StatWRLecture6.ppt
•Download as PPT, PDF•
0 likes•3 views
This document discusses statistical techniques for comparing two sets of data, including the t-distribution for unknown standard deviations, hypothesis testing, and the Wilcoxon rank-sum test. It provides examples of using the Wilcoxon rank-sum test to compare precipitation water quality data from two sites and determine that the residential concentration is lower than the industrial concentration with a p-value of 0.024.
Report
Share
Report
Share
Recommended
B.8 diff between paired means
This document outlines the steps for conducting a hypothesis test comparing the means of paired data. It explains that the test assumes a simple random sample of paired data that is approximately normally distributed. The steps include stating the null and alternative hypotheses about the difference between population means, using a paired t-test method to test the sample data, and interpreting the results by comparing the p-value to the significance level.
The Kruskal-Wallis H Test
This presentation gives a rough idea on what is the Kruskal-Wallis H Test and when it can be used and how to use it.
Class 5 Hypothesis & Normal Disdribution.pptx
The document discusses hypothesis testing and statistical inference. It defines key terms like hypothesis, null hypothesis, alternative hypothesis, parameters, statistics, population, sample, parametric tests, and significance level. It explains that the goal of hypothesis testing is to either confirm or disconfirm a research hypothesis by testing the null hypothesis. The process involves collecting a sample, calculating statistics, determining p-values and confidence levels, and deciding whether to reject or fail to reject the null hypothesis based on these values. The document also discusses types of errors like type I and type II errors that can occur in hypothesis testing.
Important terminologies
This document is highly important for the learners of research methodology. A number of statistical terminologies are defined with examples for the simplicity of learners.
T test^jsample size^j ethics
The document discusses the t-test, including:
1. It was introduced in 1908 by William Gosset under the pseudonym "Student" to test hypotheses about population means using small samples with unknown standard deviations.
2. The t-test has assumptions such as normality and equal variances that must be met.
3. There are different types of t-tests for different study designs: single sample t-test, independent samples t-test, and paired t-test.
4. Examples are provided to demonstrate how to calculate and interpret t-tests.
Parametric tests
This document provides an overview of parametric statistical tests used in pharmacology research. It introduces biostatistics and common statistical terms. It describes different types of data and measures of central tendency like mean, median, and mode. Parametric tests discussed include the z-test, t-test, and ANOVA. The z-test is used for large samples to compare proportions or means. The t-test is similar but for small samples and includes one-sample, two-sample, and paired t-tests. ANOVA compares multiple group means and includes one-way and two-way ANOVA. Examples are provided to demonstrate how to perform and interpret each test.
Bayesian intro
This document provides an introduction to Bayesian methods. It discusses key Bayesian concepts like priors, likelihoods, and Bayes' theorem. Bayes' theorem states that the posterior probability of a measure is proportional to the prior probability times the likelihood function. The document uses examples to illustrate Bayesian analysis and key principles like the likelihood principle and exchangeability. It also briefly discusses Bayesian pioneers like Bayes, Laplace, and Gauss and computational Bayesian methods.
2주차
This document provides an introduction to probability and statistics concepts over 2 weeks. It covers basic probability topics like sample spaces, events, probability definitions and axioms. Conditional probability and the multiplication rule for conditional probability are explained. Bayes' theorem relating prior, likelihood and posterior probabilities is introduced. Examples on probability calculations for coin tosses, dice rolls and medical testing are provided. Key terms around experimental units, populations, descriptive and inferential statistics are also defined.
Recommended
B.8 diff between paired means
This document outlines the steps for conducting a hypothesis test comparing the means of paired data. It explains that the test assumes a simple random sample of paired data that is approximately normally distributed. The steps include stating the null and alternative hypotheses about the difference between population means, using a paired t-test method to test the sample data, and interpreting the results by comparing the p-value to the significance level.
The Kruskal-Wallis H Test
This presentation gives a rough idea on what is the Kruskal-Wallis H Test and when it can be used and how to use it.
Class 5 Hypothesis & Normal Disdribution.pptx
The document discusses hypothesis testing and statistical inference. It defines key terms like hypothesis, null hypothesis, alternative hypothesis, parameters, statistics, population, sample, parametric tests, and significance level. It explains that the goal of hypothesis testing is to either confirm or disconfirm a research hypothesis by testing the null hypothesis. The process involves collecting a sample, calculating statistics, determining p-values and confidence levels, and deciding whether to reject or fail to reject the null hypothesis based on these values. The document also discusses types of errors like type I and type II errors that can occur in hypothesis testing.
Important terminologies
This document is highly important for the learners of research methodology. A number of statistical terminologies are defined with examples for the simplicity of learners.
T test^jsample size^j ethics
The document discusses the t-test, including:
1. It was introduced in 1908 by William Gosset under the pseudonym "Student" to test hypotheses about population means using small samples with unknown standard deviations.
2. The t-test has assumptions such as normality and equal variances that must be met.
3. There are different types of t-tests for different study designs: single sample t-test, independent samples t-test, and paired t-test.
4. Examples are provided to demonstrate how to calculate and interpret t-tests.
Parametric tests
This document provides an overview of parametric statistical tests used in pharmacology research. It introduces biostatistics and common statistical terms. It describes different types of data and measures of central tendency like mean, median, and mode. Parametric tests discussed include the z-test, t-test, and ANOVA. The z-test is used for large samples to compare proportions or means. The t-test is similar but for small samples and includes one-sample, two-sample, and paired t-tests. ANOVA compares multiple group means and includes one-way and two-way ANOVA. Examples are provided to demonstrate how to perform and interpret each test.
Bayesian intro
This document provides an introduction to Bayesian methods. It discusses key Bayesian concepts like priors, likelihoods, and Bayes' theorem. Bayes' theorem states that the posterior probability of a measure is proportional to the prior probability times the likelihood function. The document uses examples to illustrate Bayesian analysis and key principles like the likelihood principle and exchangeability. It also briefly discusses Bayesian pioneers like Bayes, Laplace, and Gauss and computational Bayesian methods.
2주차
This document provides an introduction to probability and statistics concepts over 2 weeks. It covers basic probability topics like sample spaces, events, probability definitions and axioms. Conditional probability and the multiplication rule for conditional probability are explained. Bayes' theorem relating prior, likelihood and posterior probabilities is introduced. Examples on probability calculations for coin tosses, dice rolls and medical testing are provided. Key terms around experimental units, populations, descriptive and inferential statistics are also defined.
The t Test for Two Related Samples
Chapter 11 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
Test of significance in Statistics
This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.
Business research methods 2
This document discusses various methods of processing and analyzing data. It describes four key processing operations: editing, coding, classification, and tabulation. It then explains several statistical techniques for analyzing data, including measures of central tendency, dispersion, relationships, probability distributions, hypothesis testing, and tests of means, variances, and association. Hypothesis testing involves defining the null and alternative hypotheses, test statistics, significance levels, and types of errors. Contingency tables can be used to test for association between qualitative variables.
Statistical analysis
The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.
Interpreting ‘tree space’ in the context of very large empirical datasets
Seminar presented at the Maths Department, University of Portsmouth, 19th November 2014
Evolutionary biologists represent actual or hypothesised evolutionary relations between living organisms using phylogenies, directed bifurcating graphs (trees) that describe evolutionary processes in terms of speciation or splitting events (nodes) and elapsed evolutionary time or distance (edges). Molecular evolution itself is largely dominated by mutations in DNA sequences, a stochastic process. Traditionally, probabilistic models of molecular evolution and phylogenies are fitted to DNA sequence data by maximum likelihood on the assumption that a single simple phylogeny will serve to approximate the evolution of a majority of DNA positions in the dataset. However modern studies now routinely sample several orders of magnitude more DNA positions, and this assumption no longer holds. Unfortunately, our conception of ‘tree space’ - a notional multidimensional surface containing all possible phylogenies - is extremely imprecise, and similarly techniques to model phylogeny model fitting in very large datasets are limited. I will show the background to this field and present some of the challenges arising from the present limited analytical framework.
Statistical analysis.pptx
1. Statistical analysis involves collecting, organizing, analyzing data, and drawing inferences about populations based on samples. It includes both descriptive and inferential statistics.
2. The document defines key terms used in statistical analysis like population, sample, statistical analysis, and discusses various statistical measures like mean, median, mode, interquartile range, and standard deviation.
3. The purposes of statistical analysis are outlined as measuring relationships, making predictions, testing hypotheses, and summarizing results. Both parametric and non-parametric statistical analyses are discussed.
Sampling fundamentals
This document discusses key concepts in sampling and statistical inference. It defines parameters and statistics, and explains sampling distributions including the sampling distribution of the mean, proportion, and difference between means. The central limit theorem is covered, stating that as sample size increases, the sampling distribution of the mean approaches a normal distribution. Common distributions used in statistical inference like the t, F, and chi-square distributions are also summarized.
Student t test
The document discusses different types of t-tests, including the one sample t-test, independent samples t-test, and paired t-test. It explains the assumptions and equations for each test and provides examples of their applications. The key differences between the t-test and z-test are also outlined. Specifically, t-tests are used for small sample sizes when the population variance is unknown, while z-tests are for large samples when the variance is known.
Chi-square IMP.ppt
A chi-squared test (χ2) is basically a data analysis on the basis of observations of a random set of variables. Usually, it is a comparison of two statistical data sets. This test was introduced by Karl Pearson in 1900 for categorical data analysis and distribution. So, it was mentioned as Pearson’s chi-squared test.
B.6 tests for mean
This document outlines the steps for conducting a hypothesis test for a mean, including stating the null and alternative hypotheses, conducting a one-sample t-test to compare the sample mean to the hypothesized population mean, calculating the t-statistic and associated p-value, and interpreting the results by comparing the p-value to the significance level to determine whether to reject the null hypothesis.
satatistics Presentation.pptx
This document defines key concepts in statistics including descriptive statistics, inferential statistics, parametric statistics, non-parametric statistics, skewness, kurtosis, the t-test, correlation, and various correlation coefficients. It provides examples of how different statistical tests are used including the one-sample t-test, independent two-sample t-test, and paired sample t-test. Correlation measures the strength and direction of association between two variables.
Stat1008 Tutorial
This document provides an overview and examples for a STAT1008 tutorial covering 9 sections: 1) data collection, 2) variables and statistics, 3) probability, 4) continuous probability distributions, 5) sampling distributions, 6) interval estimation, 7) hypothesis testing, 8) hypothesis tests for variances, and 9) regression analysis. Key concepts are explained for each section along with examples from past exams. The tutorial aims to cover the key points needed to understand the material and do well on exams through challenging questions and general hints.
Lec 5 - Normality Testing.pptx
This document discusses testing for normality in statistical data. It describes comparing an actual data distribution to a theoretical normal distribution based on the data's mean and standard deviation. Several graphical and statistical tests for normality are presented, including Q-Q plots, Kolmogorov-Smirnov tests, and Shapiro-Wilk tests. Large p-values from these tests indicate the data are normally distributed, while small p-values show the data are non-normal. The document emphasizes that normality testing is more important for smaller sample sizes and provides guidelines for selecting an appropriate normality test.
Overview of statistics: Statistical testing (Part I)
Overview of statistics: Statistical testing (Part I)Bioinformatics and Computational Biosciences Branch
The document provides an overview of statistical testing, including:
- When to use parametric vs. nonparametric tests
- When large sample tests or exact tests are needed
- When adjustments for multiple testing are required
It discusses key concepts like null and alternative hypotheses, test statistics, p-values, and type I and II errors. Examples of the Student's t-test and Wilcoxon rank sum test are provided.T distribution | Statistics
T-distribution is the most famous theoretical probability distribution in continuous family of distributions. T distribution is used in estimation where normal distribution cannot be used to estimate population parameters. Copy the link given below and paste it in new browser window to get more information on T distribution:- http://www.transtutors.com/homework-help/statistics/t-distribution.aspx
Point Estimate, Confidence Interval, Hypotesis tests
This document provides an overview of key statistical concepts taught in a statistics lab lesson, including point estimation, confidence intervals, and hypothesis testing. It defines point estimators like the sample mean that summarize a population using a sample. Confidence intervals give a range of values that the population parameter is expected to lie within. Hypothesis testing involves setting up null and alternative hypotheses and using a test statistic and critical value to reject or fail to reject the null hypothesis. Formulas for confidence intervals and hypothesis tests are presented for situations involving normal, t, and binomial distributions.
S5 w1 hypothesis testing & t test
The document discusses hypothesis testing and t-tests, explaining that a hypothesis is a suggested explanation for a phenomenon that can be tested scientifically, and a t-test determines if the means of two groups are statistically different. It provides details on null and alternative hypotheses, how to perform a t-test, and how the t-statistic, degrees of freedom, and p-value are used to evaluate whether to reject the null hypothesis.
Str t-test1
This document describes the steps for conducting an independent samples t-test. The t-test is used to compare the means of two independent groups on a continuous dependent variable. It tests whether the means of the two groups are statistically significantly different from each other. The steps include: 1) stating the null and alternative hypotheses, 2) setting the significance level, 3) calculating the t-value, 4) finding the critical t-value, and 5) making a conclusion about whether to reject the null hypothesis based on the t-values. An example compares math test scores of male and female college students to determine if gender significantly impacts scores.
Statistics
The document discusses key statistical concepts including variance, standard deviation, the normal distribution, frequency distributions, data matrices, properties of good graphs, populations and parameters, hypothesis testing, and point and interval estimation. It provides definitions and examples of these terms and how they relate to drawing statistical inferences from data.
Bayesian statistics using r intro
This document provides an introduction to Bayesian statistics using R. It discusses key Bayesian concepts like the prior, likelihood, and posterior distributions. It assumes familiarity with basic probability and probability distributions. Examples are provided to demonstrate Bayesian estimation and inference for binomial and normal distributions. Specifically, it shows how to estimate the probability of success θ in a binomial model and the mean μ in a normal model using different prior distributions and calculating the resulting posterior distributions in R.
water-13-00495-v3.pdf
This document summarizes a study that analyzed different unsteady friction models used in water hammer analysis software. The study implemented several friction models in Python code and compared them based on input requirements, stability, computational efficiency, and accuracy when validated with experimental data. It was found that Vítkovský's unsteady friction model performed best. This model was then implemented in the WANDA commercial software. Testing showed the model provided more accurate simulations of transient pressure waves compared to the previous quasisteady friction approach.
AssessingNormalityandDataTransformations.ppt
This document discusses assessing normality and data transformations. It notes that many statistical methods require normality and outlines tools to assess it such as histograms, boxplots, normal quantile plots, and goodness of fit tests. It also discusses how transformations like taking the square root, logarithm, or reciprocal can be used to remove skewness and make data more normal, with the goal of meeting assumptions of statistical methods. Tukey's ladder of powers is presented as a guide for selecting transformations to reduce left or right skewness. Examples demonstrate transforming skewed data like systolic volume to achieve normality.
More Related Content
Similar to StatWRLecture6.ppt
The t Test for Two Related Samples
Chapter 11 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
Test of significance in Statistics
This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.
Business research methods 2
This document discusses various methods of processing and analyzing data. It describes four key processing operations: editing, coding, classification, and tabulation. It then explains several statistical techniques for analyzing data, including measures of central tendency, dispersion, relationships, probability distributions, hypothesis testing, and tests of means, variances, and association. Hypothesis testing involves defining the null and alternative hypotheses, test statistics, significance levels, and types of errors. Contingency tables can be used to test for association between qualitative variables.
Statistical analysis
The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.
Interpreting ‘tree space’ in the context of very large empirical datasets
Seminar presented at the Maths Department, University of Portsmouth, 19th November 2014
Evolutionary biologists represent actual or hypothesised evolutionary relations between living organisms using phylogenies, directed bifurcating graphs (trees) that describe evolutionary processes in terms of speciation or splitting events (nodes) and elapsed evolutionary time or distance (edges). Molecular evolution itself is largely dominated by mutations in DNA sequences, a stochastic process. Traditionally, probabilistic models of molecular evolution and phylogenies are fitted to DNA sequence data by maximum likelihood on the assumption that a single simple phylogeny will serve to approximate the evolution of a majority of DNA positions in the dataset. However modern studies now routinely sample several orders of magnitude more DNA positions, and this assumption no longer holds. Unfortunately, our conception of ‘tree space’ - a notional multidimensional surface containing all possible phylogenies - is extremely imprecise, and similarly techniques to model phylogeny model fitting in very large datasets are limited. I will show the background to this field and present some of the challenges arising from the present limited analytical framework.
Statistical analysis.pptx
1. Statistical analysis involves collecting, organizing, analyzing data, and drawing inferences about populations based on samples. It includes both descriptive and inferential statistics.
2. The document defines key terms used in statistical analysis like population, sample, statistical analysis, and discusses various statistical measures like mean, median, mode, interquartile range, and standard deviation.
3. The purposes of statistical analysis are outlined as measuring relationships, making predictions, testing hypotheses, and summarizing results. Both parametric and non-parametric statistical analyses are discussed.
Sampling fundamentals
This document discusses key concepts in sampling and statistical inference. It defines parameters and statistics, and explains sampling distributions including the sampling distribution of the mean, proportion, and difference between means. The central limit theorem is covered, stating that as sample size increases, the sampling distribution of the mean approaches a normal distribution. Common distributions used in statistical inference like the t, F, and chi-square distributions are also summarized.
Student t test
The document discusses different types of t-tests, including the one sample t-test, independent samples t-test, and paired t-test. It explains the assumptions and equations for each test and provides examples of their applications. The key differences between the t-test and z-test are also outlined. Specifically, t-tests are used for small sample sizes when the population variance is unknown, while z-tests are for large samples when the variance is known.
Chi-square IMP.ppt
A chi-squared test (χ2) is basically a data analysis on the basis of observations of a random set of variables. Usually, it is a comparison of two statistical data sets. This test was introduced by Karl Pearson in 1900 for categorical data analysis and distribution. So, it was mentioned as Pearson’s chi-squared test.
B.6 tests for mean
This document outlines the steps for conducting a hypothesis test for a mean, including stating the null and alternative hypotheses, conducting a one-sample t-test to compare the sample mean to the hypothesized population mean, calculating the t-statistic and associated p-value, and interpreting the results by comparing the p-value to the significance level to determine whether to reject the null hypothesis.
satatistics Presentation.pptx
This document defines key concepts in statistics including descriptive statistics, inferential statistics, parametric statistics, non-parametric statistics, skewness, kurtosis, the t-test, correlation, and various correlation coefficients. It provides examples of how different statistical tests are used including the one-sample t-test, independent two-sample t-test, and paired sample t-test. Correlation measures the strength and direction of association between two variables.
Stat1008 Tutorial
This document provides an overview and examples for a STAT1008 tutorial covering 9 sections: 1) data collection, 2) variables and statistics, 3) probability, 4) continuous probability distributions, 5) sampling distributions, 6) interval estimation, 7) hypothesis testing, 8) hypothesis tests for variances, and 9) regression analysis. Key concepts are explained for each section along with examples from past exams. The tutorial aims to cover the key points needed to understand the material and do well on exams through challenging questions and general hints.
Lec 5 - Normality Testing.pptx
This document discusses testing for normality in statistical data. It describes comparing an actual data distribution to a theoretical normal distribution based on the data's mean and standard deviation. Several graphical and statistical tests for normality are presented, including Q-Q plots, Kolmogorov-Smirnov tests, and Shapiro-Wilk tests. Large p-values from these tests indicate the data are normally distributed, while small p-values show the data are non-normal. The document emphasizes that normality testing is more important for smaller sample sizes and provides guidelines for selecting an appropriate normality test.
Overview of statistics: Statistical testing (Part I)
Overview of statistics: Statistical testing (Part I)Bioinformatics and Computational Biosciences Branch
The document provides an overview of statistical testing, including:
- When to use parametric vs. nonparametric tests
- When large sample tests or exact tests are needed
- When adjustments for multiple testing are required
It discusses key concepts like null and alternative hypotheses, test statistics, p-values, and type I and II errors. Examples of the Student's t-test and Wilcoxon rank sum test are provided.T distribution | Statistics
T-distribution is the most famous theoretical probability distribution in continuous family of distributions. T distribution is used in estimation where normal distribution cannot be used to estimate population parameters. Copy the link given below and paste it in new browser window to get more information on T distribution:- http://www.transtutors.com/homework-help/statistics/t-distribution.aspx
Point Estimate, Confidence Interval, Hypotesis tests
This document provides an overview of key statistical concepts taught in a statistics lab lesson, including point estimation, confidence intervals, and hypothesis testing. It defines point estimators like the sample mean that summarize a population using a sample. Confidence intervals give a range of values that the population parameter is expected to lie within. Hypothesis testing involves setting up null and alternative hypotheses and using a test statistic and critical value to reject or fail to reject the null hypothesis. Formulas for confidence intervals and hypothesis tests are presented for situations involving normal, t, and binomial distributions.
S5 w1 hypothesis testing & t test
The document discusses hypothesis testing and t-tests, explaining that a hypothesis is a suggested explanation for a phenomenon that can be tested scientifically, and a t-test determines if the means of two groups are statistically different. It provides details on null and alternative hypotheses, how to perform a t-test, and how the t-statistic, degrees of freedom, and p-value are used to evaluate whether to reject the null hypothesis.
Str t-test1
This document describes the steps for conducting an independent samples t-test. The t-test is used to compare the means of two independent groups on a continuous dependent variable. It tests whether the means of the two groups are statistically significantly different from each other. The steps include: 1) stating the null and alternative hypotheses, 2) setting the significance level, 3) calculating the t-value, 4) finding the critical t-value, and 5) making a conclusion about whether to reject the null hypothesis based on the t-values. An example compares math test scores of male and female college students to determine if gender significantly impacts scores.
Statistics
The document discusses key statistical concepts including variance, standard deviation, the normal distribution, frequency distributions, data matrices, properties of good graphs, populations and parameters, hypothesis testing, and point and interval estimation. It provides definitions and examples of these terms and how they relate to drawing statistical inferences from data.
Bayesian statistics using r intro
This document provides an introduction to Bayesian statistics using R. It discusses key Bayesian concepts like the prior, likelihood, and posterior distributions. It assumes familiarity with basic probability and probability distributions. Examples are provided to demonstrate Bayesian estimation and inference for binomial and normal distributions. Specifically, it shows how to estimate the probability of success θ in a binomial model and the mean μ in a normal model using different prior distributions and calculating the resulting posterior distributions in R.
Similar to StatWRLecture6.ppt (20)
Interpreting ‘tree space’ in the context of very large empirical datasets
Interpreting ‘tree space’ in the context of very large empirical datasets
Overview of statistics: Statistical testing (Part I)
Overview of statistics: Statistical testing (Part I)
Point Estimate, Confidence Interval, Hypotesis tests
Point Estimate, Confidence Interval, Hypotesis tests
More from BAGARAGAZAROMUALD2
water-13-00495-v3.pdf
This document summarizes a study that analyzed different unsteady friction models used in water hammer analysis software. The study implemented several friction models in Python code and compared them based on input requirements, stability, computational efficiency, and accuracy when validated with experimental data. It was found that Vítkovský's unsteady friction model performed best. This model was then implemented in the WANDA commercial software. Testing showed the model provided more accurate simulations of transient pressure waves compared to the previous quasisteady friction approach.
AssessingNormalityandDataTransformations.ppt
This document discusses assessing normality and data transformations. It notes that many statistical methods require normality and outlines tools to assess it such as histograms, boxplots, normal quantile plots, and goodness of fit tests. It also discusses how transformations like taking the square root, logarithm, or reciprocal can be used to remove skewness and make data more normal, with the goal of meeting assumptions of statistical methods. Tukey's ladder of powers is presented as a guide for selecting transformations to reduce left or right skewness. Examples demonstrate transforming skewed data like systolic volume to achieve normality.
5116427.ppt
This document provides an introduction to using SPSS (Statistical Package for the Social Sciences) software. It covers opening and navigating SPSS, cleaning and transforming data, descriptive statistics, graphs and charts, and saving work. The topics are demonstrated using a sample education data set. Key functions covered include selecting cases, recoding variables, descriptive statistics like frequencies and crosstabs, formatting histograms and other graphs, and performing a one-way ANOVA test. Resources for further learning SPSS are also provided.
240-design.ppt
The document discusses survey network adjustment theory, design, and testing. It covers topics such as network adjustment algorithms, input and output data, statistical testing of results, reliability indicators, and network design considerations. Proper network design and testing is important to determine the quality and accuracy of survey results. A variety of network types are discussed, including level networks, resection, intersection, control traverse, and control networks.
Remote Sensing_2020-21 (1).pdf
This document provides an overview of remote sensing. It defines remote sensing as acquiring information about an object without physical contact. The history of remote sensing is outlined from early uses of photography from balloons and planes to modern satellite systems. The key principles of remote sensing are described, including the electromagnetic spectrum, energy sources, atmospheric interactions, and how radiation is absorbed, transmitted or reflected when interacting with targets. Remote sensing applications and different sensor types are also mentioned.
Szeliski_NLS1.ppt
This document provides an overview of non-linear least squares and sparse matrix techniques. It begins with an introduction to non-linear least squares using the example of triangulation. It then discusses solutions such as normal equations, LDLT factorization, and the Levenberg-Marquardt algorithm. The document next covers sparse matrix techniques for structure from motion problems, including sparse LDLT factorization and iterative methods like conjugate gradients. It concludes by comparing direct and iterative solutions for 1D and 2D problems.
AssessingNormalityandDataTransformations.ppt
This document discusses assessing normality and data transformations. It notes that many statistical methods require normality and outlines tools to assess it such as histograms, boxplots, normal quantile plots, and goodness of fit tests. It also discusses how transformations like taking the square root, logarithm, or reciprocal can be used to remove skewness and make data more normal, with the goal of meeting assumptions of statistical methods. Tukey's ladder of powers is presented as a guide for selecting transformations to reduce left or right skewness. Examples demonstrate transforming skewed variables like systolic volume to achieve normality.
18-21 Principles of Least Squares.ppt
- The document discusses principles of least squares adjustment for survey measurements.
- It introduces random error adjustment to account for measurement errors by minimizing the sum of squared residuals.
- The fundamental principle of least squares states that to obtain the most probable values, the sum of squares of the residuals must be minimized.
- It presents examples to demonstrate setting up and solving least squares adjustments through normal equation matrices in both linear and nonlinear systems.
Ch 11.2 Chi Squared Test for Independence.pptx
The document provides information on performing a chi-squared test for independence using contingency tables. It defines key terms like degrees of freedom and expected values. An example compares infections at 3 hospitals. It shows calculating degrees of freedom, stating hypotheses, finding the critical value, computing the test statistic, and making a decision to reject or fail to reject the null hypothesis of independence.
lecture12.ppt
This document discusses statistical tests used to analyze data from different types of study designs. It provides an overview of tests for comparing two or more groups, including ANOVA and chi-square tests. It also reviews alternatives that can be used if the assumptions of those tests, like normality, are violated. Examples are given of how to calculate ANOVA by hand and how it relates to the t-test. In summary, the document reviews best practices for selecting the appropriate statistical test based on the study design, number of groups, type of outcome variable, and whether observations are independent or correlated between groups.
Chi-Square Presentation - Nikki.ppt
The document provides an overview of the chi-square distribution and how to conduct chi-square tests. It discusses when chi-square tests can be used, the assumptions of chi-square tests, and how to perform chi-square analyses including calculating expected frequencies, degrees of freedom, and comparing the chi-square statistic to critical values. Examples demonstrating chi-square tests for proportions and association are presented. SAS code for conducting chi-square tests is also shown.
chapter18.ppt
This document introduces two nonparametric hypothesis tests using the chi-square statistic: the chi-square test for goodness of fit and the chi-square test for independence. These tests do not require assumptions about population parameters and instead use frequency data. The chi-square test for goodness of fit tests hypotheses about the shape or proportions of a population using observed and expected frequencies. The chi-square test for independence tests hypotheses about the relationship between two variables or differences between population proportions using a matrix of observed and expected frequencies. Both tests calculate a chi-square statistic to measure discrepancy between observed and expected frequencies under the null hypothesis.
Corr-and-Regress.ppt
This document discusses correlation, regression, and the general linear model. It defines correlation as assessing the relationship between two variables, while regression describes how well one variable can predict another. Pearson's r standardizes the covariance between variables. Linear regression finds the best-fitting line that minimizes the residuals through the least squares method. The coefficient of determination, r2, indicates how much variance in the dependent variable is explained by the independent variable. Multiple regression extends this to include multiple independent variables. The general linear model encompasses both linear regression and multiple regression.
More from BAGARAGAZAROMUALD2 (13)
Recently uploaded
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
毕业原版【微信:41543339】【(Glasgow毕业证书)格拉斯哥大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
毕业原版【微信:41543339】【(牛布毕业证书)牛津布鲁克斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
Recently uploaded (20)
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
StatWRLecture6.ppt
- 1. Statistics in Water Resources, Lecture 6 • Key theme – T-distribution for distributions where standard deviation is unknown – Hypothesis testing – Comparing two sets of data to see if they are different • Reading: Helsel and Hirsch, Chapter 6 Matched Pair Tests
- 3. t-, z and ChiSquare Source: http://en.wikipedia.org/wiki/Student's_t-distribution
- 4. Normal and t-distributions Normal t-dist for ν = 1 t-dist for ν = 30 t-dist for ν = 5 t-dist for ν = 3 t-dist for ν = 2 t-dist for ν = 10
- 5. • Standard Normal z – X1, … , Xn are independently distributed (μ,σ), and – then is normally distributed with mean 0 and std dev 1 Standard Normal and Student - t • Student’s t-distribution – Applies to the case where the true standard deviation σ is unknown and is replaced by its sample estimate Sn
- 6. 6 p-value is the probability of obtaining the value of the test-statistic if the null hypothesis (Ho) is true If p-value is very small (<0.05 or 0.025) then reject Ho If p-value is larger than α then do not reject Ho
- 9. Helsel and Hirsch p.120
- 10. Box and Whisker Plots of the N data
- 11. Precipitation Water Quality at two sites
- 12. Ranked Precipitation Quality Data Mean concentration is nearly the same but ranks suggest residential concentration is smaller. Is this so?
- 13. Wilcoxon Rank Sum Test Helsel and Hirsch p. 462 This is < 0.05 for a one-sided test, thus reject Ho and say residential concentration is lower than industrial p-value in middle is for P(Wrs > X) or P(Wrs < X*) for m = n = 10 Note that the sum of n = 1, 2, …. 20 = 210 and X + X* = 210 in all cases in this table. Test sum of higher ranks Test sum of lower ranks p-value is 0.024 for Rank sum of 78.5