StatWRLecture6.ppt
•Download as PPT, PDF•
0 likes•2 views
This document discusses statistical techniques for comparing two sets of data, including the t-distribution for unknown standard deviations, hypothesis testing, and the Wilcoxon rank-sum test. It provides examples of using the Wilcoxon rank-sum test to compare precipitation water quality data from two sites and determine that the residential concentration is lower than the industrial concentration with a p-value of 0.024.
Report
Share
Report
Share
Recommended
B.8 diff between paired means
This document outlines the steps for conducting a hypothesis test comparing the means of paired data. It explains that the test assumes a simple random sample of paired data that is approximately normally distributed. The steps include stating the null and alternative hypotheses about the difference between population means, using a paired t-test method to test the sample data, and interpreting the results by comparing the p-value to the significance level.
The Kruskal-Wallis H Test
This presentation gives a rough idea on what is the Kruskal-Wallis H Test and when it can be used and how to use it.
Class 5 Hypothesis & Normal Disdribution.pptx
The document discusses hypothesis testing and statistical inference. It defines key terms like hypothesis, null hypothesis, alternative hypothesis, parameters, statistics, population, sample, parametric tests, and significance level. It explains that the goal of hypothesis testing is to either confirm or disconfirm a research hypothesis by testing the null hypothesis. The process involves collecting a sample, calculating statistics, determining p-values and confidence levels, and deciding whether to reject or fail to reject the null hypothesis based on these values. The document also discusses types of errors like type I and type II errors that can occur in hypothesis testing.
Important terminologies
This document is highly important for the learners of research methodology. A number of statistical terminologies are defined with examples for the simplicity of learners.
T test^jsample size^j ethics
The document discusses the t-test, including:
1. It was introduced in 1908 by William Gosset under the pseudonym "Student" to test hypotheses about population means using small samples with unknown standard deviations.
2. The t-test has assumptions such as normality and equal variances that must be met.
3. There are different types of t-tests for different study designs: single sample t-test, independent samples t-test, and paired t-test.
4. Examples are provided to demonstrate how to calculate and interpret t-tests.
Parametric tests
This document provides an overview of parametric statistical tests used in pharmacology research. It introduces biostatistics and common statistical terms. It describes different types of data and measures of central tendency like mean, median, and mode. Parametric tests discussed include the z-test, t-test, and ANOVA. The z-test is used for large samples to compare proportions or means. The t-test is similar but for small samples and includes one-sample, two-sample, and paired t-tests. ANOVA compares multiple group means and includes one-way and two-way ANOVA. Examples are provided to demonstrate how to perform and interpret each test.
Bayesian intro
This document provides an introduction to Bayesian methods. It discusses key Bayesian concepts like priors, likelihoods, and Bayes' theorem. Bayes' theorem states that the posterior probability of a measure is proportional to the prior probability times the likelihood function. The document uses examples to illustrate Bayesian analysis and key principles like the likelihood principle and exchangeability. It also briefly discusses Bayesian pioneers like Bayes, Laplace, and Gauss and computational Bayesian methods.
2주차
This document provides an introduction to probability and statistics concepts over 2 weeks. It covers basic probability topics like sample spaces, events, probability definitions and axioms. Conditional probability and the multiplication rule for conditional probability are explained. Bayes' theorem relating prior, likelihood and posterior probabilities is introduced. Examples on probability calculations for coin tosses, dice rolls and medical testing are provided. Key terms around experimental units, populations, descriptive and inferential statistics are also defined.
Recommended
B.8 diff between paired means
This document outlines the steps for conducting a hypothesis test comparing the means of paired data. It explains that the test assumes a simple random sample of paired data that is approximately normally distributed. The steps include stating the null and alternative hypotheses about the difference between population means, using a paired t-test method to test the sample data, and interpreting the results by comparing the p-value to the significance level.
The Kruskal-Wallis H Test
This presentation gives a rough idea on what is the Kruskal-Wallis H Test and when it can be used and how to use it.
Class 5 Hypothesis & Normal Disdribution.pptx
The document discusses hypothesis testing and statistical inference. It defines key terms like hypothesis, null hypothesis, alternative hypothesis, parameters, statistics, population, sample, parametric tests, and significance level. It explains that the goal of hypothesis testing is to either confirm or disconfirm a research hypothesis by testing the null hypothesis. The process involves collecting a sample, calculating statistics, determining p-values and confidence levels, and deciding whether to reject or fail to reject the null hypothesis based on these values. The document also discusses types of errors like type I and type II errors that can occur in hypothesis testing.
Important terminologies
This document is highly important for the learners of research methodology. A number of statistical terminologies are defined with examples for the simplicity of learners.
T test^jsample size^j ethics
The document discusses the t-test, including:
1. It was introduced in 1908 by William Gosset under the pseudonym "Student" to test hypotheses about population means using small samples with unknown standard deviations.
2. The t-test has assumptions such as normality and equal variances that must be met.
3. There are different types of t-tests for different study designs: single sample t-test, independent samples t-test, and paired t-test.
4. Examples are provided to demonstrate how to calculate and interpret t-tests.
Parametric tests
This document provides an overview of parametric statistical tests used in pharmacology research. It introduces biostatistics and common statistical terms. It describes different types of data and measures of central tendency like mean, median, and mode. Parametric tests discussed include the z-test, t-test, and ANOVA. The z-test is used for large samples to compare proportions or means. The t-test is similar but for small samples and includes one-sample, two-sample, and paired t-tests. ANOVA compares multiple group means and includes one-way and two-way ANOVA. Examples are provided to demonstrate how to perform and interpret each test.
Bayesian intro
This document provides an introduction to Bayesian methods. It discusses key Bayesian concepts like priors, likelihoods, and Bayes' theorem. Bayes' theorem states that the posterior probability of a measure is proportional to the prior probability times the likelihood function. The document uses examples to illustrate Bayesian analysis and key principles like the likelihood principle and exchangeability. It also briefly discusses Bayesian pioneers like Bayes, Laplace, and Gauss and computational Bayesian methods.
2주차
This document provides an introduction to probability and statistics concepts over 2 weeks. It covers basic probability topics like sample spaces, events, probability definitions and axioms. Conditional probability and the multiplication rule for conditional probability are explained. Bayes' theorem relating prior, likelihood and posterior probabilities is introduced. Examples on probability calculations for coin tosses, dice rolls and medical testing are provided. Key terms around experimental units, populations, descriptive and inferential statistics are also defined.
The t Test for Two Related Samples
Chapter 11 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
Test of significance in Statistics
This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.
Business research methods 2
This document discusses various methods of processing and analyzing data. It describes four key processing operations: editing, coding, classification, and tabulation. It then explains several statistical techniques for analyzing data, including measures of central tendency, dispersion, relationships, probability distributions, hypothesis testing, and tests of means, variances, and association. Hypothesis testing involves defining the null and alternative hypotheses, test statistics, significance levels, and types of errors. Contingency tables can be used to test for association between qualitative variables.
Statistical analysis
The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.
Interpreting ‘tree space’ in the context of very large empirical datasets
Seminar presented at the Maths Department, University of Portsmouth, 19th November 2014
Evolutionary biologists represent actual or hypothesised evolutionary relations between living organisms using phylogenies, directed bifurcating graphs (trees) that describe evolutionary processes in terms of speciation or splitting events (nodes) and elapsed evolutionary time or distance (edges). Molecular evolution itself is largely dominated by mutations in DNA sequences, a stochastic process. Traditionally, probabilistic models of molecular evolution and phylogenies are fitted to DNA sequence data by maximum likelihood on the assumption that a single simple phylogeny will serve to approximate the evolution of a majority of DNA positions in the dataset. However modern studies now routinely sample several orders of magnitude more DNA positions, and this assumption no longer holds. Unfortunately, our conception of ‘tree space’ - a notional multidimensional surface containing all possible phylogenies - is extremely imprecise, and similarly techniques to model phylogeny model fitting in very large datasets are limited. I will show the background to this field and present some of the challenges arising from the present limited analytical framework.
Statistical analysis.pptx
1. Statistical analysis involves collecting, organizing, analyzing data, and drawing inferences about populations based on samples. It includes both descriptive and inferential statistics.
2. The document defines key terms used in statistical analysis like population, sample, statistical analysis, and discusses various statistical measures like mean, median, mode, interquartile range, and standard deviation.
3. The purposes of statistical analysis are outlined as measuring relationships, making predictions, testing hypotheses, and summarizing results. Both parametric and non-parametric statistical analyses are discussed.
Sampling fundamentals
This document discusses key concepts in sampling and statistical inference. It defines parameters and statistics, and explains sampling distributions including the sampling distribution of the mean, proportion, and difference between means. The central limit theorem is covered, stating that as sample size increases, the sampling distribution of the mean approaches a normal distribution. Common distributions used in statistical inference like the t, F, and chi-square distributions are also summarized.
Student t test
The document discusses different types of t-tests, including the one sample t-test, independent samples t-test, and paired t-test. It explains the assumptions and equations for each test and provides examples of their applications. The key differences between the t-test and z-test are also outlined. Specifically, t-tests are used for small sample sizes when the population variance is unknown, while z-tests are for large samples when the variance is known.
Chi-square IMP.ppt
A chi-squared test (χ2) is basically a data analysis on the basis of observations of a random set of variables. Usually, it is a comparison of two statistical data sets. This test was introduced by Karl Pearson in 1900 for categorical data analysis and distribution. So, it was mentioned as Pearson’s chi-squared test.
B.6 tests for mean
This document outlines the steps for conducting a hypothesis test for a mean, including stating the null and alternative hypotheses, conducting a one-sample t-test to compare the sample mean to the hypothesized population mean, calculating the t-statistic and associated p-value, and interpreting the results by comparing the p-value to the significance level to determine whether to reject the null hypothesis.
satatistics Presentation.pptx
This document defines key concepts in statistics including descriptive statistics, inferential statistics, parametric statistics, non-parametric statistics, skewness, kurtosis, the t-test, correlation, and various correlation coefficients. It provides examples of how different statistical tests are used including the one-sample t-test, independent two-sample t-test, and paired sample t-test. Correlation measures the strength and direction of association between two variables.
Stat1008 Tutorial
This document provides an overview and examples for a STAT1008 tutorial covering 9 sections: 1) data collection, 2) variables and statistics, 3) probability, 4) continuous probability distributions, 5) sampling distributions, 6) interval estimation, 7) hypothesis testing, 8) hypothesis tests for variances, and 9) regression analysis. Key concepts are explained for each section along with examples from past exams. The tutorial aims to cover the key points needed to understand the material and do well on exams through challenging questions and general hints.
Lec 5 - Normality Testing.pptx
This document discusses testing for normality in statistical data. It describes comparing an actual data distribution to a theoretical normal distribution based on the data's mean and standard deviation. Several graphical and statistical tests for normality are presented, including Q-Q plots, Kolmogorov-Smirnov tests, and Shapiro-Wilk tests. Large p-values from these tests indicate the data are normally distributed, while small p-values show the data are non-normal. The document emphasizes that normality testing is more important for smaller sample sizes and provides guidelines for selecting an appropriate normality test.
Overview of statistics: Statistical testing (Part I)
Overview of statistics: Statistical testing (Part I)Bioinformatics and Computational Biosciences Branch
The document provides an overview of statistical testing, including:
- When to use parametric vs. nonparametric tests
- When large sample tests or exact tests are needed
- When adjustments for multiple testing are required
It discusses key concepts like null and alternative hypotheses, test statistics, p-values, and type I and II errors. Examples of the Student's t-test and Wilcoxon rank sum test are provided.T distribution | Statistics
T-distribution is the most famous theoretical probability distribution in continuous family of distributions. T distribution is used in estimation where normal distribution cannot be used to estimate population parameters. Copy the link given below and paste it in new browser window to get more information on T distribution:- http://www.transtutors.com/homework-help/statistics/t-distribution.aspx
Point Estimate, Confidence Interval, Hypotesis tests
This document provides an overview of key statistical concepts taught in a statistics lab lesson, including point estimation, confidence intervals, and hypothesis testing. It defines point estimators like the sample mean that summarize a population using a sample. Confidence intervals give a range of values that the population parameter is expected to lie within. Hypothesis testing involves setting up null and alternative hypotheses and using a test statistic and critical value to reject or fail to reject the null hypothesis. Formulas for confidence intervals and hypothesis tests are presented for situations involving normal, t, and binomial distributions.
S5 w1 hypothesis testing & t test
The document discusses hypothesis testing and t-tests, explaining that a hypothesis is a suggested explanation for a phenomenon that can be tested scientifically, and a t-test determines if the means of two groups are statistically different. It provides details on null and alternative hypotheses, how to perform a t-test, and how the t-statistic, degrees of freedom, and p-value are used to evaluate whether to reject the null hypothesis.
Str t-test1
This document describes the steps for conducting an independent samples t-test. The t-test is used to compare the means of two independent groups on a continuous dependent variable. It tests whether the means of the two groups are statistically significantly different from each other. The steps include: 1) stating the null and alternative hypotheses, 2) setting the significance level, 3) calculating the t-value, 4) finding the critical t-value, and 5) making a conclusion about whether to reject the null hypothesis based on the t-values. An example compares math test scores of male and female college students to determine if gender significantly impacts scores.
Statistics
The document discusses key statistical concepts including variance, standard deviation, the normal distribution, frequency distributions, data matrices, properties of good graphs, populations and parameters, hypothesis testing, and point and interval estimation. It provides definitions and examples of these terms and how they relate to drawing statistical inferences from data.
Bayesian statistics using r intro
This document provides an introduction to Bayesian statistics using R. It discusses key Bayesian concepts like the prior, likelihood, and posterior distributions. It assumes familiarity with basic probability and probability distributions. Examples are provided to demonstrate Bayesian estimation and inference for binomial and normal distributions. Specifically, it shows how to estimate the probability of success θ in a binomial model and the mean μ in a normal model using different prior distributions and calculating the resulting posterior distributions in R.
1.1.pptx inclusive education for med and bed
The document discusses definitions, concepts, and the need for inclusive education. It defines inclusive education as providing services and support for students with disabilities in age-appropriate general education classrooms. It notes the debate around mainstreaming versus full inclusion. The need for inclusive education is that research shows students perform better academically and socially in inclusive environments, and it helps create an accepting society. The document then provides a brief history of education for students with diverse needs in India, including the establishment of early special schools and initiatives to promote inclusive education through integrated programs and national policies.
sociology significance.pptx educational sociology
Sociology is important for nursing as it enables nurses to understand the social forces that can influence patients, collect important socio-cultural information about patients, understand different social perspectives and challenges patients may face, and educate people about health issues. Sociology provides nurses with knowledge of social problems patients face, techniques for interviews and applying statistics, and ways to facilitate adjustment. It helps nurses understand psycho-social problems which is useful for medical treatment.
More Related Content
Similar to StatWRLecture6.ppt
The t Test for Two Related Samples
Chapter 11 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
Test of significance in Statistics
This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.
Business research methods 2
This document discusses various methods of processing and analyzing data. It describes four key processing operations: editing, coding, classification, and tabulation. It then explains several statistical techniques for analyzing data, including measures of central tendency, dispersion, relationships, probability distributions, hypothesis testing, and tests of means, variances, and association. Hypothesis testing involves defining the null and alternative hypotheses, test statistics, significance levels, and types of errors. Contingency tables can be used to test for association between qualitative variables.
Statistical analysis
The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.
Interpreting ‘tree space’ in the context of very large empirical datasets
Seminar presented at the Maths Department, University of Portsmouth, 19th November 2014
Evolutionary biologists represent actual or hypothesised evolutionary relations between living organisms using phylogenies, directed bifurcating graphs (trees) that describe evolutionary processes in terms of speciation or splitting events (nodes) and elapsed evolutionary time or distance (edges). Molecular evolution itself is largely dominated by mutations in DNA sequences, a stochastic process. Traditionally, probabilistic models of molecular evolution and phylogenies are fitted to DNA sequence data by maximum likelihood on the assumption that a single simple phylogeny will serve to approximate the evolution of a majority of DNA positions in the dataset. However modern studies now routinely sample several orders of magnitude more DNA positions, and this assumption no longer holds. Unfortunately, our conception of ‘tree space’ - a notional multidimensional surface containing all possible phylogenies - is extremely imprecise, and similarly techniques to model phylogeny model fitting in very large datasets are limited. I will show the background to this field and present some of the challenges arising from the present limited analytical framework.
Statistical analysis.pptx
1. Statistical analysis involves collecting, organizing, analyzing data, and drawing inferences about populations based on samples. It includes both descriptive and inferential statistics.
2. The document defines key terms used in statistical analysis like population, sample, statistical analysis, and discusses various statistical measures like mean, median, mode, interquartile range, and standard deviation.
3. The purposes of statistical analysis are outlined as measuring relationships, making predictions, testing hypotheses, and summarizing results. Both parametric and non-parametric statistical analyses are discussed.
Sampling fundamentals
This document discusses key concepts in sampling and statistical inference. It defines parameters and statistics, and explains sampling distributions including the sampling distribution of the mean, proportion, and difference between means. The central limit theorem is covered, stating that as sample size increases, the sampling distribution of the mean approaches a normal distribution. Common distributions used in statistical inference like the t, F, and chi-square distributions are also summarized.
Student t test
The document discusses different types of t-tests, including the one sample t-test, independent samples t-test, and paired t-test. It explains the assumptions and equations for each test and provides examples of their applications. The key differences between the t-test and z-test are also outlined. Specifically, t-tests are used for small sample sizes when the population variance is unknown, while z-tests are for large samples when the variance is known.
Chi-square IMP.ppt
A chi-squared test (χ2) is basically a data analysis on the basis of observations of a random set of variables. Usually, it is a comparison of two statistical data sets. This test was introduced by Karl Pearson in 1900 for categorical data analysis and distribution. So, it was mentioned as Pearson’s chi-squared test.
B.6 tests for mean
This document outlines the steps for conducting a hypothesis test for a mean, including stating the null and alternative hypotheses, conducting a one-sample t-test to compare the sample mean to the hypothesized population mean, calculating the t-statistic and associated p-value, and interpreting the results by comparing the p-value to the significance level to determine whether to reject the null hypothesis.
satatistics Presentation.pptx
This document defines key concepts in statistics including descriptive statistics, inferential statistics, parametric statistics, non-parametric statistics, skewness, kurtosis, the t-test, correlation, and various correlation coefficients. It provides examples of how different statistical tests are used including the one-sample t-test, independent two-sample t-test, and paired sample t-test. Correlation measures the strength and direction of association between two variables.
Stat1008 Tutorial
This document provides an overview and examples for a STAT1008 tutorial covering 9 sections: 1) data collection, 2) variables and statistics, 3) probability, 4) continuous probability distributions, 5) sampling distributions, 6) interval estimation, 7) hypothesis testing, 8) hypothesis tests for variances, and 9) regression analysis. Key concepts are explained for each section along with examples from past exams. The tutorial aims to cover the key points needed to understand the material and do well on exams through challenging questions and general hints.
Lec 5 - Normality Testing.pptx
This document discusses testing for normality in statistical data. It describes comparing an actual data distribution to a theoretical normal distribution based on the data's mean and standard deviation. Several graphical and statistical tests for normality are presented, including Q-Q plots, Kolmogorov-Smirnov tests, and Shapiro-Wilk tests. Large p-values from these tests indicate the data are normally distributed, while small p-values show the data are non-normal. The document emphasizes that normality testing is more important for smaller sample sizes and provides guidelines for selecting an appropriate normality test.
Overview of statistics: Statistical testing (Part I)
Overview of statistics: Statistical testing (Part I)Bioinformatics and Computational Biosciences Branch
The document provides an overview of statistical testing, including:
- When to use parametric vs. nonparametric tests
- When large sample tests or exact tests are needed
- When adjustments for multiple testing are required
It discusses key concepts like null and alternative hypotheses, test statistics, p-values, and type I and II errors. Examples of the Student's t-test and Wilcoxon rank sum test are provided.T distribution | Statistics
T-distribution is the most famous theoretical probability distribution in continuous family of distributions. T distribution is used in estimation where normal distribution cannot be used to estimate population parameters. Copy the link given below and paste it in new browser window to get more information on T distribution:- http://www.transtutors.com/homework-help/statistics/t-distribution.aspx
Point Estimate, Confidence Interval, Hypotesis tests
This document provides an overview of key statistical concepts taught in a statistics lab lesson, including point estimation, confidence intervals, and hypothesis testing. It defines point estimators like the sample mean that summarize a population using a sample. Confidence intervals give a range of values that the population parameter is expected to lie within. Hypothesis testing involves setting up null and alternative hypotheses and using a test statistic and critical value to reject or fail to reject the null hypothesis. Formulas for confidence intervals and hypothesis tests are presented for situations involving normal, t, and binomial distributions.
S5 w1 hypothesis testing & t test
The document discusses hypothesis testing and t-tests, explaining that a hypothesis is a suggested explanation for a phenomenon that can be tested scientifically, and a t-test determines if the means of two groups are statistically different. It provides details on null and alternative hypotheses, how to perform a t-test, and how the t-statistic, degrees of freedom, and p-value are used to evaluate whether to reject the null hypothesis.
Str t-test1
This document describes the steps for conducting an independent samples t-test. The t-test is used to compare the means of two independent groups on a continuous dependent variable. It tests whether the means of the two groups are statistically significantly different from each other. The steps include: 1) stating the null and alternative hypotheses, 2) setting the significance level, 3) calculating the t-value, 4) finding the critical t-value, and 5) making a conclusion about whether to reject the null hypothesis based on the t-values. An example compares math test scores of male and female college students to determine if gender significantly impacts scores.
Statistics
The document discusses key statistical concepts including variance, standard deviation, the normal distribution, frequency distributions, data matrices, properties of good graphs, populations and parameters, hypothesis testing, and point and interval estimation. It provides definitions and examples of these terms and how they relate to drawing statistical inferences from data.
Bayesian statistics using r intro
This document provides an introduction to Bayesian statistics using R. It discusses key Bayesian concepts like the prior, likelihood, and posterior distributions. It assumes familiarity with basic probability and probability distributions. Examples are provided to demonstrate Bayesian estimation and inference for binomial and normal distributions. Specifically, it shows how to estimate the probability of success θ in a binomial model and the mean μ in a normal model using different prior distributions and calculating the resulting posterior distributions in R.
Similar to StatWRLecture6.ppt (20)
Interpreting ‘tree space’ in the context of very large empirical datasets
Interpreting ‘tree space’ in the context of very large empirical datasets
Overview of statistics: Statistical testing (Part I)
Overview of statistics: Statistical testing (Part I)
Point Estimate, Confidence Interval, Hypotesis tests
Point Estimate, Confidence Interval, Hypotesis tests
More from DevarajuBn
1.1.pptx inclusive education for med and bed
The document discusses definitions, concepts, and the need for inclusive education. It defines inclusive education as providing services and support for students with disabilities in age-appropriate general education classrooms. It notes the debate around mainstreaming versus full inclusion. The need for inclusive education is that research shows students perform better academically and socially in inclusive environments, and it helps create an accepting society. The document then provides a brief history of education for students with diverse needs in India, including the establishment of early special schools and initiatives to promote inclusive education through integrated programs and national policies.
sociology significance.pptx educational sociology
Sociology is important for nursing as it enables nurses to understand the social forces that can influence patients, collect important socio-cultural information about patients, understand different social perspectives and challenges patients may face, and educate people about health issues. Sociology provides nurses with knowledge of social problems patients face, techniques for interviews and applying statistics, and ways to facilitate adjustment. It helps nurses understand psycho-social problems which is useful for medical treatment.
4.1.pptx educational issues and related to
The document discusses issues related to teacher education in India. It defines teacher education and outlines its importance. Some key challenges in teacher education are identified. Measures to address these challenges are proposed, including reforms to pre-service and in-service teacher training programs, as well as establishing national bodies to oversee teacher education. Suggestions are made to improve various aspects of teacher education programs.
Integrated Scheme on School Education.pptx
The document outlines the key aspects of Samagra Shiksha Abhiyan, India's new integrated scheme for school education from pre-school to Class 12. Some key points:
1) It integrates existing schemes of Sarva Shiksha Abhiyan, Rashtriya Madhyamik Shiksha Abhiyan, and Teacher Education for a unified approach to school education.
2) The scheme focuses on improving access, quality, and equity in school education through interventions like strengthening infrastructure, digital initiatives, vocational education, and teacher training.
3) Norms across schemes have been revised, with increased funding for various components and expanded coverage from pre-school to senior secondary levels.
Digital Library.pptx educational connect
The document discusses the benefits and challenges of digital libraries. Digital libraries store and provide access to information in digital format, allowing many users to access materials simultaneously from anywhere at any time. However, digital libraries also face challenges like high costs, technological obsolescence of storage media, difficulties in administration and copyright issues, and require expertise to develop. Overall, the document emphasizes that digital libraries help expand access to knowledge but also present obstacles that professionals must address.
Educational sociology for nursing first year
The document discusses the concept of social stratification. It begins by defining social stratification as the ranking of individuals or social groups in a society based on factors like wealth, power, and status. All known societies practice some form of social stratification through systems like caste, class, gender, race, and slavery. Social stratification influences people's life experiences and opportunities by determining their access to resources and life chances based on their social rank and category.
approaches inclusive.pptx development program
Educational approaches and measures for meeting the diverse needs can involve special education, integrated education, inclusive education, and remedial education. At the classroom level, inclusive education requires small group instruction, centers tailored to different ability levels, blending basic and specialized lessons, rotating lessons between groups and teachers, and providing materials at various difficulty levels. It also important to consider the needs of students at the societal, education system, school, and classroom levels to facilitate inclusive practices. In India, services currently include special education classrooms, integrated education blending academic and vocational training, and efforts toward more inclusive education accepting of all students and abilities.
interview-1.pptx types of interview bbgg
This document provides an overview of interview methods for educational research. It defines interviews as a verbal exchange between a researcher and subject used to collect qualitative data. The document outlines different types of interviews including structured, unstructured, personal, phone and email interviews. It provides tips for effective interviews such as having clear questions and establishing rapport. Guidelines for successful interviews include planning, choosing an appropriate time and place, and having a friendly approach. The document discusses merits like flexibility and quick responses, and limitations such as possible biases. Overall, the document serves as a reference for understanding interview methodology for research.
Operating system education. technology.
This seminar discusses operating systems for secondary education information communication technology. It defines an operating system as software that manages computer hardware and allows other programs to run. The document outlines the history, features, types, functions and examples of operating systems. It discusses how operating systems handle processes, memory, devices, files, errors and security. The conclusion states that an operating system acts as an interface between hardware, software and users.
Idealism of philosophy-1.pptx philosophy of education
Idealism is a philosophy that believes reality is fundamentally mental or spiritual rather than physical. It originated with ancient Greek philosophers like Plato and Socrates and was further developed by leaders like Berkeley and Hegel. Idealists view the mind and ideas as more important than physical matter. They believe education should focus on developing students' character, spirituality, and ability to think critically about ideas rather than just conveying facts. Teachers are seen as role models who help students discover truth through questioning and discussion rather than simple information transfer.
MMT-2021.ppt multimedia technologies in education
Multimedia technology refers to the integration of various media like text, images, audio, video and animation to enhance the learning experience. It allows for a variety of instructional methods and modes of learning to accommodate different learning styles and theories. Effective multimedia systems utilize appropriate combinations of media to reinforce learning and help achieve learning objectives. They provide rich, engaging content and permit flexible, self-paced learning. Educational institutions should establish well-equipped multimedia labs to promote the adoption of multimedia approaches in teaching and learning.
Multimedia lab.ppt multimidia lab ppt education
This document describes a multimedia lab workshop that provides 11 scripted modules to teach participants about multimedia equipment, software, and file types. The workshop exposes users to new devices and software in a self-paced environment. Participants work through 20 scripts on over 20 computers with assistance from trainers. The goal is for educators to learn outcomes useful in their teaching.
brm - Copy.pptx of group 7 top telecommunication
Group 7 is comprised of 8 members: Sowmya, Rohith, Arun, Pavan, Ashwath, Pruthivi, Thejaraj, and Preetham. The document discusses the history and development of telecommunication technologies over time, from early speaking tubes and tin can phones to modern cellular networks and satellite phones. It also analyzes Bharat Sanchar Nigam Limited (BSNL), the largest provider of fixed telephony in India, covering its growth, challenges, and strategies to overcome problems in the highly competitive telecommunications market.
listening and stage of listening by yashwini
This document discusses the stages of listening and important components of listening. It outlines five stages of listening: receiving, interpreting, remembering, evaluating, and responding. Receiving involves physically hearing the message and taking note. Interpreting means inferring the speaker's meaning based on one's own experiences. Remembering stores the message for future reference, such as taking notes. Evaluating assesses the importance and quality of the facts provided. Responding is reacting in some way, such as giving feedback or nodding.
FIRST GENERATIONS ppt they are first ones in they're
First-generation learners refer to students who are the first in their family to pursue higher education, as their parents typically only completed primary education. Many first-generation learners come from low-income backgrounds. Universities provide special support programs to help first-generation students with the academic, social, and financial challenges they face navigating higher education without family experience or resources. Such challenges include affording increasing college fees, gender-based obstacles, and stress from managing everything without support. There are estimated to be over 121 million first-generation learners in India today.
I BBA- Unit 2_ Planning and Organizing- Organizing.pptx
This document defines key terms related to organization and organizing. It defines organization as combining work and facilities to efficiently apply effort according to Sheldon. Organizing is providing a business with resources to function according to Fayol. The document also discusses the nature, features, importance and process of organizing. It describes types of organization structures like formal, informal, line, staff, and matrix. It notes advantages, disadvantages and limitations of various organization structures and concludes with topics like centralization, decentralization, delegation and trends in flat organizations.
TEACHER'S DAY.pptx
The document describes the rules and structure for a quiz competition between groups. It includes the following:
- The quiz has multiple rounds with different rules for timing, scoring, and number of questions for each group. Rounds include questions, buzzer rounds, visual rounds, and logo identification.
- Questions cover topics like months, technology, famous people, math problems, puzzles, and images/gifs with questions about details within them.
- Scoring and time limits vary between rounds, with some rounds having negative points for incorrect answers or no passing allowed versus other rounds.
- The overall document lays out a structured format to facilitate a competitive quiz being carried out in an organized fashion between groups.
More from DevarajuBn (20)
technology chapter 1.pptx digital media in education
technology chapter 1.pptx digital media in education
Idealism of philosophy-1.pptx philosophy of education
Idealism of philosophy-1.pptx philosophy of education
FIRST GENERATIONS ppt they are first ones in they're
FIRST GENERATIONS ppt they are first ones in they're
I BBA- Unit 2_ Planning and Organizing- Organizing.pptx
I BBA- Unit 2_ Planning and Organizing- Organizing.pptx
Recently uploaded
How to deliver Powerpoint Presentations.pptx
"How to make and deliver dynamic presentations by making it more interactive to captivate your audience attention"
How Barcodes Can Be Leveraged Within Odoo 17
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
How to Predict Vendor Bill Product in Odoo 17
This slide will guide us through the process of predicting vendor bill products based on previous purchases from the vendor in Odoo 17.
Leveraging Generative AI to Drive Nonprofit Innovation
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Gender and Mental Health - Counselling and Family Therapy Applications and In...
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
A Visual Guide to 1 Samuel | A Tale of Two Hearts
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Practical manual for National Examination Council, Nigeria.
Contains guides on answering questions on the specimens provided
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Benner "Expanding Pathways to Publishing Careers"
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
HYPERTENSION - SLIDE SHARE PRESENTATION.
IT WILL BE HELPFULL FOR THE NUSING STUDENTS
IT FOCUSED ON MEDICAL MANAGEMENT AND NURSING MANAGEMENT.
HIGHLIGHTS ON HEALTH EDUCATION.
Recently uploaded (20)
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
StatWRLecture6.ppt
- 1. Statistics in Water Resources, Lecture 6 • Key theme – T-distribution for distributions where standard deviation is unknown – Hypothesis testing – Comparing two sets of data to see if they are different • Reading: Helsel and Hirsch, Chapter 6 Matched Pair Tests
- 3. t-, z and ChiSquare Source: http://en.wikipedia.org/wiki/Student's_t-distribution
- 4. Normal and t-distributions Normal t-dist for ν = 1 t-dist for ν = 30 t-dist for ν = 5 t-dist for ν = 3 t-dist for ν = 2 t-dist for ν = 10
- 5. • Standard Normal z – X1, … , Xn are independently distributed (μ,σ), and – then is normally distributed with mean 0 and std dev 1 Standard Normal and Student - t • Student’s t-distribution – Applies to the case where the true standard deviation σ is unknown and is replaced by its sample estimate Sn
- 6. 6 p-value is the probability of obtaining the value of the test-statistic if the null hypothesis (Ho) is true If p-value is very small (<0.05 or 0.025) then reject Ho If p-value is larger than α then do not reject Ho
- 9. Helsel and Hirsch p.120
- 10. Box and Whisker Plots of the N data
- 11. Precipitation Water Quality at two sites
- 12. Ranked Precipitation Quality Data Mean concentration is nearly the same but ranks suggest residential concentration is smaller. Is this so?
- 13. Wilcoxon Rank Sum Test Helsel and Hirsch p. 462 This is < 0.05 for a one-sided test, thus reject Ho and say residential concentration is lower than industrial p-value in middle is for P(Wrs > X) or P(Wrs < X*) for m = n = 10 Note that the sum of n = 1, 2, …. 20 = 210 and X + X* = 210 in all cases in this table. Test sum of higher ranks Test sum of lower ranks p-value is 0.024 for Rank sum of 78.5