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
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
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 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.
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.
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.
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
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.
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
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 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.
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.
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.
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
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.
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 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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
Statistical tests of significance and Student`s T-TestVasundhraKakkar
Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained
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.
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.
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.
Parmetric and non parametric statistical test in clinical trailsVinod Pagidipalli
The document discusses parametric and non-parametric statistical tests used in clinical trials. Parametric tests like the z-test, t-test, ANOVA, and correlation tests are used when data follows a normal distribution. Non-parametric tests like the chi-square test, Fisher's exact test, and binomial test are used when data cannot be assumed to be normally distributed. Several statistical tests are described, including how to apply them in clinical trials to compare treatment groups, analyze associations between variables, and test hypotheses about population proportions.
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.
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 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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
Statistical tests of significance and Student`s T-TestVasundhraKakkar
Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained
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.
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.
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.
Parmetric and non parametric statistical test in clinical trailsVinod Pagidipalli
The document discusses parametric and non-parametric statistical tests used in clinical trials. Parametric tests like the z-test, t-test, ANOVA, and correlation tests are used when data follows a normal distribution. Non-parametric tests like the chi-square test, Fisher's exact test, and binomial test are used when data cannot be assumed to be normally distributed. Several statistical tests are described, including how to apply them in clinical trials to compare treatment groups, analyze associations between variables, and test hypotheses about population proportions.
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.
The document provides information about contact details for Hemant Trivedi and then discusses analysis of variance (ANOVA) techniques, including one-way, two-way, and three or more way ANOVA. It provides an example of using ANOVA to determine the best type of packaging among three options. The document also includes information about chi square tests, including their applications and a formula. It provides two examples for chi square test exercises. Finally, it discusses fundamentals of variables, types of tests, hypotheses, and choosing a significance level.
Biostatistics_Unit_II_Research Methodology & Biostatistics_M. Pharm (Pharmace...RAHUL PAL
This document provides an overview of biostatistics topics including parametric and non-parametric statistical tests, sample size calculation, and factors influencing sample size. It discusses commonly used parametric tests like the t-test, ANOVA, correlation coefficient, and regression analysis. Non-parametric tests like the Wilcoxon rank-sum test are also covered. The importance of considering sample size, factors that can impact it, and how dropouts are handled are summarized as well.
Amrita Kumari from Banaras Hindu University submitted an application discussing parametric tests. Parametric tests were developed by R. Fisher and make assumptions about the population distribution from which a sample is drawn. The key assumptions are that the population is normally distributed, observations are independent, populations have equal variance, and data is on a ratio or interval scale. Parametric tests can be used even when distributions are skewed or variances differ, and they have more statistical power than non-parametric tests. Common parametric tests include t-tests, z-tests, and ANOVA. The document then discusses one-sample, dependent, and independent t-tests in more detail. Both advantages like precision and disadvantages like sensitivity
The chi-square test is used to determine if there is a significant association between two categorical variables. It can be used for independence tests between two variables or goodness-of-fit tests to determine if observed data fits a theoretical distribution. The chi-square test calculates expected frequencies and compares them to observed frequencies to determine if any differences could be due to chance or indicate a true association. It is widely applied in research fields to analyze relationships in categorical data.
Experimental design and statistical power in swine experimentation: A present...Kareem Damilola
This document discusses experimental design and statistical power considerations for swine experimentation. It begins by explaining the importance of valid and precise animal experiments for developing new feeds and feeding standards. It then describes common experimental designs like completely randomized design and randomized complete block design. The document emphasizes that sufficient replication is needed within experiments to maximize statistical power. It also stresses the importance of performing a power analysis to determine adequate sample sizes and avoid type II errors. The document concludes by noting that power analyses are vital for efficient experimental planning and limiting unnecessary replications in swine research.
This document discusses parametric tests. Parametric tests were proposed by R. Fisher and make assumptions about the population distribution. The key assumptions are normality, independence of observations, homogeneity of variances, and that data is on a ratio or interval scale. Parametric tests can be used even when distributions are skewed or variances differ. Examples of parametric tests discussed include t-tests (one sample, paired, and independent samples) and ANOVA. Steps for conducting one sample, paired, and independent t-tests are provided. Advantages of parametric tests include not requiring convertible data and having more statistical power, while disadvantages include susceptibility to violations of assumptions and being less applicable to small sample sizes.
Statistical analysis of biological data (comaprison of means)Mohamed Afifi
The document provides information about statistical analysis methods for comparing means from biological data, including t-tests, ANOVA, and post-hoc tests. It discusses two-sample t-tests, paired t-tests, one-way ANOVA, post-hoc multiple comparisons tests like Tukey's and Bonferroni tests, and assumptions and examples for each method. Flowcharts are also provided to help select the appropriate statistical test based on the study design and data.
This document provides an overview of parametric statistical tests, including the z-test, t-tests, chi-square test, F-test, and Bartlett's test. It discusses the history and development of the Student's t-test, including its creation by William Gosset under the pseudonym "Student." The t-test is used to compare means between two samples or between a sample and a theoretical population. The document outlines the assumptions, calculations, and interpretations of one-sample, unpaired, and paired t-tests.
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:
1) Student's t-test, developed by Gosset, which is used to compare two means from small samples with unknown variances.
2) ANOVA (analysis of variance), developed by Fisher, which compares variance between and within groups to test for significant differences between means of more than two groups.
3) Correlation analysis, which measures the strength and direction of association between two continuous variables using Pearson's correlation coefficient.
4) Chi-square test, which analyzes relationships between categorical variables to
This powerpoint presentation gives a brief explanation about the biostatic data .this is quite helpful to individuals to understand the basic research methodology terminologys
Response of Watermelon to Five Different Rates of Poultry Manure in Asaba Are...IOSR Journals
The document discusses experimental research designs, specifically pretest-posttest designs. It begins by explaining true experimental designs that use control and experimental groups, with pretests and posttests to both groups.
It then discusses different pretest-posttest designs in more detail, including Solomon four group designs. The Solomon four group design involves four groups - two groups that receive a pretest and posttest, one that only receives a posttest, and one that only receives a pretest.
The document provides an example of how pretest-posttest designs could be used to study the effects of fertilizers in agriculture. It evaluates the internal and external validity of different experimental designs and their ability to control for confounding variables
This document discusses key concepts related to sampling, statistics, and sample size for impact evaluations. It covers how samples relate to populations, sampling variation, the law of large numbers, the central limit theorem, hypothesis testing, and statistical power. The main points are:
1) Samples are subsets of populations that can be used to make inferences about the overall population. Larger sample sizes reduce sampling variation and provide more accurate estimates.
2) According to the central limit theorem, the distribution of sample means will approximate a normal distribution, even if the underlying population is not normal, as long as the sample size is large enough.
3) Hypothesis testing involves comparing sample results to determine if an intervention had a
1. The document discusses hypothesis testing, including defining the null and alternative hypotheses, types of errors, test statistics, and testing differences between population means and differences between two samples.
2. Examples are provided to demonstrate hypothesis testing for one and two sample means. This includes stating the hypotheses, significance level, test statistic, critical region, and conclusion.
3. Assignments are given applying hypothesis testing to compare lung destruction between smokers and non-smokers, serum complement activity between disease and normal subjects, and podiatric problems between elderly diabetic and non-diabetic patients.
This document provides an overview of various statistical tests used for hypothesis testing, including parametric and non-parametric tests. It defines key terms like population, sample, mean, median, mode, and standard deviation. It explains the stages of hypothesis testing including creating the null and alternative hypotheses, determining the significance level, and deciding which statistical test to use based on the type of data and number of samples. Specific tests covered include the z-test, t-test, ANOVA, chi-square test, Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis test, and Friedman test.
This document discusses blood collection methods. It describes the three main types of blood collection: venous, capillary, and arterial. Venous blood collection is the most common, involving puncturing a vein with a needle and collecting blood into a tube or syringe. Capillary collection involves a small skin puncture to obtain a small blood sample, while arterial collection is rarely needed and involves puncturing an artery. The document outlines the equipment, procedures, and safety measures for performing venipuncture. It also discusses different types of blood collection tubes and their uses based on the additive used.
This PPT deals with the problems and solutions for sampling of large variables and relate, compare the observations with the exception of the population sample ie testing the difference between means of two samples, standard error of the difference between two standard deviations.
The document discusses several applications of genomics and bioinformatics across various fields such as medicine, agriculture, microbiology, and more. It describes how genomic studies of humans and model organisms are providing insights into disease mechanisms and treatments. Applications in agriculture include developing crops with improved traits like insect or drought resistance. Microbial genomics is explored for uses like bioremediation, alternative energy, and industrial applications. Bioinformatics tools aid research through literature retrieval and comparative genomics studies.
This PPT explains about computer network in easily understandable way. It deals about terminals, computer, communication processor, communication media, telecommunication software, functions of telecommunication software such as security control, error control, access control etc.,
THIS POWERPOINT EXPLAINS ABOUT HYPOTHESIS AND ITS TYPES, ROLE OF HYPOTHESIS,TEST OF SIGNIFICANCE AND PROCEDURE FOR TESTING A HYPOTHESIS, TYPE I AND TYPE ii ERROR
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Streptomycin is an antibiotic discovered in 1944 that is produced through fermentation of Streptomyces griseus bacteria. It is used to treat infections caused by gram-positive and gram-negative bacteria as well as tuberculosis. Production occurs over 3 phases, beginning with rapid bacterial growth and ending with cell lysis and harvest. Streptomycin is recovered through adsorption onto activated carbon or ion exchange resins before precipitation and purification. It functions by binding to the bacterial ribosome and inhibiting protein synthesis.
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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.
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.
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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.
Codeless Generative AI Pipelines
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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
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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
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
3.
4.
5.
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.