1. The document discusses predicting characteristics of a population from a sample, as is important in engineering experiment design.
2. It describes how experiments can be designed to evaluate quality, uniformity, and probability of occurrence for a product based on normal, Weibull, and binomial distributions.
3. The relationships between sample and population characteristics like mean and standard deviation are explored using concepts like the t-distribution and z-distribution to make predictions with a given confidence level.
The document provides an overview of the binomial distribution including its basics, prerequisites, and examples. It defines a binomial experiment as having a fixed number of independent trials where each trial results in one of two possible outcomes (success or failure) with a constant probability. The document gives examples of flipping a coin and throwing a die to illustrate binomial experiments. It also provides notation used in binomial distributions and shows how to determine if an experiment follows a binomial distribution.
Statistik 1 5 distribusi probabilitas diskritSelvin Hadi
This document discusses discrete probability distributions. It defines key terms like probability distribution, random variables, and types of random variables. It also covers calculating the mean, variance, and standard deviation of discrete probability distributions. Specific discrete probability distributions covered include the binomial, hypergeometric, and Poisson distributions. Examples are provided to demonstrate calculating probabilities and distribution properties.
Statistics and Probability-Random Variables and Probability DistributionApril Palmes
Here are the solutions to the problems:
1. a) Mean = 0.05 rotten tomatoes
b) P(x>1) = 0.03
2. a) Mean = 3.5
b) Variance = 35/12 = 2.91667
c) Standard deviation = 1.7321
3. a) Mean = $0.80
b) Variance = $2.40
4. X Probability
0 1/8
1 3/8
2 3/8
3 1/8
This document provides an overview of random variables and probability distributions. It defines discrete and continuous random variables and gives examples. Common probability distributions like the binomial, geometric, and normal distributions are introduced along with how to calculate their key properties like mean and standard deviation. Formulas for computing probabilities and combining random variables are presented.
Determining the Mean, Variance, and Standard Deviation of a Discrete Random Variable
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
This document provides an overview of random variables and probability distributions. It defines discrete and continuous random variables and gives examples of each. Discrete random variables have probabilities associated with each possible value, while continuous random variables are defined by probability density functions where the area under the curve equals the probability. The document discusses how to calculate the mean, variance and standard deviation of discrete random variables from their probability distributions. It also covers how the mean and variance are affected for linear transformations of random variables.
To get a copy of the slides for free Email me at: japhethmuthama@gmail.com
You can also support my PhD studies by donating a 1 dollar to my PayPal.
PayPal ID is japhethmuthama@gmail.com
The document provides an overview of the binomial distribution including its basics, prerequisites, and examples. It defines a binomial experiment as having a fixed number of independent trials where each trial results in one of two possible outcomes (success or failure) with a constant probability. The document gives examples of flipping a coin and throwing a die to illustrate binomial experiments. It also provides notation used in binomial distributions and shows how to determine if an experiment follows a binomial distribution.
Statistik 1 5 distribusi probabilitas diskritSelvin Hadi
This document discusses discrete probability distributions. It defines key terms like probability distribution, random variables, and types of random variables. It also covers calculating the mean, variance, and standard deviation of discrete probability distributions. Specific discrete probability distributions covered include the binomial, hypergeometric, and Poisson distributions. Examples are provided to demonstrate calculating probabilities and distribution properties.
Statistics and Probability-Random Variables and Probability DistributionApril Palmes
Here are the solutions to the problems:
1. a) Mean = 0.05 rotten tomatoes
b) P(x>1) = 0.03
2. a) Mean = 3.5
b) Variance = 35/12 = 2.91667
c) Standard deviation = 1.7321
3. a) Mean = $0.80
b) Variance = $2.40
4. X Probability
0 1/8
1 3/8
2 3/8
3 1/8
This document provides an overview of random variables and probability distributions. It defines discrete and continuous random variables and gives examples. Common probability distributions like the binomial, geometric, and normal distributions are introduced along with how to calculate their key properties like mean and standard deviation. Formulas for computing probabilities and combining random variables are presented.
Determining the Mean, Variance, and Standard Deviation of a Discrete Random Variable
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
This document provides an overview of random variables and probability distributions. It defines discrete and continuous random variables and gives examples of each. Discrete random variables have probabilities associated with each possible value, while continuous random variables are defined by probability density functions where the area under the curve equals the probability. The document discusses how to calculate the mean, variance and standard deviation of discrete random variables from their probability distributions. It also covers how the mean and variance are affected for linear transformations of random variables.
To get a copy of the slides for free Email me at: japhethmuthama@gmail.com
You can also support my PhD studies by donating a 1 dollar to my PayPal.
PayPal ID is japhethmuthama@gmail.com
Solutions. Design and Analysis of Experiments. MontgomeryByron CZ
This document summarizes solutions to problems from a chapter on simple comparative experiments. Key points include:
- Hypotheses are tested to compare means and variances of samples from two populations or processes.
- t-tests and F-tests are used to analyze differences in means and variances based on sample data.
- Confidence intervals are constructed to estimate population parameters based on sample statistics.
- Normality assumptions and sample sizes are considered in selecting appropriate statistical tests.
This document provides an overview of binomial distributions including:
- Defining binomial experiments as having fixed trials with two possible outcomes, the same probability of success each trial, and counting the number of successes.
- Noting the notation used including n trials, p probability of success, q probability of failure, and x counting successes.
- Explaining how to calculate binomial probabilities using formulas, tables, and technology.
- Discussing how to graph binomial distributions and find their mean, variance, and standard deviation.
Bernoulli and binomial random variables are used to model success/failure experiments. A Bernoulli variable represents a single trial with outcomes success (1) and failure (0). A binomial variable counts the number of successes in n independent Bernoulli trials. The probability of x successes in n trials is given by the binomial distribution. Its mean and variance can be derived. The moment generating function of the binomial distribution helps compute moments like variance.
Discrete Probability Distribution Test questions slideshareRobert Tinaro
The document contains multiple choice, fill-in-the-blank, and true/false questions about binomial distributions and discrete probability distributions. It asks the reader to calculate mean and standard deviation from a binomial distribution graph, classify random variables as discrete or continuous, determine if scenarios describe binomial experiments, and identify true statements about binomial experiments and probability distributions.
Applied statistics and probability for engineers solution montgomery && rungerAnkit Katiyar
This document is the copyright page and preface for the book "Applied Statistics and Probability for Engineers" by Douglas C. Montgomery and George C. Runger. The copyright is held by John Wiley & Sons, Inc. in 2003. This book was edited, designed, and produced by various teams at John Wiley & Sons and printed by Donnelley/Willard. The preface states that the purpose of the included Student Solutions Manual is to provide additional help for students in understanding the problem-solving processes presented in the main text.
Chapter 4 part3- Means and Variances of Random Variablesnszakir
Statistics, study of probability, The Mean of a Random Variable, The Variance of a Random Variable, Rules for Means and Variances, The Law of Large Numbers,
This document discusses discrete and continuous random variables. It begins by defining a random variable as a numerical variable that can change value between outcomes of a random experiment. Discrete random variables take on countable values, while continuous variables can take any value within a range. The document provides examples of discrete random variables like the number of scratches on a surface. It then discusses probability mass functions for discrete variables and cumulative distribution functions, which are step functions representing the probabilities of values being below a given point. Formulas for mean, variance and standard deviation are also presented.
This document discusses the central limit theorem. It states that for large sample sizes (n ≥ 30), the sampling distribution of the sample mean will be approximately normally distributed, regardless of the shape of the population distribution. It also notes that if the population is normally distributed, the sampling distribution of the sample mean will also be normally distributed. The document provides examples to demonstrate how to calculate probabilities using the normal distribution and the central limit theorem.
Probability Distributions for Discrete Variablesgetyourcheaton
This document discusses probability distributions for discrete variables. It begins by defining a probability distribution as a relative frequency distribution of all possible outcomes of an experiment. It provides examples of probability distributions for discrete variables like the binomial distribution. It discusses key aspects of probability distributions like the mean, standard deviation, and different types of distributions like binomial. It provides examples of calculating probabilities, means, and standard deviations for binomial distributions. It discusses the basic characteristics of the binomial distribution and provides an example of constructing a binomial distribution and calculating related probabilities.
random variables-descriptive and contincuousar9530
The document discusses discrete and continuous random variables and their probability mass functions (pmf), probability density functions (pdf), and cumulative distribution functions (cdf). It provides examples of discrete and continuous random variables. It defines pmf as P(X=x) and pdf as f(x)=P[(x-a/2)≤X≤(x+a/2)]/a for discrete and continuous variables respectively. The cdf is defined as F(x)=P(X≤x). It also discusses mathematical expectation (mean) as E(X)=ΣxP(x) for discrete variables and ∫xf(x)dx for continuous variables.
- The document describes Stanley Milgram's famous experiment on obedience to authority from 1963. In the experiment, participants were instructed to administer electric shocks to a learner for incorrect answers, though no actual shocks were given.
- About 65% of participants administered what they believed were severe electric shocks, showing high obedience to authority. Each participant can be viewed as a Bernoulli trial with probability of 0.35 to refuse the shock.
- The document then discusses using the binomial distribution to calculate probabilities of outcomes with a given number of trials and probability of success for each trial. It provides the formula and conditions for applying the binomial distribution.
Bayesian Variable Selection in Linear Regression and A ComparisonAtilla YARDIMCI
In this study, Bayesian approaches, such as Zellner, Occam’s Window and Gibbs sampling, have been compared in terms of selecting the correct subset for the variable selection in a linear regression model. The aim of this comparison is to analyze Bayesian variable selection and the behavior of classical criteria by taking into consideration the different values of β and σ and prior expected levels.
This document defines key concepts related to random variables including:
- A random variable is a numerical measure of outcomes from a random phenomenon.
- Probability distributions describe the probabilities associated with random variables.
- Expected value refers to the mean or weighted average of a probability distribution.
- As the number of trials increases, the actual mean approaches the true mean due to the Law of Large Numbers.
- Binomial and geometric distributions model situations with success/failure outcomes and independence between trials.
1) The document discusses hypothesis testing, which involves proposing hypotheses about population parameters and using sample data to evaluate them. It defines key concepts like null and alternative hypotheses, type 1 and 2 errors, and p-values.
2) Examples are given of common hypothesis tests involving the mean, including one-sample and two-sample tests. Tests can be one-tailed or two-tailed depending on the alternative hypothesis.
3) The general procedure for conducting a hypothesis test is outlined, including defining the population and variables, stating the hypotheses, choosing a statistical test, determining the significance level, and making a decision to reject or fail to reject the null hypothesis.
This document discusses methods for comparing two population or treatment means, including notation, hypothesis tests, and confidence intervals. Key points covered include:
1) Notation for comparing two means includes the sample size, mean, variance, and standard deviation for each population or treatment.
2) Hypothesis tests for comparing two means can use a z-test if the population standard deviations are known, or a two-sample t-test if the standard deviations are unknown.
3) Confidence intervals can be constructed for the difference between two population means using a t-distribution, assuming independent random samples of sufficient size or approximately normal populations.
This document discusses the binomial distribution and provides an example of how it can be used to calculate probabilities in a clinical drug trial. The binomial distribution requires a fixed number of independent trials with two possible outcomes. The example calculates the probability of 2 deaths out of 3 patients as 3/8.
The document discusses the hypergeometric distribution, which describes the probability of successes in draws without replacement from a finite population. It provides the formula for the hypergeometric distribution and compares it to the binomial distribution. Examples are given to demonstrate how to calculate probabilities of various outcomes using the hypergeometric distribution formula.
The document discusses sampling distributions and standard errors. It provides:
1) An explanation of sampling distributions as the set of values a statistic can take when calculated from all possible samples of a given size.
2) Formulas for calculating the mean and variance of sampling distributions.
3) A definition of standard error as the standard deviation of a sampling distribution.
4) Common standard errors formulas for statistics like the sample mean, proportion, and difference between means.
5) An example problem demonstrating calculation of the mean and standard error of a sampling distribution of sample means.
A random variable is a variable whose values are determined by the outcome of a random experiment and can be used to model probabilities. Examples of random variables include the sum of dice rolls or number of heads from coin tosses. A probability distribution assigns probabilities to each possible value of a random variable. It must satisfy the properties that probabilities are greater than or equal to 0 and sum to 1. Common probability distributions include the binomial and normal distributions.
This technical memorandum defines a generalized error function to handle the normal probability distribution in n dimensions. For even dimensions n, the error function can be written in closed form involving exponential and factorial terms. For odd dimensions, explicit integral representations are derived. Some properties of the generalized error functions are proved, including recursion formulas and relationships between different dimensional cases. Graphs of the functions are also presented to illustrate their behavior.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Solutions. Design and Analysis of Experiments. MontgomeryByron CZ
This document summarizes solutions to problems from a chapter on simple comparative experiments. Key points include:
- Hypotheses are tested to compare means and variances of samples from two populations or processes.
- t-tests and F-tests are used to analyze differences in means and variances based on sample data.
- Confidence intervals are constructed to estimate population parameters based on sample statistics.
- Normality assumptions and sample sizes are considered in selecting appropriate statistical tests.
This document provides an overview of binomial distributions including:
- Defining binomial experiments as having fixed trials with two possible outcomes, the same probability of success each trial, and counting the number of successes.
- Noting the notation used including n trials, p probability of success, q probability of failure, and x counting successes.
- Explaining how to calculate binomial probabilities using formulas, tables, and technology.
- Discussing how to graph binomial distributions and find their mean, variance, and standard deviation.
Bernoulli and binomial random variables are used to model success/failure experiments. A Bernoulli variable represents a single trial with outcomes success (1) and failure (0). A binomial variable counts the number of successes in n independent Bernoulli trials. The probability of x successes in n trials is given by the binomial distribution. Its mean and variance can be derived. The moment generating function of the binomial distribution helps compute moments like variance.
Discrete Probability Distribution Test questions slideshareRobert Tinaro
The document contains multiple choice, fill-in-the-blank, and true/false questions about binomial distributions and discrete probability distributions. It asks the reader to calculate mean and standard deviation from a binomial distribution graph, classify random variables as discrete or continuous, determine if scenarios describe binomial experiments, and identify true statements about binomial experiments and probability distributions.
Applied statistics and probability for engineers solution montgomery && rungerAnkit Katiyar
This document is the copyright page and preface for the book "Applied Statistics and Probability for Engineers" by Douglas C. Montgomery and George C. Runger. The copyright is held by John Wiley & Sons, Inc. in 2003. This book was edited, designed, and produced by various teams at John Wiley & Sons and printed by Donnelley/Willard. The preface states that the purpose of the included Student Solutions Manual is to provide additional help for students in understanding the problem-solving processes presented in the main text.
Chapter 4 part3- Means and Variances of Random Variablesnszakir
Statistics, study of probability, The Mean of a Random Variable, The Variance of a Random Variable, Rules for Means and Variances, The Law of Large Numbers,
This document discusses discrete and continuous random variables. It begins by defining a random variable as a numerical variable that can change value between outcomes of a random experiment. Discrete random variables take on countable values, while continuous variables can take any value within a range. The document provides examples of discrete random variables like the number of scratches on a surface. It then discusses probability mass functions for discrete variables and cumulative distribution functions, which are step functions representing the probabilities of values being below a given point. Formulas for mean, variance and standard deviation are also presented.
This document discusses the central limit theorem. It states that for large sample sizes (n ≥ 30), the sampling distribution of the sample mean will be approximately normally distributed, regardless of the shape of the population distribution. It also notes that if the population is normally distributed, the sampling distribution of the sample mean will also be normally distributed. The document provides examples to demonstrate how to calculate probabilities using the normal distribution and the central limit theorem.
Probability Distributions for Discrete Variablesgetyourcheaton
This document discusses probability distributions for discrete variables. It begins by defining a probability distribution as a relative frequency distribution of all possible outcomes of an experiment. It provides examples of probability distributions for discrete variables like the binomial distribution. It discusses key aspects of probability distributions like the mean, standard deviation, and different types of distributions like binomial. It provides examples of calculating probabilities, means, and standard deviations for binomial distributions. It discusses the basic characteristics of the binomial distribution and provides an example of constructing a binomial distribution and calculating related probabilities.
random variables-descriptive and contincuousar9530
The document discusses discrete and continuous random variables and their probability mass functions (pmf), probability density functions (pdf), and cumulative distribution functions (cdf). It provides examples of discrete and continuous random variables. It defines pmf as P(X=x) and pdf as f(x)=P[(x-a/2)≤X≤(x+a/2)]/a for discrete and continuous variables respectively. The cdf is defined as F(x)=P(X≤x). It also discusses mathematical expectation (mean) as E(X)=ΣxP(x) for discrete variables and ∫xf(x)dx for continuous variables.
- The document describes Stanley Milgram's famous experiment on obedience to authority from 1963. In the experiment, participants were instructed to administer electric shocks to a learner for incorrect answers, though no actual shocks were given.
- About 65% of participants administered what they believed were severe electric shocks, showing high obedience to authority. Each participant can be viewed as a Bernoulli trial with probability of 0.35 to refuse the shock.
- The document then discusses using the binomial distribution to calculate probabilities of outcomes with a given number of trials and probability of success for each trial. It provides the formula and conditions for applying the binomial distribution.
Bayesian Variable Selection in Linear Regression and A ComparisonAtilla YARDIMCI
In this study, Bayesian approaches, such as Zellner, Occam’s Window and Gibbs sampling, have been compared in terms of selecting the correct subset for the variable selection in a linear regression model. The aim of this comparison is to analyze Bayesian variable selection and the behavior of classical criteria by taking into consideration the different values of β and σ and prior expected levels.
This document defines key concepts related to random variables including:
- A random variable is a numerical measure of outcomes from a random phenomenon.
- Probability distributions describe the probabilities associated with random variables.
- Expected value refers to the mean or weighted average of a probability distribution.
- As the number of trials increases, the actual mean approaches the true mean due to the Law of Large Numbers.
- Binomial and geometric distributions model situations with success/failure outcomes and independence between trials.
1) The document discusses hypothesis testing, which involves proposing hypotheses about population parameters and using sample data to evaluate them. It defines key concepts like null and alternative hypotheses, type 1 and 2 errors, and p-values.
2) Examples are given of common hypothesis tests involving the mean, including one-sample and two-sample tests. Tests can be one-tailed or two-tailed depending on the alternative hypothesis.
3) The general procedure for conducting a hypothesis test is outlined, including defining the population and variables, stating the hypotheses, choosing a statistical test, determining the significance level, and making a decision to reject or fail to reject the null hypothesis.
This document discusses methods for comparing two population or treatment means, including notation, hypothesis tests, and confidence intervals. Key points covered include:
1) Notation for comparing two means includes the sample size, mean, variance, and standard deviation for each population or treatment.
2) Hypothesis tests for comparing two means can use a z-test if the population standard deviations are known, or a two-sample t-test if the standard deviations are unknown.
3) Confidence intervals can be constructed for the difference between two population means using a t-distribution, assuming independent random samples of sufficient size or approximately normal populations.
This document discusses the binomial distribution and provides an example of how it can be used to calculate probabilities in a clinical drug trial. The binomial distribution requires a fixed number of independent trials with two possible outcomes. The example calculates the probability of 2 deaths out of 3 patients as 3/8.
The document discusses the hypergeometric distribution, which describes the probability of successes in draws without replacement from a finite population. It provides the formula for the hypergeometric distribution and compares it to the binomial distribution. Examples are given to demonstrate how to calculate probabilities of various outcomes using the hypergeometric distribution formula.
The document discusses sampling distributions and standard errors. It provides:
1) An explanation of sampling distributions as the set of values a statistic can take when calculated from all possible samples of a given size.
2) Formulas for calculating the mean and variance of sampling distributions.
3) A definition of standard error as the standard deviation of a sampling distribution.
4) Common standard errors formulas for statistics like the sample mean, proportion, and difference between means.
5) An example problem demonstrating calculation of the mean and standard error of a sampling distribution of sample means.
A random variable is a variable whose values are determined by the outcome of a random experiment and can be used to model probabilities. Examples of random variables include the sum of dice rolls or number of heads from coin tosses. A probability distribution assigns probabilities to each possible value of a random variable. It must satisfy the properties that probabilities are greater than or equal to 0 and sum to 1. Common probability distributions include the binomial and normal distributions.
This technical memorandum defines a generalized error function to handle the normal probability distribution in n dimensions. For even dimensions n, the error function can be written in closed form involving exponential and factorial terms. For odd dimensions, explicit integral representations are derived. Some properties of the generalized error functions are proved, including recursion formulas and relationships between different dimensional cases. Graphs of the functions are also presented to illustrate their behavior.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document describes a procedure for obtaining maximum likelihood estimates of parameters for a generalized exponential distribution of earthquake intensity and magnitude. It presents the maximum likelihood parameter values for initial and extremal distributions of earthquakes in Zagreb, Croatia. Specifically:
- It describes an iterative procedure to determine the maximum likelihood values of the parameters a', k', and xo' that characterize the initial distribution, and parameters a, k, and xo that characterize the extremal distribution.
- When applied to earthquake intensity data from Zagreb, the procedure yields maximum likelihood parameter estimates of k' = 0.23820, a' = 1.9387, xo' = 1 for the initial distribution, and k = 0.
Some Unbiased Classes of Estimators of Finite Population Meaninventionjournals
This document presents a class of estimators proposed by Abu-Dayyeh et al. to estimate the mean of a variable of interest (Y) in a finite population using information from two auxiliary variables (X1 and X2). The estimators are shown to be biased. The authors then propose using a jackknife technique to create an unbiased class of estimators from the original estimators. They derive expressions for the bias and mean square error of the original and proposed jackknifed estimators. Finally, they compare the proposed jackknifed estimators to other existing estimators such as the sample mean, ratio estimator, and product estimator in terms of bias and mean square error.
Here are the solutions to the exercises:
1. The area under the standard normal curve between z=-∞ and z=2 is 0.9772 (using the standard normal table)
2. The probability that a z value will be between -2.55 and +2.55 is 0.9932 (using the standard normal table)
3. The proportion of z values between -2.74 and 1.53 is 0.9950
4. P(z ≥ 2.71) = 1 - 0.9958 = 0.0042
5. P(.84 ≤ z ≤ 2.45) = 0.8036 - 0.1967 = 0.6069
This document discusses moments, skewness, kurtosis, and several statistical distributions including binomial, Poisson, hypergeometric, and chi-square distributions. It defines key terms such as moment ratios, central moments, theorems, skewness, kurtosis, and correlation. Properties and applications of the binomial, Poisson, and hypergeometric distributions are provided. Finally, the document discusses the chi-square test for goodness of fit and independence.
I am Driss Fumio. I am a Multivariate Methods Assignment Expert at statisticsassignmentexperts.com. I hold a Master’s Degree in Statistics, from New Brunswick University, Canada. I have been helping students with their assignments for the past 14 years. I solve assignments related to Multivariate Methods. Visit statisticsassignmentexperts.com or email info@statisticsassignmentexperts.com. You can also call on +1 678 648 4277 for any assistance with Multivariate Methods Assignments.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document presents a third order shear deformation theory to analyze flexure of thick cantilever beams. The theory uses a sinusoidal function in the displacement field to account for transverse shear deformation effects through the beam thickness. Governing equations and boundary conditions are derived using the principle of virtual work. Numerical examples of a cantilever beam with a cosine load distribution are presented and displacement, stress results are obtained in non-dimensional form. The results are discussed and compared to other beam theories to demonstrate the efficiency of the third order shear deformation theory.
Use of the correlation coefficient as a measure of effectiveness of a scoring...Wajih Alaiyan
The document discusses using the correlation coefficient to measure the effectiveness of machine scoring systems compared to human scoring. It provides three applications of using the correlation coefficient: 1) Assigning machine scores that match the expected value of the human score ranking, 2) Assigning machine scores that match the expected human score within clusters of essays, 3) Using instrumental variables to estimate machine scores in a way that maximizes the correlation with human scores. The analysis shows that maximizing the correlation coefficient provides a justified way to measure scoring system effectiveness.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
- The document describes a study that uses a modified Kolmogorov-Smirnov (KS) test to test if the innovations of a GARCH model come from a mixture of normal distributions rather than a standard normal distribution.
- It establishes critical values for the KS test and modified KS (MKS) test through simulation under the null hypothesis. It then uses simulation to calculate the size and power of both tests when the innovations come from alternative distributions like the normal, Student's t, and generalized error distributions.
- The results show that the KS and MKS tests maintain the correct size when the innovations are actually from the mixture of normals. The power of both tests is greater than the nominal level when the innovations come
NANO106 is UCSD Department of NanoEngineering's core course on crystallography of materials taught by Prof Shyue Ping Ong. For more information, visit the course wiki at http://nano106.wikispaces.com.
This document presents an analysis of the exponential distribution under an adaptive type-I progressive hybrid censoring scheme for competing risks data. Maximum likelihood and Bayesian estimation methods are used to estimate the distribution parameter. Specifically, maximum likelihood estimators are derived for the exponential distribution parameter. Bayesian estimators are also obtained for the parameter based on squared error and LINEX loss functions using gamma priors. Asymptotic confidence intervals and Bayesian credible intervals are proposed. A simulation study is conducted to evaluate the performance of the estimators.
The document discusses propositions and fuzzy logic. It defines a proposition as a statement that can be either true or false but not both, and provides examples. It then explains fuzzy linguistic descriptions and fuzzy rule bases. Finally, it describes several methods for defuzzification including the centroid method, center of sums, and mean of maxima.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Statistical Computing
This document discusses various probability distributions that are important in data analytics. It begins by defining a probability distribution and giving examples of discrete probability distributions like the binomial distribution. It then discusses properties of discrete and continuous probability distributions. The document also covers specific continuous distributions like the normal, uniform, and Poisson distributions. It provides examples of calculating probabilities and distribution parameters for each type of distribution. In summary, the document presents an overview of key probability distributions and their applications in data analytics and statistics.
1) The document describes modifications made to an X-ray diffractometer to enable local strain measurements on small, curved samples like steel wires.
2) A custom sample holder was designed with adjustable angles to decouple the theta-theta scan and allow scanning at different psi angles relative to the stress directions.
3) Techniques like ray screening and X-ray capillary optics were used to confine the X-rays to small areas on the curved wire samples and enhance intensity. Measurements of residual strain on steel valve springs were then carried out with the modified diffractometer.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document discusses probability distributions for random variables. It introduces discrete distributions like the binomial and Poisson distributions which are used for counting experiments. It also introduces continuous distributions like the normal distribution which are defined over continuous ranges of values. Key concepts covered include probability density functions, cumulative distribution functions, and how to relate random variables with specific parameters to standard distributions. Examples are provided to illustrate concepts like modeling the number of plant stems in a sampling area with a Poisson distribution.
Similar to Chapter3 design of_experiments_ahmedawad (20)
1. The document discusses numerical methods for finding the roots or zeros of equations. It introduces the bisection method, which uses interval halving to bracket the root and narrow the interval containing it.
2. An example applies the bisection method to find the smallest positive root of the equation x^2 - x - 1 = 0, obtaining the approximation 1.3243 after 7 steps.
3. Notes are provided on rounding values in the bisection table and continuing iterations to achieve higher decimal place accuracy in the root. The method efficiently finds root approximations to a desired precision.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the benefits of exercise for both physical and mental health. It notes that regular exercise can reduce the risk of diseases like heart disease and diabetes, improve mood, and reduce feelings of stress and anxiety. The document recommends that adults get at least 150 minutes of moderate exercise or 75 minutes of vigorous exercise per week to gain these benefits.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Corrosion Metallurgy presents various forms of corrosion including uniform corrosion, pitting corrosion, crevice corrosion, galvanic corrosion, selective leaching, erosion corrosion, stress corrosion cracking, and intergranular corrosion. Metallurgical factors that influence corrosion rates include material properties like composition and crystal structure, microstructure features such as phases and grain boundaries, the degree and type of mechanical deformation, heat treatments applied, and the formation of passive surface layers. Understanding how these metallurgical factors impact corrosion is important for corrosion prevention and mitigation.
This document discusses powder metallurgy, including the typical process steps of metal powder production, characteristics of metal powders, compaction, sintering, and secondary operations. The key steps are producing metal powders using various methods, compacting the powder in a die to form a green compact, and sintering the compact at high temperature to bond the powder particles together without melting. Powder metallurgy allows for net-shape production of parts, uses little material waste, and can create porous or alloyed parts not possible with other methods.
This document provides an overview of welding metallurgy. It discusses the microstructure of welds and how the rapid changes in temperature during welding affect the physical characteristics and properties of metals. It examines the different zones that form in steel welds, including the fusion zone where grains are epitaxially formed, and the heat-affected zone. Problems that can occur during welding due to remelting and solidification are also summarized, such as macrosegregation, hot cracking, and cold cracking.
This dissertation examines the design of lightweight steel sandwich panels with integrated structural and thermal insulating performance for use as residential roofing. The author develops models for predicting the shear buckling strength and bearing failure of thin steel webs embedded in polymer foam cores. Finite element analysis and prototype testing are used to validate the models. Design procedures are presented for determining optimal panel geometry based on thermal performance, strength, and deflection requirements for different climate zones. Minimum weight designs are developed and compared for panels using carbon steel, stainless steel, and two-layer panel configurations.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
1. 67.
One of the important tasks in designing engineering cxpctiments is to draw
conclusions about a population from a sample. An engineer seldom knows
the characteristics of the population. In most cases such information just
does not exist. Therefore, be chooses II. sample of a limited number of items.
Such a sample mayor may not be truly representative of the whole population,
and therefore it is essential to predict the characteristics of the population
from the sample. .
Experiments can be designed around samples which would evaluate
the following characteristics: the quality of the product, the uniformity of
the product, and the probability of occurrence of a particular event associated
witr the product. The first two are the basic parameters of the normal
and Wcibull distributions. In the case of the normal distribution, the quality
is expressed by mean 1.1 and the uniformity by standard deviation (1 or variance
(12. For the Weibull distribution, the corresponding two parameters' are
the characteristic value 0 and the Wcibull slope b. The: probab.lity 0:-
occurrence of a particular event p is 1.11cbasic pzramete: -:;f the bi';~::n;G.j
distribution.
!
/3I . ,;. .:.~,!.. . . .
Experiments of Evaluation-
Mood. A. M., and F. A. GraybiJ(:·"JnttOduction·to the Theory or Statistics," 2d ed.,
McGraw·Hill Book Company;:New York,'J9Q3.: ' ':
Moroney, M. 1.: ..Facts from 'FigureS;""Pengliin Books; Inc., Baltimore, 1965.
Natrella, M. G.: £,cpcnmc:,ta.1 Statistics, Nat, Dur. Stand. Hand.-91, Aug. I, 196J.
Weibull, W.: A Statistical Representation of Fatigue 'Failures in Solids. A~I" Po;yuc;'.,
Mrch, Eng. Ser., vol, I, no. 9, 1949.
_._; A SlallsttcaJ Distribution Function of Wide Appljc.~hility. 1. App'. Mech., vel, IS.
pp. 293-297, September, J9SJ.
56 STAr:STIC/,L O.sSIGN AND ANALYSIS OF ENGINEEfW~G EXPERIMENTS
2. Graphically, this means that the probability that X2 > X;" is given h~ the
. h d f r d b···..een v1 ~-.' ecarea a: under the curve, Wit v egrets 0 rcc: onl. '. ~ - ' .,.: T Q",~ .
r'
a=.j21'
Hence. the mean of a i distribution is I' and the standard deviation is j:;;.
Tbe probability that 'r ;:::X;;. is
PC':/'> r.;,,) = r luI) dx1
1.;..
or
fIx' ) (3.4)
G'~, = J'" (r!)lj(xl) d(J.l)
, 0
0" J (xl)l( 2.. /2 - I
J
' . _'_!_!__ (,-~"2 d(x2)
... 0 2,/2 f(v/2) I,
'= 2'
(3.1)
TI1<.:vm iunce ofax.2 distribution is
5. If the, variable is distributed with mean II and variance ,;l, then
Hence
£(J.2) = JI = v
I. The experiment consists of v trials,
2. The variables XI' x 2 .... ,X, are obtained by the first, second, ... , nh
trial, respectively,
3. These variables arc independent and. normally distributed.
4. If the normal distribution has mean J.I ee 0 and variance a1 :;:: I, then
The chi-squo re (x') distribution Consider an experiment which. has
the following characteristics:
where v - number of trials = degrees of frcedolO, and r represents tbc gamma
function (Table A-J 0). Figure 3.1 shows such .a distribution. Th~re arc
many physical phenomena which have a probability distribution approximated
b 2' , ' "
y X • . 2 • . , •
111C expected value £(:/) of ax distribution IS
E(:r) = ( (xl)j(J.2) dX1
...I( l),/l
=f X C-x"ld(xl)
• () 2V/2r(v/2)
l. f(I'J2 + I)
'" 2·/lr(v/2) +<.,,/2+ 1)
2,/2+1 (1'/2)r(vj2)
._-- ='
- 2"1 r('/2)
x2 = sum of squares of random variable
(3.2)
!
3.1.1 ESTIMATE OF THE lJNIFORMrrY OF A PRODUCT
In many engineering problems the proper functioniog of the product is
determined by the uniformity of the characteristics of (he product. A test
therefore is rUII, sample standard deviation s is determined. and from this
the population standard deviation (J is predicted. In these cases, therefore.
it is desirable to know the relation between sand a. This relation Ciln be
derived from the Xl distribution. -
Tbe ./ is another random variable which has a '/ distribution, and its density
function is3.1 NORMAL DISTRIBUTION
69u:~,[R1,....1("'1:; 0 F EVALU, no«STp. TIS~ICAL :; t 51Gl AN :)' ANALYSIS Of ENG:~,E oRING EXFEnIV." '~TS6S
/
3. SO/III;OIl
x = 8.254 X )0-' in s=» 1.94 x 10-l in [from Eq. {1.I4»)
Y~-1l-1 v=IO-I~9(3.5)with v = n -- I
with degrees of freedom ,. equal to n - 1.
The two quantities s· and r? are related by the equation
Dctc~m;nc the standard deviation of the ......ear of the population from whi:h this
sample was llke!l, at ?O. 95, and 99 percent confidence. .
when mean ::: J.I and variance = 0'2, Therefore,
8.05 5.0
9.1 8.S3
6.2 6.8
7.S 9.6
10.3 11.46
Wear, 0.001 ;rt
r I0 h I removed after n cert ain U!'C, were
ExamQll' 3,1 Friction liningS rom W ces. -
- measured fOi wear with the following results:
(3.6)( .
Prediction of the population variance from the sample variance
The following derivation is based on a general case of 1 random variable
when distributed normally with mean J.l. and variance 0'2. From Eq, (1.14),
when x is assumed to be equal to p ;
2 I(x - 11)2
S = =-"----'-
n,- I
where n is the sample size and n - I arc the degrees of freedom (sec Sec. 1.12)
for unbiased estimate of variance. From Eq. (3.1).
From Table A.2, IX is given as 0.62, er 2 percent.
From fig. 3.3:
P{~2 ..- 2.r ,,2 ) - 1 - 0:
A'(I-ct'l);v'::'): ~""l;. - .
p[xt1-.,1IIY s (n - 1) ;::S1.;/2;.] '" 1 - 0:
or the confidence interval for the population standard deviation a is
This is shown in Fig. 3.2. The distribution starts at zero and therefore is
skewed to the right. These probabilities are given in Table A-2 for tlirrcrcnt
values of 0: and I'. For example, if I = 2 and ,;;. => 7.824, then
, 3 3 'I'he (I - ox)cor.fl(.ko~ interval fc~rn X' distributioll.F,g. .
Fig. 3.2 The mc:Irung.r X:,•.
x' 0;'o
I,
Ij(X' )
"drytt1 of freedom
7~
'...<?ERI:....'.r.N;S 0;: F...',...i..~.I/...TlCN
STATISTICAL C~SIGt; AND ANALYSIS OF ENGIN(CflING o(PEfUMCNTS70
4. t .
(
nJl
=-=.:/l
n
_.
I
':;'- (p + II + ... + II)
II
J I 1
= - £(x,) + - £(Xl) + ... + -E(x,.)
n n' II
(I I I )
E(i)=E -Xl+-X.+···+-x.
Vf If n
Hence, x can be considered a new variable which is the linear combination
of independent random variables x,, X2' •• , x•. The expected value of this
variable is
X, X2 X.
o:! -+-+ ...+-
11 II n
... r.
_ :<:+X2+X3+···+X•
x,. .cc . ..,.';'_--"---"--
Relationship between the distributions of x and i Consider a
lest conducted with the following sample of II items, XII Xl •••. ' X.' Then
In order to predict the population mean J.I from the sample mean X. for
both cases it is necessary first 10 establish rhe relationship between the dis.
tribution of the variable x and of x.
l~ yr the standard deviation of th'c population o is known, in which case the
~. sample size 'chosen may be small: or if a is not known, in which case
sample size must be large (» 30) so that the standard deviation of the
sample s is approximately equal to the standard deviation of U1C
population rJ-in these cases use the z -distribuuon (Subsec. 2.1.1).
yt: If the standard deviation of the population (J is-not known and the sample
~ 'J size is small (< 30), use the t distribution (discussed later in this section).
3.1.Z 'ESTIMATE OF THE QUALITY OF A 'PRODUCT
. 'Probably the most important characteristic of It product is its quality, which
statistically can be best expressed by the mean value. Since the mean of
tr;e population J1 is seldom known, a test is run on a sample taken from this
population, and l~~ sample mean :x: is determined. From x the value of J.I
is then predicted. To make this prediction. it is essential to establish the
relation between X. and /1. This relation is established here for the following
cases:j
EXPERIMENT:; OF llALUATlON
1.22 X 10-' in :$ C1 ~ 4 . 52 x 10-' in
and f01' 99 percent confidence:
Hence, it an be concluded with 90 percent confidence that the standard deviation
of-wear of the population of the lining i.~.o.minimum of 1.43 x 10-> in and a maxi-
mum of 3.24 x IO-J in. Similarly. {or.9S percent confidence:
1.43 x 10-> in :S 0' :S J.24 )( 10-) in
or
Therefore
/(10 - 1)(1.94') )( 10·< sC1 sJ(IO - 1)(I.'14'} x 10--
'" 16.919 3.325
I 33.8 '" C' J3l.810·' x -- ;:,a;;:, -- X 10-'
". 16.919 3 .32.5
For W pc",cnt conrideoce:
,--- ,---
J (n - 1)1' '(It - I}s'
-,-- ~a$ I-,-,-;__
X./ll" "v' X(I-"1)I.
From Eq. (3.6):
xl" I' = ,f.,OO'" - 23.589
ex
1- 2 =0.995
ex
2 - 0.005
For 99 percent confidence:
X!1l I" cr X~,OJ':o .. 19,023
(I
1 - ~ -A 0.975
-' .
ex
'2 ~ 0.025
For 95 percent confidence:
x;.-.",:. = ,f..9J,. - ~.325
;,
ex
1 ~ 2 = 0.95
..'2 = O.OS
For 90 r<:rccnt confidence:
STI:-ISTlCAl C~SlGN ANO ,NALysrs 0;:' (NC;N~Ef!lNG [XPERI.1tNr~72
j
5. a: = 0.05J - IX - 0.95
IX
2 ~ 0.02.5n = 36
Soluuon
With 95 percent confidence. predict the true (population) mean value of the resistance.
s ~ 4.2 ill ;;;(1..1'=20 )(11-£:xample 3.2 Asample of 36 resistors was tested. and the following data were: obtained:
.,f ••
!
Prediction of tho poputatlon mean from the sample mean by
using z distribution As pointed out before, z distribution applies to the
situations when the standard deviation of the population a is known, in which
case the sample .size rnay .bc small; or if a is oot known, ·the sample size
must be over ~O.
Substituting Eq. (3.12) into Eq. (3.10),
p( -Z0/2 ~ :,In.$Z."l) ""I - a (3.13)
II will be recalled that 'this is applicable when the sample size is large (over 30).
For smaller sample sizes, I distribution •. discussed later in this section,
should be used, .
(l,-1~)
. X-I-'
z = <J/.J~
This equation is used for the prediction of the population mean from the
sample mean.
With the aid or [q. (3.'7) this becomes,
.(3.11)
In the present case, the variable under consideration.is x instead of x; there-
fore z can be defined as .
X-Jl
z=--.-.
(3.10)·
Of', in general,
and the probability that x is larger than the predetermined value b was
defined as
PCr >b) ~ l'(z > b : II) 7a
I
7"
J
I
I,.~
.~
j
I
I
)
Fig, 3.4 Relation between the distributions of SAmple mean x and toe
variable x,
Oi",lbullon or.f "
/ (SI"nd",dd••I.,lon. -::-1
. V"
Diudbution of .t
(Standard d.·bliun ~ 0)
.~.~
i ~..~ r.
-e ~
'" ...
2
z=x-p
a
The z distribution In Eqs. (2.2) and (2.4), the variable was).' and the
standardized normal variate }. was defined as
Figure 3.4 shows the relation between the distribution of x and the.
dist~ibution of x,
(3.9)
(3.8)
s
$;=7
....;n
£(x) r.: J.I.
Similarly,
0"" therefore. is t hc standard deviation of the variable X. It is frequently
referred to as the standard error of the mean. Thus
(3.7)
or
I 2
'" - (11(1 )
III
(
<7) 1 (v J (<J)2
= -: + -) + ... + -
h n n
In a silr.ilJJ[ ::)311,IC[ the variance of x is given by
~ 2 _ 2 2! 2 2 '1 '1 1
v s n. G1 (71 -. (/1 <71 + Q,) 17J + ... + 0. <7"
74
6. '_"
Fig. 3.6 Meaning of c.:
Prediction of the population mean from the sarnpte mean by
usi~g I distribu~.ion It has been shown before that when X,. Xl, •.•• :".
arc Independent and normally distributed with mean J1 and varia nee (Jl. the
variable
!
I
.[
I,
I
I
I
!
Values of c ' arc given for VIti ious values of I. in Table A-J. It is. . .f;. ,
mtcrcsung to no re that the values of :x for I.;. when ' = CO are the same as
the values' of :x given in Table A-I for the z distribution. This follows from
the fact that as I' _, eo the I distribution asymptotically approaches a normal
distribution. The tabulation of :x is made only up to ..... 500. This is
because :x does not change appreciably with changes in ' above 500. For
example. to find the probability that a variable with t distribution has a
value greater than or equal to. 2.787. given that v = 25. Ptt > I.:.) = «.
Then, frOJT; Table 1·3 for . ee 25 and to;. "" 2.787. r.J. = 0.005 = required
probability.
o
Fig. 3.5 Density plOI of I distribution.
I
I
I
.I.
·1
I
((II
I
77EXPEfUM~NT$ OF EViLUATION
,
I !
for ' = 2
and the variance of a t distribution is
.-"
for v >£(1) = (' (f(l) d: = 0
. i z distribution extends symmetrically from - c.o to + oc whereas a
X2 distribution extends from 0 to + cc. Hence." I distribution also extends
symmetrically from - 00 to + 00. Figure 3.5 shows a plot of j(l} versus I
From this figure it is seen thai as v - co , a I distribution tends to be" normal
distribution.
The expected value of a I distribution is
-OO<I<cr. (3.14)
X
jl.~'.
has a I distr ibution wit h v degrees of !reedom.
Such a distriburion has II frequency distributio« function
1 f[(I'+I)!2J( (!)-C''''1:2
J(I) = -:-= I +-
J;rl' r(I'J2} v,
The t distribution /distribution is used for the prediction of the popula-
tion mean II from the sample mean x when the population standard deviation
a is not known and the sample size is small. The I distribution is mathc-
rnatically defined as follows:
l . If Xl' Xl' " ', x. arc normally distributed random variables with zero
mean and unit variance, and
2. Xl is a random variable' independent of x and having 2 -; distribution
with ,. degrees of freedom. then the random variable
Therefore. with a 95 percent confidence levcl.jhc true rncan value j.J. or the rtsis'~:1ce
is between J 8.63 and 21.37 kO.
'I'[(X-:"l ;~)$}L$(X~='(l J~)]=l-~
(20 - 1.96 ~/':) $ ".~(20 _ 1.96 4;~) ~ 0.95
., " 36; .jJ6 .
P(l8.63 sIis:21.37),., 0.95
STATISTICAL CcSIGN AND ANALYSIS OF ENGil,EEflII'iG EXPfn:r ....i·,-S76
(
.. fT ,.
(1/ = I /2j(l) dl =--
• -.r, r - 2
The evaluation of these integrals is generally not made because 'lhe £(1) and
a/ arc of little use in applications of the I distribution.
The probability a that the value of / exceeds a preset constant value 1,;,
is given by the shaded area in Fig. 3.6. This is denoted by '] = P(I > I'l.)' C
7. This interval for population distribution is larger than the one that
covers a single parameter of the population.
Table 3.1 gives the value of K for the two-sided cases where the estimate
of both the upper limit (x+ Ks) and of the lower limit (x - Ks) are ~:';ce
.nade. Table 3.2 is to be used. for one-sided t.:a~~. whe,- 31" .r.::.llmat·! !.~ "':"
oe made of only one of the two IBT.:ls.
3.1.3 ESTIMATE OF THE POPULATION LIMITS OF A P RODUCT
Sections 3.), I nod 3,1.2 were concerned with the estimation of the uniformity
of a product in lerms of the standard deviation «(1) and of the quality of a
product in terms of its mean value (J.I) respectively. A pair of confidence
limits were determined so that the interval between the two limits would
contain the popularion parameters 0 or J.I with a certain confidence. How.
ever, in many engineering situations it is important to determine an interval
that would cover a fixed proportion of the population distribution. This
interval can be expressed in the form
i±Ks
where x = sample average based on the sample size n
s = sample standard deviation .
and K = factor that depends on sample size n, the desired proportion P of
the population distribution, and the confidence at which this
interval is estimated
93.24707,:;; It $ 96,75307.
Hence. it can be concluded with 90 percent confidence thaI the range 93.247 10
96.753 oz will contain the population mean J.L.
---- --_.._. - "".._ ..' '" ."..._,,- ..._-_ ........_... "'_" -.._._.........
or
Substituting the values in EQ. (3.16),
4 4
95 - /_. x 1.753 ::::IL:::; 95·t J- x 1.753
"V t6 16
',/1,.-10.0.,,, = 1.753 (rom Table A·3
lI=n-'!-15n ... 16s~ 4 O~
I
. v"x - 95 oz
Solution
Example 3.2 A filling machine is set .to fill packages 10 a certain weight x, Six-
teen pa::kag!:S were chosen at random; their average weight was found (0 be 95 OZ,
and th e standard deviation 4.00 oz, Estimate the population mean I~ with 90
percent confidenee,
(3.16)
or (I ._:r) confidence interval for ,Iis
_ s _ s
x ---r:, './2:. '$ II s: X + .r:. 1./2;.
" n In
79
/
././
,
~
I
I
J
I
I
"'1
' '
o
Fig. 3.7 The (I - «) confidence interval (or a I distribulion,
P(-I./2:. S; (~ +!'f2:.) = I - a
where I -;x is the confidence, and 1= x - f.J.
. s/~~
p( -1.12:,'5,X-: '5, +1012:.) = I-:x
sf";fI
/'( - '.12 > I) == :
2'
where . = /I - I
Referring to rig. 3.7:
P(I.s I./l,.) <= ) - ~
. 2
Since. by definition,
has a X2 dis!ribution with (n, - ) degrees of freedom,
xf=_
.Jx2(v
by combining the Above relations, the f~lJowing is obtained:
,[{x - 11)';;1:(01';;;; =t (.~- J1)~
J(n,-I)s2ioj ... s :<...,/ (lIS)
has a 1 distribution witli (.'I, - I) degrees of freedom.
The usefulness of this variable I becomes apparent when • l
that x and rare th . , . one o,lS(!rVCS
ld b fo '. c paramctcr.~ associated 'with :I given sample and as such
:~u e ound ,from a lest,. TIle, relationship between the sample mean x
.d the population mean II IS derived from the following l 1..'1'
mcnts., pro la", uy stare-
qATI"'T' -A' CE" G~ • ~ IL;" ',Ii N ANa /-t'jALYSIS OF CNGIN",I'lr,'"c c 1 Y rxrEf~a"r,··"f!,;
is also normally oistribu(ed with zero mean and unit vari~ncc' _ h
obtained Iron I" , ,x ere was
h
( , ' .1 n.J( samp C Size, It Wil,'> also shown thai for ,:I S·r.'~I~ size
I e variable ' , ," "I"~ n,!
73
I
8. :;, HC'N "'2"" GUscr.-a:;on~~h-:-"" :-: ,..·,ad' ,r- e~~'1rc Y.!;(, 90 r-e.f'XI" ;i;n!i.dc:;'~H:"t
;'"6~;"-;': ,(:,1 ~ 1.7{~":·
Whal is the average interrelcnCe cf :his ')1 of lOO.O<Xlassemblies? Provide the answer at
95 percent conf:dcr.cc level.
q9An engineer teSlS a $3mplc or 1I bearings for hardness. The S3mplc variance is
2.850. The rest data arc norn1a!ly distributct.!. Find the range of the population variance
",ith 80 yerCCIlI confidence.
,~--"__ 3.7. A eenain Iype of lill)ll buib Ita, a variance in burninG time of 10.OOOh'. A sample of
2O'blllbs was picked. presumably from this lot, and ilS variance was four:d 10 be 12.000 h'.
At 9$ percent conl'tlknce level. dctern1ine whether the 20 bulbs were pic.kcd from the righ!
lot.
I 3~8>Fifteen pl:.~tlC lining.s made out of standard material were tested for wear with the
• tc>i:",,,,,,,,fi.re~:.>IL~·:; "" 0.0090 in. s "" 0 (l02! in. Wh.:.l call be SOlid about the poy;;;al:Ci
-,"1 '" :.';," . ~,~ ~1 ';P" ~O) !;IKcr. ~ "~ ...cr ",ith 95 pc~ccn' <:cnlio·!ncc.
0.00060
O.Ov"'()4()
0.00050
0.00035
0.00060
0.00055
0.00050
0.00045
0.00045
0.00060
I
2
3
"5
(,
7
8
9
10
D;omcrral
inltr/t(tnu. in
Pa.lS-fir
assrmbly nllmbrr
.
I
/':i':D The sample mean of nine bOTCi is 1.004 in~and the standard ceviation is 0,005 in.
The distribl!tion of dir:1ension sires is (lorma!.
(1'1) Determine the average of the population with 95 percent confidence.
(b) Determine the average of the population with 99 percent confidence.
(3;2> Determine by Weibuil rl,clhod, with 90 percenl confidence. the mean life and the
characteri~tic life. on the basis of the following lest data (life cycles in 10'): 0.51. 0.97,1.50,
2.20.3.00.
3.3. Firty [terns were picked at random from 1I 101 of two thousand. and ten items were
found defective. Predict with 91) pcrcen~ confidence the minimum 811d Ihe maximum
number of irerns that could be defective in the remaining items of n'iis lot.
(~A_) TCII metallic pieces were chosen at ranool:1 [rom a normal po;>ulation. and their
Iialdnesses ....-ere found to be 66.68.67.69. n. 70. 70, 71,63. and 63 ac, Plollne mean
ha,dnc!.lo(S of the population versus the confidcn~e levels.
3.5. Ten press-lit 3ssen1blies. invol.ing a ShAft and a bore, were picked out or a 101of one
hundred thousand assemblies. and their inler(ercno:s musured. with the following Te$ullS;
!
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