. Various aspects of the linear regression equation are described, including computing predictions, graphing lines, and determining how well data fits the regression line.
1) The range, interquartile range, average deviation, variance, and standard deviation are common measures used to describe the distribution and variability of data.
2) The range is the difference between the greatest and least values in a data set. The interquartile range describes variability by looking at the spread between the first and third quartiles.
3) Variance and standard deviation both consider how far each observation is from the mean, with variance being the average of the squared deviations and standard deviation being the square root of the variance.
This document discusses measures of central tendency and variation for numerical data. It defines and provides formulas for the mean, median, mode, range, variance, standard deviation, and coefficient of variation. Quartiles and interquartile range are introduced as measures of spread less influenced by outliers. The relationship between these measures and the shape of a distribution are also covered at a high level.
Measures of dispersion describe how similar or different scores in a data set are from each other. The more similar the scores, the lower the measure of dispersion, and the more varied the scores, the higher the measure. Common measures include the range, semi-interquartile range (SIR), variance, and standard deviation. Variance is the average of the squared deviations from the mean, while standard deviation is the square root of variance. Skew and kurtosis also describe the shape and symmetry of a distribution.
This document discusses six measures of variation used to determine how values are distributed in a data set: range, quartile deviation, mean deviation, variance, standard deviation, and coefficient of variation. It provides definitions and examples of calculating each measure. The range is defined as the difference between the highest and lowest values. Quartile deviation uses the interquartile range (Q3-Q1). Mean deviation is the average of the absolute deviations from the mean. Variance and standard deviation measure how spread out values are from the mean, with variance using sums of squares and standard deviation taking the square root of variance.
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
Measures of Dispersion: Standard Deviation and Co- efficient of Variation RekhaChoudhary24
This document discusses measures of dispersion, specifically standard deviation and coefficient of variation. It begins by defining standard deviation as a measure of how spread out numbers are from the mean. It then provides the formula for calculating standard deviation and discusses its properties. Several examples are shown to demonstrate calculating standard deviation for individual data series using both the direct and shortcut methods. The document also discusses calculating standard deviation for discrete and continuous data series. It concludes by defining variance and coefficient of variation, and providing an example to calculate coefficient of variation and determine which of two company's share prices is more stable.
This document contains a quiz on statistical measures of central tendency and dispersion. It includes 40 multiple choice questions covering topics like mean, median, mode, range, standard deviation, variance, coefficient of variation, quartile deviation, and how these measures are affected by changes to the data. The questions ask about concepts like which measures use all observations, which are unaffected by outliers, and how the measures change if the data is shifted or scaled.
This document provides an overview of regression analysis. It defines regression as a statistical technique for finding the best-fitting straight line for a set of data. Regression allows predictions to be made based on correlations between two variables. The relationship between correlation and regression is examined, noting that correlation determines the relationship between variables while regression is used to make predictions. Various aspects of the linear regression equation are described, including computing predictions, graphing lines, and determining how well data fits the regression line.
This document discusses various measures of dispersion in statistics. It defines dispersion as the extent to which items in a data set vary from the central value. Some key measures of dispersion discussed include range, interquartile range, quartile deviation, mean deviation, and standard deviation. Formulas and examples are provided for calculating range, quartile deviation, and mean deviation from data sets. The objectives, properties, merits and demerits of each measure are outlined.
Chapter 11 ,Measures of Dispersion(statistics)Ananya Sharma
This document discusses various measures of dispersion, which describe how spread out or varied the values in a data set are. It describes absolute measures like range and relative measures like coefficient of range. It also discusses interquartile range, mean deviation, standard deviation, and the Lorenz curve. Quartile deviation and mean deviation are simple measures but are less accurate and reliable. Standard deviation is a more certain measure but gives more importance to extreme values. The Lorenz curve measures deviation from equal distribution for variables like income, wealth, wages and more.
This document discusses six measures of variation used to determine how values are distributed in a data set: range, quartile deviation, mean deviation, and variance. It defines each measure and provides examples of calculating them from both ungrouped and grouped data. The range is the difference between the highest and lowest values. Quartile deviation uses the interquartile range (Q3-Q1). Mean deviation is the average of the absolute deviations from the mean. Variance measures how spread out values are from the mean.
Lesson 23 planning data analyses using statisticsmjlobetos
This document discusses strategies for analyzing collected data, including descriptive and inferential statistics. Descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, standard deviation) are used to summarize and describe data. Inferential statistics like t-tests, ANOVA, and tests of correlation can analyze relationships, differences between groups, and make generalizations from samples to populations. The document provides formulas and examples of how to calculate and interpret various statistical measures.
This document discusses the key concepts and assumptions of multiple linear regression analysis. It begins by defining the multiple regression model as examining the linear relationship between a dependent variable (Y) and two or more independent variables (X1, X2, etc). It then provides an example using data on pie sales, price, and advertising spending to estimate a multiple regression equation. Key outputs from the regression analysis like coefficients, R-squared, standard error, and t-statistics are introduced and interpreted.
STANDARD DEVIATION (2018) (STATISTICS)sumanmathews
THIS IS A QUICK AND EASY METHOD TO LEARN STANDARD DEVIATION FOR DISCRETE AND GROUPED FREQUENCY DISTRIBUTION.
IT GIVES A STEP BY STEP, SIMPLE EXPLANATION OF PROBLEMS WITH FORMULAE.
SO WATCH THE ENTIRE VIDEO TODAY.
The document contains information about measures of variation and distributions, including:
1) A table showing the age distribution of Nigeria's population in 1991, with the lower quartile around 11 years, median around 24 years, and upper quartile around 40.5 years.
2) A table with test marks for 70 students, including constructing a cumulative frequency curve and determining that 28 students passed with a mark over 47.
3) Box and whisker plots are constructed to represent the goals scored in football matches by two teams, comparing their median, quartiles, and range.
4) Standard deviation is defined as a measure of spread from the mean, and the standard deviations of three data sets S1, S2,
This document discusses linear regression and bivariate analysis. It defines linear regression as a technique used when the relationship between two continuous variables is studied. Logistic regression is used when the data is categorical. The document outlines different levels of data measurement and describes the properties of the correlation coefficient r, which measures the strength and direction of the linear relationship between two variables. It also discusses using regression to estimate dependent variable values based on independent variable values and interpreting the residual plot as a diagnostic tool.
- The document discusses simple linear regression analysis and how to use it to predict a dependent variable (y) based on an independent variable (x).
- Key points covered include the simple linear regression model, estimating regression coefficients, evaluating assumptions, making predictions, and interpreting results.
- Examples are provided to demonstrate simple linear regression analysis using data on house prices and sizes.
The document discusses coefficient of variation (CV), which is the ratio of the standard deviation to the mean. It provides an example comparing the CV of two multiple choice tests with different conditions. Formulas for calculating CV by hand and in Excel are shown. Methods for finding quartiles in ungrouped and grouped data are explained. The document also demonstrates how to calculate quartile deviation and construct box and whisker plots, and provides references for further information.
This document provides an overview and outline of Chapter 14: Multiple Regression Analysis from a textbook. It discusses key concepts in multiple regression including developing multiple regression models with two or more predictors, performing significance tests on the overall model and regression coefficients, interpreting residuals, R-squared, and adjusted R-squared values, and interpreting computer output for multiple regression analyses. Examples of multiple regression problems and solutions are provided.
This document discusses the normal distribution and related concepts. It begins with an introduction to the normal distribution and its properties. It then covers the probability density function and cumulative distribution function of the normal distribution. The rest of the document discusses key properties like the 68-95-99.7 rule, using the standard normal distribution, and how to determine if a data set follows a normal distribution including using a normal probability plot. Examples are provided throughout to illustrate the concepts.
The document outlines objectives for students to learn about measures of variability, including range, mean deviation, variance, and standard deviation. It provides examples of calculating each measure for two data sets of boys' and girls' math scores. The results show the girls' data is more homogeneous, as the measures of variability are lower for the girls' data than the boys' data.
This document discusses measures of variation in data, including range, variance, and standard deviation. It provides examples of calculating these measures for both individual data points and grouped data. The key measures are:
- Range is the highest value minus the lowest value.
- Variance is the average of the squared distances from the mean.
- Standard deviation is the square root of the variance, measuring average deviation from the mean.
- Coefficient of variation allows comparison of variables with different units by expressing standard deviation as a percentage of the mean.
- Chebyshev's theorem and the empirical rule specify what proportion of data falls within a given number of standard deviations of the mean.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
Measure of dispersion part II ( Standard Deviation, variance, coefficient of ...Shakehand with Life
This tutorial gives the detailed explanation measure of dispersion part II (standard deviation, properties of standard deviation, variance, and coefficient of variation). It also explains why std. deviation is used widely in place of variance. This tutorial also teaches the MS excel commands of calculation in excel.
The document discusses various measures of variability and statistical analysis that can be used to analyze data, including range, standard deviation, z-scores, quartile deviation, and correlation. It also provides examples of how to calculate these measures, such as calculating the range by subtracting the lowest score from the highest, and how to interpret the results, like higher standard deviation indicating more variation in the data. The document also covers topics like grades, grading systems, and guidelines for effective grading.
The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population.
Statistical Analysis of the "Statistics Marks" of PGDM StudentsNivin Vinoi
The document analyzes data from 30 students' marks in a bridge course and midterm exam. It calculates measures of central tendency and dispersion for both sets of marks. A scatter plot and correlation coefficient show a weak positive relationship between bridge and midterm marks. Hypothesis testing using a t-test shows that students' midterm and bridge course marks are statistically significantly below 40%, so the null hypothesis is rejected.
R = R0(1 + α(t - 20))
- The resistance (R) of a copper wire is calculated using a formula that relates it to the resistance at 20°C (R0), the coefficient of resistance (α), and the temperature (t).
- R0 is given as 6Ω with an uncertainty of ±0.3%.
- To determine the uncertainty in R, the uncertainties in R0, α, and t must be determined and propagated through the equation using partial derivatives.
- The overall uncertainty in R combines the individual uncertainties from each variable according to the propagation of uncertainty formula.
This document provides an outline and overview of key concepts related to experimental errors and statistics. It discusses significant figures in calculations, propagation of uncertainty, measures of central tendency and spread, characterizing experimental errors, and treating random errors with statistics. Specific topics covered include calculating uncertainties, confidence intervals, normal distributions, and distinguishing between random and systematic errors. The document uses examples and sample problems to illustrate key points about analyzing and interpreting experimental data.
This document discusses various measures of dispersion in statistics. It defines dispersion as the extent to which items in a data set vary from the central value. Some key measures of dispersion discussed include range, interquartile range, quartile deviation, mean deviation, and standard deviation. Formulas and examples are provided for calculating range, quartile deviation, and mean deviation from data sets. The objectives, properties, merits and demerits of each measure are outlined.
Chapter 11 ,Measures of Dispersion(statistics)Ananya Sharma
This document discusses various measures of dispersion, which describe how spread out or varied the values in a data set are. It describes absolute measures like range and relative measures like coefficient of range. It also discusses interquartile range, mean deviation, standard deviation, and the Lorenz curve. Quartile deviation and mean deviation are simple measures but are less accurate and reliable. Standard deviation is a more certain measure but gives more importance to extreme values. The Lorenz curve measures deviation from equal distribution for variables like income, wealth, wages and more.
This document discusses six measures of variation used to determine how values are distributed in a data set: range, quartile deviation, mean deviation, and variance. It defines each measure and provides examples of calculating them from both ungrouped and grouped data. The range is the difference between the highest and lowest values. Quartile deviation uses the interquartile range (Q3-Q1). Mean deviation is the average of the absolute deviations from the mean. Variance measures how spread out values are from the mean.
Lesson 23 planning data analyses using statisticsmjlobetos
This document discusses strategies for analyzing collected data, including descriptive and inferential statistics. Descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, standard deviation) are used to summarize and describe data. Inferential statistics like t-tests, ANOVA, and tests of correlation can analyze relationships, differences between groups, and make generalizations from samples to populations. The document provides formulas and examples of how to calculate and interpret various statistical measures.
This document discusses the key concepts and assumptions of multiple linear regression analysis. It begins by defining the multiple regression model as examining the linear relationship between a dependent variable (Y) and two or more independent variables (X1, X2, etc). It then provides an example using data on pie sales, price, and advertising spending to estimate a multiple regression equation. Key outputs from the regression analysis like coefficients, R-squared, standard error, and t-statistics are introduced and interpreted.
STANDARD DEVIATION (2018) (STATISTICS)sumanmathews
THIS IS A QUICK AND EASY METHOD TO LEARN STANDARD DEVIATION FOR DISCRETE AND GROUPED FREQUENCY DISTRIBUTION.
IT GIVES A STEP BY STEP, SIMPLE EXPLANATION OF PROBLEMS WITH FORMULAE.
SO WATCH THE ENTIRE VIDEO TODAY.
The document contains information about measures of variation and distributions, including:
1) A table showing the age distribution of Nigeria's population in 1991, with the lower quartile around 11 years, median around 24 years, and upper quartile around 40.5 years.
2) A table with test marks for 70 students, including constructing a cumulative frequency curve and determining that 28 students passed with a mark over 47.
3) Box and whisker plots are constructed to represent the goals scored in football matches by two teams, comparing their median, quartiles, and range.
4) Standard deviation is defined as a measure of spread from the mean, and the standard deviations of three data sets S1, S2,
This document discusses linear regression and bivariate analysis. It defines linear regression as a technique used when the relationship between two continuous variables is studied. Logistic regression is used when the data is categorical. The document outlines different levels of data measurement and describes the properties of the correlation coefficient r, which measures the strength and direction of the linear relationship between two variables. It also discusses using regression to estimate dependent variable values based on independent variable values and interpreting the residual plot as a diagnostic tool.
- The document discusses simple linear regression analysis and how to use it to predict a dependent variable (y) based on an independent variable (x).
- Key points covered include the simple linear regression model, estimating regression coefficients, evaluating assumptions, making predictions, and interpreting results.
- Examples are provided to demonstrate simple linear regression analysis using data on house prices and sizes.
The document discusses coefficient of variation (CV), which is the ratio of the standard deviation to the mean. It provides an example comparing the CV of two multiple choice tests with different conditions. Formulas for calculating CV by hand and in Excel are shown. Methods for finding quartiles in ungrouped and grouped data are explained. The document also demonstrates how to calculate quartile deviation and construct box and whisker plots, and provides references for further information.
This document provides an overview and outline of Chapter 14: Multiple Regression Analysis from a textbook. It discusses key concepts in multiple regression including developing multiple regression models with two or more predictors, performing significance tests on the overall model and regression coefficients, interpreting residuals, R-squared, and adjusted R-squared values, and interpreting computer output for multiple regression analyses. Examples of multiple regression problems and solutions are provided.
This document discusses the normal distribution and related concepts. It begins with an introduction to the normal distribution and its properties. It then covers the probability density function and cumulative distribution function of the normal distribution. The rest of the document discusses key properties like the 68-95-99.7 rule, using the standard normal distribution, and how to determine if a data set follows a normal distribution including using a normal probability plot. Examples are provided throughout to illustrate the concepts.
The document outlines objectives for students to learn about measures of variability, including range, mean deviation, variance, and standard deviation. It provides examples of calculating each measure for two data sets of boys' and girls' math scores. The results show the girls' data is more homogeneous, as the measures of variability are lower for the girls' data than the boys' data.
This document discusses measures of variation in data, including range, variance, and standard deviation. It provides examples of calculating these measures for both individual data points and grouped data. The key measures are:
- Range is the highest value minus the lowest value.
- Variance is the average of the squared distances from the mean.
- Standard deviation is the square root of the variance, measuring average deviation from the mean.
- Coefficient of variation allows comparison of variables with different units by expressing standard deviation as a percentage of the mean.
- Chebyshev's theorem and the empirical rule specify what proportion of data falls within a given number of standard deviations of the mean.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
Measure of dispersion part II ( Standard Deviation, variance, coefficient of ...Shakehand with Life
This tutorial gives the detailed explanation measure of dispersion part II (standard deviation, properties of standard deviation, variance, and coefficient of variation). It also explains why std. deviation is used widely in place of variance. This tutorial also teaches the MS excel commands of calculation in excel.
The document discusses various measures of variability and statistical analysis that can be used to analyze data, including range, standard deviation, z-scores, quartile deviation, and correlation. It also provides examples of how to calculate these measures, such as calculating the range by subtracting the lowest score from the highest, and how to interpret the results, like higher standard deviation indicating more variation in the data. The document also covers topics like grades, grading systems, and guidelines for effective grading.
The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population.
Statistical Analysis of the "Statistics Marks" of PGDM StudentsNivin Vinoi
The document analyzes data from 30 students' marks in a bridge course and midterm exam. It calculates measures of central tendency and dispersion for both sets of marks. A scatter plot and correlation coefficient show a weak positive relationship between bridge and midterm marks. Hypothesis testing using a t-test shows that students' midterm and bridge course marks are statistically significantly below 40%, so the null hypothesis is rejected.
R = R0(1 + α(t - 20))
- The resistance (R) of a copper wire is calculated using a formula that relates it to the resistance at 20°C (R0), the coefficient of resistance (α), and the temperature (t).
- R0 is given as 6Ω with an uncertainty of ±0.3%.
- To determine the uncertainty in R, the uncertainties in R0, α, and t must be determined and propagated through the equation using partial derivatives.
- The overall uncertainty in R combines the individual uncertainties from each variable according to the propagation of uncertainty formula.
This document provides an outline and overview of key concepts related to experimental errors and statistics. It discusses significant figures in calculations, propagation of uncertainty, measures of central tendency and spread, characterizing experimental errors, and treating random errors with statistics. Specific topics covered include calculating uncertainties, confidence intervals, normal distributions, and distinguishing between random and systematic errors. The document uses examples and sample problems to illustrate key points about analyzing and interpreting experimental data.
This document provides an introduction and overview of numerical analysis. It begins by stating that numerical analysis aims to find approximate solutions to complex mathematical problems through repeated computational steps when analytical solutions are not available or practical. It then discusses that numerical analysis is important because it allows for the conversion of physical phenomena into mathematical models that can be solved through basic arithmetic operations. Finally, it explains that numerical analysis involves developing algorithms and numerical techniques to solve problems, implementing those techniques using computers, and analyzing errors in approximate solutions.
1. Numerical analysis provides approximate solutions to complex mathematical problems through repeated calculations. It is used when analytical solutions are not possible or too complex.
2. The document discusses the importance of numerical analysis in engineering and science for solving real-world problems. It also defines key concepts like errors, significant digits, and accuracy in numerical analysis.
3. Numerical methods allow finding approximate solutions to problems described by mathematical models through simple arithmetic operations. They are important when analytical solutions are not available.
Chapter 1 Errors and Approximations.pptEyob Adugnaw
This document provides an introduction to numerical methods. It discusses how numerical methods are used to find approximate solutions to problems where an exact analytical solution does not exist, such as higher order polynomial equations. Numerical methods are iterative and provide approximations that improve in accuracy with each iteration. Examples of numerical methods covered include finding the square root of a number and solving a second order polynomial equation. The document also discusses concepts such as error analysis, significant figures, rounding, and sources of numerical errors.
Diploma sem 2 applied science physics-unit 1-chap 2 error sRai University
This document discusses various types of errors that can occur in measurements. It describes instrumental error, observer error, and procedural error as the three main sources of uncertainty. It also defines accuracy as a measure of how close a measurement is to the accepted value, while precision refers to the closeness of repeated measurements. The document provides examples of calculating percentage error, relative error, and discusses significant figures when taking measurements.
This document discusses error analysis and significant figures in measurements. It defines absolute and relative errors, and explains that random errors can be estimated by taking multiple measurements and calculating their standard deviation. Systematic errors result from flaws in the measurement process. The document also provides rules for propagating errors through calculations based on measured values. Measurements should be reported with a number of significant figures consistent with their estimated error.
The document discusses experimental data and uncertainty. It explains that all data has some uncertainty due to limitations of instruments and humans. It also discusses accuracy, precision, and significant figures when reporting results. The mean, uncertainty in the mean, and fractional and percentage uncertainties are also covered.
1. Numerical methods are techniques that allow mathematical problems to be solved using arithmetic operations. They have become increasingly important for engineering problem solving.
2. A mathematical model expresses the key features of a physical system using variables and equations. It defines dependent and independent variables as well as parameters and forcing functions.
3. Sources of error in numerical solutions include truncation from approximations and round-off from limited significant figures. Relative and absolute errors are used to quantify the accuracy of solutions versus the true or analytical values.
This document defines key terms related to measurement and metrology such as accuracy, precision, sensitivity, and resolution. It provides examples of calculating average values, ranges of error, and combining measurements with associated uncertainties. The key sources of error are defined as gross, systematic, and random errors. Statistical analysis techniques like determining the arithmetic mean and standard deviation are demonstrated. The concept of probability distribution and determining proper error from standard deviation is also explained.
This document discusses measurement accuracy and precision in engineering. It introduces key concepts such as:
- Accuracy refers to how close a measurement is to the true value, while precision refers to the consistency of repeated measurements.
- Sources of error in measurements include personal errors, instrument errors, and natural errors.
- Significant figures indicate the precision of measurements based on the reliability of each digit. Calculations must be rounded according to the least precise measurement.
- Repeated measurements of the same quantity reduce random errors, following the law of error compensation. Estimating total error involves taking the square root of the number of measurements.
1. Significant figures indicate the precision of a measurement and depend on the certain and estimated digits in a number. Leading and trailing zeros can be either significant or not.
2. Numerical methods yield approximate results that may contain errors from rounding, truncation, or subtractive cancellation in calculations. It is important to determine how much error is present and if it is tolerable.
3. Numbers like pi and square roots cannot be represented exactly with a finite number of digits, introducing rounding or chopping errors when stored in computers. Using more digits improves estimates but does not eliminate error.
MEASUREMENT - TOPIC 1 (MEASUREMENT AND ERROR).pdfMohdYusri55
Here are the answers to the tutorial questions:
i. Error is the deviation of the measured value from the true value.
ii. Accuracy refers to how close a measurement is to the actual or true value. It is expressed as a percentage of the true value.
iii. Measurement is the process of obtaining values that represent a quantity by comparison with a standard unit.
iv. Precision refers to the consistency and repeatability of measurements. It indicates how close repeated measurements are to each other regardless of their accuracy.
2) Explain three of the types of error:
i. Gross error occurs due to human factors like carelessness in taking readings or selecting an improper measuring range.
ii. System
This document introduces numerical analysis and discusses floating point numbers. It covers topics such as absolute and relative errors, roundoff and truncation errors, Taylor series approximations, interpolation methods, solving nonlinear equations, numerical differentiation and integration, numerical solutions to differential equations, and linear algebra techniques. Example C programs are provided to illustrate various numerical methods.
This document discusses approximation and round-off error in engineering. It defines approximation as using an inexact value when the exact value is unknown or difficult to obtain. Approximations introduce errors from measurements in the real world. There are two main types of errors - truncation error from dropping digits during approximations, and rounding error from representing numbers with a fixed number of significant figures. The absolute error is the difference between the true and approximate values, while relative error is the percentage difference between the absolute error and true value.
This document discusses approximation and round-off error in engineering. It defines approximation as using an inexact value when the exact value is unknown or difficult to obtain. Approximations introduce errors from measurements in the real world. There are two main types of errors - truncation error from dropping digits during approximations, and rounding error from representing numbers with a fixed number of significant figures. The absolute error is the difference between the true and approximate values, while relative error is the percentage difference between the absolute error and true value. Understanding error is important for engineering applications that use numerical methods and measurements.
NUMERICA METHODS 1 final touch summary for test 1musadoto
MY FINAL TOUCH SUMMARY FOR TEST 1
ON 6TH MAY 2018
TOPICS AND MATERIALS COVERED
1. Class lecture notes (Basic concepts, errors and roots of function).
2. Lecture’s examples.
3. Past Years Examples.
4. Past Years examination papers.
5. Tutorial Questions.
6. Reference Books + web.
This chapter discusses numerical approximation and error analysis in numerical methods. It defines error as the difference between the true value being sought and the approximate value obtained. There are two main sources of error: rounding error from representing values with a finite number of digits, and truncation error from using a finite number of terms to approximate infinite expressions. The concept of significant figures is also introduced to determine the precision of numerical methods.
This chapter discusses numerical approximation and error analysis in numerical methods. It defines error as the difference between the true value being sought and the approximate value obtained. There are two main sources of error: rounding error from representing values with a finite number of digits, and truncation error from using a finite number of terms to approximate infinite expressions. The concept of significant figures is also introduced to determine the precision of numerical methods.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
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as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
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This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
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the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
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Mechanical Engineering on AAI Summer Training Report-003.pdf
Numerical approximation
1. UNIVERSITY OF GUYANA
FACULTY OF TECHNOLOGY
Department of Civil With Environmental Engineering
EMT 3200- ENGINEERING MATHEMATICS V
TOPIC: NUMERICAL APPROXIMATION
NUMERICAL APPROXIMATION
1
2. GROUP MEMBERS
Orin Edwards 1013799
Mazule Hutson 1014749
Malik Lewis 1014806
Silos Singh 1011551
Akeem St. Louis 1012535
Haresh Jaipershad 1011056
Joemoal Williams 1014049
Parmendra Persaud 1010286
2NUMERICAL APPROXIMATION
3. OUTLINE OF PRESENTATION
Introduction
Accuracy and Precision
Mistakes
Errors
Types of Errors
Error Propagation
Euler’s Method
References
3NUMERICAL APPROXIMATION
4. INTRODUCTION
• Numerical analysis is the area of mathematics and computer science that
creates, analyzes, and implements algorithms for solving numerically, the
problems of continuous mathematics.
• Such problems originate generally from real-world applications of algebra,
geometry and calculus, and they involve variables which vary continuously.
• Numerical Approximation is an inexact representation of a numerical value
that is still close enough to be useful.
4NUMERICAL APPROXIMATION
5. ACCURACY
• Accuracy refers to the closeness of a measured value to a standard or known
value.
• For example, in lab you obtain a weight measurement of 3.2 kg for a given
substance, but the actual or known weight is 10 kg.
• Your measurement is inaccurate as it is not close to the known value.
5NUMERICAL APPROXIMATION
6. PRECISION
• Precision refers to the closeness of two or more measurements to each
other.
• Using the same example, you weigh a given substance five times and get
3.2 kg
• Precision is independent of accuracy. You can be very precise but inaccurate
as well as accurate but imprecise.
• The term error represents the imprecision and inaccuracy of a numerical
computation.
6NUMERICAL APPROXIMATION
8. MISTAKES
• According to the Cambridge Dictionary, a mistake is “an action, decision, or
judgment that produces an unwanted or unintentional result”.
• In numerical analysis, a mistake is not an error.
• A mistake is due to blunder and it may be negligible, with little or no effect
on the accuracy of the calculation or it may be so serious as to render the
calculated results quite wrong.
8NUMERICAL APPROXIMATION
9. MISTAKES
Some common mistakes include:
• Misreading of repeated digits (e.g., reading 26638 as 26338).
•Transposition of digits (e.g., reading 1832 as 1382).
• Incorrectly positioning a decimal point; (e.g., placing a decimal point at
422.438 as 4224.38).
• Overlooking signs (especially near sign changes).
• Mistakes in reading the instrument, recording and tabulating data.
•Misreading of tables (for example, referring to a wrong line or a wrong
column).
9NUMERICAL APPROXIMATION
11. MISTAKES
Some ways to minimize mistakes include:
• Have an awareness of common mistakes
• Ensure signs are clearly written
• Double check calculations
11NUMERICAL APPROXIMATION
12. ERRORS
• The numerical errors are generated with the use of approximations to
represent mathematical operations and quantities.
• An error is the representation of the inaccuracy and vagueness of predictions.
• For the types of errors, the relationship between the true value or true result
and the approximate value is given by:
True Value = Approximation + error
12NUMERICAL APPROXIMATION
13. ABSOLUTE ERROR
• Is the amount of physical error in a measurement.
• Let’s say a meter stick is used to measure a given distance. The error is
rather hastily made, but it is good to ±1mm.
• This is the absolute error of the measurement. That is, absolute error =
±1mm (0.001m)
13NUMERICAL APPROXIMATION
14. RELATIVE ERROR
• This gives an indication of how good a measurement is relative to the size of the thing
being measured.
• Let’s say that two students measure two objects with a meter stick. One student
measures the height of a room and gets a value of 3.215 meters ±1mm (0.001m). Another
student measures the height of a small cylinder and measures 0.075 meters ±1mm
(0.001m).
• Clearly, the overall accuracy of the ceiling height is much better than that of the 7.5 cm
cylinder.
14NUMERICAL APPROXIMATION
15. RELATIVE ERROR
The comparative accuracy of these measurements can be determined by
looking at their relative errors.
• Relative error = absolute error / value of thing measured
• Relative error (ceiling height) = 0.001m/ 3.125m •100 = 0.0003%
• Relative error (cylinder height) = 0.001m/ 0.075m •100 = 0.01%
The relative error in the ceiling height is considerably smaller than the
relative error in the cylinder height even though the amount of absolute
error is the same in each case.
15NUMERICAL APPROXIMATION
16. ROUND OFF ERROR
• The round-off error is used because representing every number as a real
number isn't possible. So rounding is introduced to adjust for this situation.
• A round-off error represents the numerical amount between what a figure
actually is versus its closest real number value, depending on how the round
is applied.
• For instance, rounding to the nearest whole number means you round up or
down to what is the closest whole figure. In terms of numerical analysis the
round-off error is an attempt to identify what the rounding distance is when
it comes up in algorithms. It's also known as a quantization error.
16NUMERICAL APPROXIMATION
17. TRUNCATION ERROR
• A truncation error occurs when approximation is involved in numerical
analysis.
• The error factor is related to how much the approximate value is at variance
from the actual value in a formula or math result.
• For example, consider the speed of light in a vacuum. The official value is
299,792,458 m/s. In scientific notation, it is expressed as 2.99792458 x108.
Truncating it to 2 decimal places yields 2.99 x 108.
•The truncation error is the difference between the actual value and the
truncated value. This is equal to 0.00792458 x 108
or 7.92458 x 105
.
17NUMERICAL APPROXIMATION
18. NUMBER REPRESENTATION ERROR
• Number representation errors are errors that occur when numbers with
no exact value are approximated.
• For example, numbers with infinite decimal places or numbers that don’t
terminate in binary form e.g. 0.1
18NUMERICAL APPROXIMATION
19. APPROXIMATION ERROR
• The approximation error is the discrepancy between present
approximated value and the previous approximated value to it.
𝐸𝑎 = Present Approximation – Previous Approximation
19NUMERICAL APPROXIMATION
20. ERROR PROPAGATION
• Propagation of Error is defined as the effects on a function by a variable's
uncertainty.
• It is a calculus derived statistical calculation designed to combine
uncertainties from multiple variables, in order to provide an accurate
measurement of uncertainty.
20NUMERICAL APPROXIMATION
21. ADDITION OF MEASURED QUANTITIES
•If you have measured values for the quantities X, Y, and Z, with
uncertainties dX, dY, and dZ, and your final result, R, is the sum or difference
of these quantities, then the uncertainty dR is:
21NUMERICAL APPROXIMATION
𝑅 = 𝑋 + 𝑌 − 𝑍
𝛿𝑅 = 𝛿𝑋 + 𝛿𝑌 + 𝛿𝑍
𝛿𝑅 = 𝛿𝑋 2 + √ 𝛿𝑌 2
+√ 𝛿𝑍 2
22. MULTIPLICATION OF MEASURED
QUANTITIES
•In the same way as for sums and differences, we can also state the result for
the case of multiplication and division:
22NUMERICAL APPROXIMATION
𝑅 =
𝑋. 𝑌
𝑍
𝛿𝑅
𝑅
≈
𝛿𝑋
𝑋
+
𝛿𝑌
𝑌
+
𝛿𝑍
𝑍
𝛿𝑅 = 𝑅 . √
𝛿𝑋
𝑋
² + √
𝛿𝑌
𝑌
² + √
𝛿𝑍
𝑍
²
23. MULTIPLICATION WITH A CONSTANT
•If the uncertainty of an observable X is known, and it is to be multiplied by a
constant that is know exactly.
•The error can be found by multiplying the error in X by the absolute value of
the constant.
23NUMERICAL APPROXIMATION
𝑅 = 𝑐 . 𝑋
𝛿𝑅 = 𝑐 . 𝛿𝑋
24. POLYNOMIAL FUNCTIONS
•If there is a dependence of the result on the measured quantity X that is not
described by simple multiplications or additions, we state the general
answer for R as a general function of one or more variables below.
24NUMERICAL APPROXIMATION
26. GENERAL FUNCTIONS
•We can express the uncertainty in R for general functions of one or more observables.
•If R is a function of X and Y, written as R(X,Y), then the uncertainty in R is obtained by
taking the partial derivatives of R with respect to each variable, multiplication with the
uncertainty in that variable, and addition of these individual terms in quadrature.
26NUMERICAL APPROXIMATION
𝑅 = 𝑅 (𝑋, 𝑌, … )
𝛿𝑅 =
𝜕𝑅
𝜕𝑋
. 𝛿𝑋
2
+
𝜕𝑅
𝜕𝑌
. 𝛿𝑌
2
+ …
27. EULER’S METHOD
• For most first-order differential equations, it simply is not possible to find
analytic solutions, since they will not fall into the few classes for which solution
techniques are available. So our final approach to analyzing first-order
differential equations is to look at the possibility of constructing a numerical
approximation to the unique solution to the initial-value problem.
•It is important to emphasize that the Euler method does not generate a formula
for the solution to the differential equation. Rather it generates a sequence of
approximations to the value of the solution at specified points. The idea behind
Euler’s method therefore is to use the tangent line to the solution curve through
(X0, Y0) to obtain such an approximation.
NUMERICAL APPROXIMATION 27
28. EULER’S METHOD
Figure 2. Euler’s method for approximating the solution to the initial-value problem
y’= dy/dx = f(x,y), y(X0) = y0.
28NUMERICAL APPROXIMATION
30. Using the Formula for
EULER’S METHOD
• This formula gives a reasonably good approximation if we take plenty of terms, and if the
value of h is reasonably small. For Euler's Method, we take the first 2 terms only.
• We start with some known value of Y, which we could call Y0. It has this value when x = x0
• The result of using this formula is the value of Y one h step to the right of the current
value. Call it Y1.
So we have:
Y1=Y0+ hf(X0,Y0)
30NUMERICAL APPROXIMATION
31. Using the Formula for
EULER’S METHOD
Y1 is the next generated solution
Y0 is the current solution
h is the interval between steps
f(x0,Y0) is the value of the derivative at the starting point (x0,Y0)
This process is continued for as many trials as needed.
31NUMERICAL APPROXIMATION
34. EXAMPLE 1
Solution:
N.B: Y(0)=0 tells us our initial conditions for X and Y.
So Since Y(X0) = Y0
then X0= 0 and Y0= 0
So
X0= 0 Y0= 0
X1= 0+0.1= 0.1 Y1= 0+0.1 ( 0 + 2(0) )= 0
34NUMERICAL APPROXIMATION
36. EXAMPLE 2
Question:
Use Euler's method with step size 0.3 to compute the approximate y-
value y(0.9) of the solution of the initial value problem y'= X2 , y(0)= 1.
36NUMERICAL APPROXIMATION
39. References
http://homepage.divms.uiowa.edu/~atkinson/NA_Overview.pdf Date Retrieved: 2017-02-27 at 3:12pm
https://www.ncsu.edu/labwrite/Experimental%20Design/accuracyprecision.htm Date Retrieved: 2017-
02-27 at 3:24pm
https://www.slideshare.net/Mileacre/numerical-approximation-4118002 Date Retrieved: 2017-02-27 at
4:14pm
https://www.techwalla.com/articles/types-of-errors-in-numerical-analysis Date Retrieved: 2017-02-27 at
4: 26pm
http://www2.phy.ilstu.edu/~wenning/slh/Absolute%20Relative%20Error.pdf Date Retrieved: 2017-02-27
at 4:43pm
http://www.intmath.com/differential-equations/11-eulers-method-des.php Date Retrieved: 2017-02-27
at 6:05pm
39NUMERICAL APPROXIMATION