Standard error-Biostatistics
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Standard error is used in the place of deviation. it shows the variations among sample is correlate to sampling error. list of formula used for standard error for different statistics and applications of tests of significance in biological sciences
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Standard error
Satyaki Aparajit Mishra presented on the topic of standard error and predictability limits. Standard error is used to estimate the standard deviation from a sample. It is calculated by dividing the standard deviation by the square root of the sample size. A larger standard error means the sample mean is less reliable at estimating the population mean. Standard error helps determine how far sample estimates may be from the true population values. Mishra discussed estimating standard error from a single sample and how standard error is used to test hypotheses. He provided an example of testing if a coin flip was unbiased using the standard error of the proportion of heads observed.
Measures of dispersion
Measures of dispersion
Absolute measure, relative measures
Range of Coe. of Range
Mean deviation and coe. of mean deviation
Quartile deviation IQR, coefficient of QD
Standard deviation and coefficient of variation
Standard deviation
Standard deviation measures how dispersed data values are from the average. It is the most reliable measure of dispersion and shows the average distance of each data point from the mean. While it is more difficult to calculate than other measures, standard deviation provides important information about how concentrated or spread out the data is. The presentation defines standard deviation, lists its merits and demerits, and shows how to calculate it for both populations and samples.
{ANOVA} PPT-1.pptx
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Introduction to Biostatistics and types of sampling methods
This power point ill help you learn about different terminologies used in biostatistics. Different sampling methods has been well explained here.
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.
F test and ANOVA
A brief description of F Test and ANOVA for Msc Life Science students. I have taken the example slides from youtube where an excellent explanation is available.
Here is the link : https://www.youtube.com/watch?v=-yQb_ZJnFXw
Z-test
This document discusses the Z-test, a statistical test used to compare means and proportions. The Z-test can be used to test if a sample mean differs from a population mean, if two sample means are equal, or if two population proportions are equal. It assumes the population is normally distributed. The steps involve formulating hypotheses, choosing a significance level, calculating the Z-statistic, and comparing it to a critical value to determine if the null hypothesis should be rejected or accepted. The Z-test is useful when sample sizes are large but requires knowing the population standard deviation.
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Standard error
Satyaki Aparajit Mishra presented on the topic of standard error and predictability limits. Standard error is used to estimate the standard deviation from a sample. It is calculated by dividing the standard deviation by the square root of the sample size. A larger standard error means the sample mean is less reliable at estimating the population mean. Standard error helps determine how far sample estimates may be from the true population values. Mishra discussed estimating standard error from a single sample and how standard error is used to test hypotheses. He provided an example of testing if a coin flip was unbiased using the standard error of the proportion of heads observed.
Measures of dispersion
Measures of dispersion
Absolute measure, relative measures
Range of Coe. of Range
Mean deviation and coe. of mean deviation
Quartile deviation IQR, coefficient of QD
Standard deviation and coefficient of variation
Standard deviation
Standard deviation measures how dispersed data values are from the average. It is the most reliable measure of dispersion and shows the average distance of each data point from the mean. While it is more difficult to calculate than other measures, standard deviation provides important information about how concentrated or spread out the data is. The presentation defines standard deviation, lists its merits and demerits, and shows how to calculate it for both populations and samples.
{ANOVA} PPT-1.pptx
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Introduction to Biostatistics and types of sampling methods
This power point ill help you learn about different terminologies used in biostatistics. Different sampling methods has been well explained here.
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.
F test and ANOVA
A brief description of F Test and ANOVA for Msc Life Science students. I have taken the example slides from youtube where an excellent explanation is available.
Here is the link : https://www.youtube.com/watch?v=-yQb_ZJnFXw
Z-test
This document discusses the Z-test, a statistical test used to compare means and proportions. The Z-test can be used to test if a sample mean differs from a population mean, if two sample means are equal, or if two population proportions are equal. It assumes the population is normally distributed. The steps involve formulating hypotheses, choosing a significance level, calculating the Z-statistic, and comparing it to a critical value to determine if the null hypothesis should be rejected or accepted. The Z-test is useful when sample sizes are large but requires knowing the population standard deviation.
Measures of dispersion
The document discusses various measures used to describe the dispersion or variability in a data set. It defines dispersion as the extent to which values in a distribution differ from the average. Several measures of dispersion are described, including range, interquartile range, mean deviation, and standard deviation. The document also discusses measures of relative standing like percentiles and quartiles, and how they can locate the position of observations within a data set. The learning objectives are to understand how to describe variability, compare distributions, describe relative standing, and understand the shape of distributions using these measures.
Measure of Dispersion in statistics
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
The t test
1) The document presents information on different types of t-tests including the single sample t-test, independent sample t-test, and dependent/paired sample t-test. Equations and examples are provided for each.
2) The single sample t-test compares the mean of a sample to a hypothesized population mean. The independent t-test compares the means of two independent samples. The dependent t-test compares the means of two related samples, such as pre-and post-test scores.
3) A z-test is also discussed and compared to t-tests. The z-test is used when the population standard deviation is known and sample sizes are large, while t-tests are used
Chi -square test
The chi-square test is used to compare observed data with expected data. It was developed by Karl Pearson in 1900. The chi-square test calculates the sum of the squares of the differences between the observed and expected frequencies divided by the expected frequency. The chi-square value is then compared to a critical value to determine if there is a significant difference between the observed and expected results. The degrees of freedom, which determine the critical value, are calculated based on the number of rows and columns in a contingency table. The chi-square test can be used to test goodness of fit, independence of attributes, and other hypotheses.
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F test
The document discusses the F-test, which is used to compare the variances of two random samples to determine if they are significantly different. It provides the formula for calculating the F-statistic, outlines the assumptions of the test, and gives two examples calculating F to test if sample variances are equal or different at the 5% significance level. In both examples, the calculated F-value is less than the critical value from the F-distribution table, so the null hypothesis of equal variances is not rejected.
Standard deviation
This document discusses standard deviation (SD), which is a measure of dispersion used commonly in statistical analysis. It describes how to calculate SD by finding the mean, deviations from the mean, sum of squared deviations, and variance. For large samples, the square root of the variance gives the SD. SD summarizes how much values vary from the mean, helps determine if differences are due to chance, and indicates appropriate sample sizes. For a normal distribution, about 68% of values fall within 1 SD of the mean, 95% within 2 SD, and 99.7% within 3 SD.
Correlation and Regression
1. Correlation analysis measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 is a perfect negative correlation, 0 is no correlation, and 1 is a perfect positive correlation.
2. Scatter diagrams provide a visual representation of the relationship between two variables but do not provide a precise measure of correlation. Pearson's correlation coefficient (r) calculates the numerical strength of the linear relationship.
3. Correlation is widely used in fields like agriculture, genetics, and physiology to study relationships between variables like crop yield and fertilizer use, gene linkage, and organism growth and environmental factors.
Analysis of variance
This document provides an overview of analysis of variance (ANOVA) techniques. It discusses one-way and two-way ANOVA, including their assumptions, calculations, and applications. For example, it explains how to set up a two-way ANOVA table and calculate values like sums of squares, degrees of freedom, mean squares, and F values. It also gives an example of using one-way ANOVA to analyze differences in crop yields between four plots of land.
Chi squared test
The document provides information about the Chi-square test, including:
- It is a non-parametric test used to evaluate categorical data using contingency tables. The test statistic follows a Chi-square distribution.
- It can test for independence between variables and goodness of fit to theoretical distributions.
- Key steps involve calculating expected frequencies, taking the difference between observed and expected, and summing the results.
- The test interprets higher Chi-square values as less likelihood the results are due to chance. Modifications like Yates' correction and Fisher's exact test address limitations for small sample sizes.
Standard deviation
The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population.
Student t-test
The document describes how to perform a student's t-test to compare two samples. It provides steps for both a matched pairs t-test and an independent samples t-test. For a matched pairs t-test, the steps are: 1) state the null and alternative hypotheses, 2) calculate the differences between pairs, 3) calculate the mean difference, 4) calculate the standard deviation of the differences, 5) calculate the standard error, 6) calculate the t value, 7) determine the degrees of freedom, 8) find the critical t value, and 9) determine if there is a statistically significant difference. For an independent samples t-test, similar steps are followed to calculate means, standard deviations, the difference between
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This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines ANOVA as a statistical tool used to test differences between two or more means by analyzing variance. One-way ANOVA tests the effect of one factor on the mean and splits total variation into between-groups and within-groups components. Two-way ANOVA controls for another variable as a blocking factor to reduce error variance and splits total variation into between treatments, between blocks, and residual components. The document reviews key ANOVA terms, assumptions, calculations including sum of squares, F-ratio and p-value, and provides examples of one-way and two-way ANOVA.
Chi – square test
The document discusses the chi-square test, which offers an alternative method for testing the significance of differences between two proportions. It was developed by Karl Pearson and follows a specific chi-square distribution. To calculate chi-square, contingency tables are made noting observed and expected frequencies, and the chi-square value is calculated using the formula. Degrees of freedom are also calculated. Chi-square test is commonly used to test proportions, associations between events, and goodness of fit to a theory. However, it has limitations when expected values are less than 5 and does not measure strength of association or indicate causation.
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The document discusses analysis of variance (ANOVA), a statistical technique developed by R.A. Fisher in 1920 to analyze the differences between group means and their associated procedures. It can be used when there are two or more samples to study the significance of differences between their mean values. ANOVA works by decomposing the overall variability into different sources and comparing the relative sizes of different variances. It is useful for research in fields like agriculture, biology, pharmacy, and more.
Standard error of the mean
The standard error of the mean is a measurement of how closely a sample represents the population by determining the amount of variation between the sample mean and the true population mean. It is calculated by taking the standard deviation of the sample and dividing it by the square root of the sample size. This provides an estimate of how far the sample mean is likely to be from the true population mean. The document then provides an example of measuring weights of men and calculating the standard error of the mean to determine variation from the average weight. It also outlines the 8 step process for calculating the standard error of the mean from a sample.
Sign test
The sign test is a nonparametric test that uses the signs (positive or negative) of deviations from a measure of central tendency, rather than the magnitudes of the deviations. There are one-sample and paired-sample versions. For the one-sample sign test, the null hypothesis is that the probability of a positive sign is 0.5. Signs are counted and compared to a critical value to determine if the null can be rejected. The document then provides examples of applying the one-sample and paired-sample sign tests to various data sets involving numbers of late workers, golf scores, and accounts receivable.
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This document provides an overview of parametric statistical tests, including the t-test, ANOVA, Pearson's correlation coefficient, and Z-test. It describes the assumptions, calculations, and procedures for each test. The t-test is used to compare means of small samples and can be used for one sample, two independent samples, or paired samples. ANOVA allows comparison of multiple population means and is used when more than two groups are involved. Pearson's correlation measures the strength of association between two continuous variables. The Z-test, which is used for larger samples, can be applied to compare means or proportions.
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The document discusses various measures used to describe the dispersion or variability in a data set. It defines dispersion as the extent to which values in a distribution differ from the average. Several measures of dispersion are described, including range, interquartile range, mean deviation, and standard deviation. The document also discusses measures of relative standing like percentiles and quartiles, and how they can locate the position of observations within a data set. The learning objectives are to understand how to describe variability, compare distributions, describe relative standing, and understand the shape of distributions using these measures.
Measure of Dispersion in statistics
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
The t test
1) The document presents information on different types of t-tests including the single sample t-test, independent sample t-test, and dependent/paired sample t-test. Equations and examples are provided for each.
2) The single sample t-test compares the mean of a sample to a hypothesized population mean. The independent t-test compares the means of two independent samples. The dependent t-test compares the means of two related samples, such as pre-and post-test scores.
3) A z-test is also discussed and compared to t-tests. The z-test is used when the population standard deviation is known and sample sizes are large, while t-tests are used
Chi -square test
The chi-square test is used to compare observed data with expected data. It was developed by Karl Pearson in 1900. The chi-square test calculates the sum of the squares of the differences between the observed and expected frequencies divided by the expected frequency. The chi-square value is then compared to a critical value to determine if there is a significant difference between the observed and expected results. The degrees of freedom, which determine the critical value, are calculated based on the number of rows and columns in a contingency table. The chi-square test can be used to test goodness of fit, independence of attributes, and other hypotheses.
Standard deviation and standard error
This document discusses standard deviation and related statistical concepts. It defines standard deviation as a measure of variability around the mean and explains how to calculate it from both ungrouped and grouped data. It also defines related terms like variance, standard error of the mean, and confidence limits of the mean. Standard deviation is calculated using the formula that sums the squared deviations from the mean, divided by n-1. Standard error is the standard deviation divided by the square root of the sample size, and confidence limits refer to ranges around the mean within which we can be certain the population mean falls.
F test
The document discusses the F-test, which is used to compare the variances of two random samples to determine if they are significantly different. It provides the formula for calculating the F-statistic, outlines the assumptions of the test, and gives two examples calculating F to test if sample variances are equal or different at the 5% significance level. In both examples, the calculated F-value is less than the critical value from the F-distribution table, so the null hypothesis of equal variances is not rejected.
Standard deviation
This document discusses standard deviation (SD), which is a measure of dispersion used commonly in statistical analysis. It describes how to calculate SD by finding the mean, deviations from the mean, sum of squared deviations, and variance. For large samples, the square root of the variance gives the SD. SD summarizes how much values vary from the mean, helps determine if differences are due to chance, and indicates appropriate sample sizes. For a normal distribution, about 68% of values fall within 1 SD of the mean, 95% within 2 SD, and 99.7% within 3 SD.
Correlation and Regression
1. Correlation analysis measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 is a perfect negative correlation, 0 is no correlation, and 1 is a perfect positive correlation.
2. Scatter diagrams provide a visual representation of the relationship between two variables but do not provide a precise measure of correlation. Pearson's correlation coefficient (r) calculates the numerical strength of the linear relationship.
3. Correlation is widely used in fields like agriculture, genetics, and physiology to study relationships between variables like crop yield and fertilizer use, gene linkage, and organism growth and environmental factors.
Analysis of variance
This document provides an overview of analysis of variance (ANOVA) techniques. It discusses one-way and two-way ANOVA, including their assumptions, calculations, and applications. For example, it explains how to set up a two-way ANOVA table and calculate values like sums of squares, degrees of freedom, mean squares, and F values. It also gives an example of using one-way ANOVA to analyze differences in crop yields between four plots of land.
Chi squared test
The document provides information about the Chi-square test, including:
- It is a non-parametric test used to evaluate categorical data using contingency tables. The test statistic follows a Chi-square distribution.
- It can test for independence between variables and goodness of fit to theoretical distributions.
- Key steps involve calculating expected frequencies, taking the difference between observed and expected, and summing the results.
- The test interprets higher Chi-square values as less likelihood the results are due to chance. Modifications like Yates' correction and Fisher's exact test address limitations for small sample sizes.
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The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population.
Student t-test
The document describes how to perform a student's t-test to compare two samples. It provides steps for both a matched pairs t-test and an independent samples t-test. For a matched pairs t-test, the steps are: 1) state the null and alternative hypotheses, 2) calculate the differences between pairs, 3) calculate the mean difference, 4) calculate the standard deviation of the differences, 5) calculate the standard error, 6) calculate the t value, 7) determine the degrees of freedom, 8) find the critical t value, and 9) determine if there is a statistically significant difference. For an independent samples t-test, similar steps are followed to calculate means, standard deviations, the difference between
Measures of Dispersion
It includes all the measures of dispersion with detailed methodology along with required practice problems.
The mann whitney u test
The Mann-Whitney U Test is used to compare two independent groups on an ordinal scale. It tests the null hypothesis that there is no difference between the groups' rankings. The document provides an example comparing traditional language learning to immersion learning. Students' Spanish test scores were ranked, and the Mann-Whitney U Test found a significant difference, rejecting the null hypothesis. The immersion group had higher rankings than the traditional group, showing greater Spanish proficiency from immersion learning.
Anova - One way and two way
This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines ANOVA as a statistical tool used to test differences between two or more means by analyzing variance. One-way ANOVA tests the effect of one factor on the mean and splits total variation into between-groups and within-groups components. Two-way ANOVA controls for another variable as a blocking factor to reduce error variance and splits total variation into between treatments, between blocks, and residual components. The document reviews key ANOVA terms, assumptions, calculations including sum of squares, F-ratio and p-value, and provides examples of one-way and two-way ANOVA.
Chi – square test
The document discusses the chi-square test, which offers an alternative method for testing the significance of differences between two proportions. It was developed by Karl Pearson and follows a specific chi-square distribution. To calculate chi-square, contingency tables are made noting observed and expected frequencies, and the chi-square value is calculated using the formula. Degrees of freedom are also calculated. Chi-square test is commonly used to test proportions, associations between events, and goodness of fit to a theory. However, it has limitations when expected values are less than 5 and does not measure strength of association or indicate causation.
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ANOVA TEST by shafeek
The document discusses analysis of variance (ANOVA), a statistical technique developed by R.A. Fisher in 1920 to analyze the differences between group means and their associated procedures. It can be used when there are two or more samples to study the significance of differences between their mean values. ANOVA works by decomposing the overall variability into different sources and comparing the relative sizes of different variances. It is useful for research in fields like agriculture, biology, pharmacy, and more.
Standard error of the mean
The standard error of the mean is a measurement of how closely a sample represents the population by determining the amount of variation between the sample mean and the true population mean. It is calculated by taking the standard deviation of the sample and dividing it by the square root of the sample size. This provides an estimate of how far the sample mean is likely to be from the true population mean. The document then provides an example of measuring weights of men and calculating the standard error of the mean to determine variation from the average weight. It also outlines the 8 step process for calculating the standard error of the mean from a sample.
Sign test
The sign test is a nonparametric test that uses the signs (positive or negative) of deviations from a measure of central tendency, rather than the magnitudes of the deviations. There are one-sample and paired-sample versions. For the one-sample sign test, the null hypothesis is that the probability of a positive sign is 0.5. Signs are counted and compared to a critical value to determine if the null can be rejected. The document then provides examples of applying the one-sample and paired-sample sign tests to various data sets involving numbers of late workers, golf scores, and accounts receivable.
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This document discusses various statistical tests used to analyze data, including tests of significance, parametric vs. non-parametric tests, and limitations. It provides background on key tests such as:
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Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
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"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
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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
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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.
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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.
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
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(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】
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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：
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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(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、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进口机器一比一制作，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！
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
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Standard error-Biostatistics
- 1. K.SUDHA RAMESHWARI ASSISTANT PROFESSOR, V.V.VANNIAPERUMAL COLLEGE FOR WOMEN,VIRUDHUNAGAR TAMILNADU,INDIA
- 2. The standard deviation of the sampling distribution is called standard error The following is the list of formula for obtaining standard error for different statistics
- 6. Utility of standard error It is used as an instrument in testing a given hypothesis. When the hypothesis is tested at 5% level of significance , if the difference between observed and expected mean is more than 1.96 standard error, we may conclude that the result of the experiment does not support the hypothesis at 5% level and if the difference is less than 1.96 SE, we can accept the hypothesis. At 1% significance level, if the difference is more than 2.58 SE, we can’t accept the hypothesisa and vice versa
- 7. Tests of significance can be classified into three 1. Tests of significance for attributes 2. Tests if significance for variables (large samples) 3. Tests of significance for variables (small samples)
- 8. Application of tests of significance in biological sciences Tests of significance of attributes: 1. To find out whether both vegetarian and non- vegetarian food eaters are equally intelligent or not 2. To test the hypothesis that male and female babies are born in equal number When we take two samples from two different populations, we can find out whether there is any significant difference in the proportion of success. Examples -To find out whether there is any significant difference in the food habits of two villagers
- 9. Tests of significance of large samples: A sample is considered to be large when its size exceeds 30 (n>30) The various fields of applications are: 1. To test the hypothesis that there is no significant difference between sample mean height and hypothetical mean height of sugarcane 2. To test the hypothesis that there is no significant difference in the yield of grains between the two varieties
- 10. 3.To test the hypothesis that there is no significant difference between mean accidents in the two towns 4. To test the hypothesis that there is no significant difference in standard deviation of yield between paddy and wheat 5. To test the hypothesis that Indian paddy plants are on an average not taller than Australian paddy plants
- 11. Tests of significance of small samples Small samples means (n<30) t-test and F-test are applied Applications of t-test are 1. To find out whether systolic blood pressure of a sample of 10 persons in the age group of 40-50 is significantly differ from the hypothetical value of blood pressure of the population 2. To test the hypothesis that the average weight of goat population is 12kgs.
- 12. Reference Statistical methods for biologists(Biostatistics)- S.Palanichamy, M.Manoharan