Inferential statistics is the process of using data analysis to infer properties of a probability distribution. It involves using a sample of data to draw conclusions or generalizations about a broader population.
The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.
Logistic regression is a statistical method used to predict a binary or categorical dependent variable from continuous or categorical independent variables. It generates coefficients to predict the log odds of an outcome being present or absent. The method assumes a linear relationship between the log odds and independent variables. Multinomial logistic regression extends this to dependent variables with more than two categories. An example analyzes high school student program choices using writing scores and socioeconomic status as predictors. The model fits significantly better than an intercept-only model. Increases in writing score decrease the log odds of general versus academic programs.
The two major areas of statistics are: descriptive statistics and inferential statistics. In this presentation, the difference between the two are shown including examples.
Maintenance of facilities and equipment is important to achieve high quality, reliability, and efficient operations. There are various types of maintenance including planned, preventive, corrective, and breakdown maintenance. The objectives of maintenance are to maximize equipment life, minimize costs, and ensure safety. An effective approach is condition-based maintenance which uses sensors and monitoring to predict failures before they occur. This improves system availability while reducing downtime and maintenance costs. Finally, proper planning and scheduling of maintenance jobs is important to efficiently execute repairs.
Project Monitorig and Evaluation_Data Collection Methods is a Presentation by William Afani Paul for a Project MEAL Masterclass by Excellence Foundation for South Sudan
This session is designed to equip participants with essential knowledge and skills in monitoring and evaluating projects effectively.
During this masterclass, participants will delve into the fundamental concepts, tools, and techniques of project monitoring and evaluation. Through interactive discussions, case studies, and practical exercises, attendees will gain a comprehensive understanding of MEAL principles and their application in diverse project contexts.
Key Objectives
Understand the importance of project monitoring and evaluation in ensuring project success.
Learn how to develop and implement effective monitoring and evaluation frameworks.
Explore various data collection methods and analysis techniques for monitoring and evaluation purposes.
Gain insights into utilizing monitoring and evaluation findings to inform decision-making and improve project outcomes.
Learning Outcomes: By the end of the masterclass, participants will able to:
Define key concepts related to project monitoring and evaluation.
Develop a monitoring and evaluation plan tailored to specific project requirements.
Apply appropriate data collection methods and tools for monitoring and evaluation activities.
Utilize monitoring and evaluation findings to enhance project performance and impact.
A sample design is a definite plan for obtaining a sample from a given population. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.
This document discusses depression, mania, and various antidepressant medications. It covers the symptoms of depression and mania. It then discusses various classes of antidepressants including SSRIs, SNRIs, TCAs, MAOIs, and atypical antidepressants. For each class, it describes the mechanisms of action, therapeutic uses, adverse effects, and examples of medications within the class.
larder food production BHM or B.SC(HHA) 5th SemesterManojRaheja4
This presentation contains Meaning of Larder, Equipments of Larder, Layout of Larder , common terms . coordination with other Department , yield Testing , Functions , hierarchy and sections of Larder and Responsibilities of Larder Chef
Through this ppt you could learn what is Wilcoxon Signed Ranked Test. This will teach you the condition and criteria where it can be run and the way to use the test.
Inferential statistics takes data from a sample and makes inferences about the larger population from which the sample was drawn.
Make use of the PPT to have a better understanding of Inferential statistics.
In Hypothesis testing parametric test is very important. in this ppt you can understand all types of parametric test with assumptions which covers Types of parametric, Z-test, T-test, ANOVA, F-test, Chi-Square test, Meaning of parametric, Fisher, one-sample z-test, Two-sample z-test, Analysis of Variance, two-way ANOVA.
Subscribe to Vision Academy for Video assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Statistical tests can be used to analyze data in two main ways: descriptive statistics provide an overview of data attributes, while inferential statistics assess how well data support hypotheses and generalizability. There are different types of tests for comparing means and distributions between groups, determining if differences or relationships exist in parametric or non-parametric data. The appropriate test depends on the question being asked, number of groups, and properties of the data.
Inferential statistics use samples to make generalizations about populations. It allows researchers to test theories designed to apply to entire populations even though samples are used. The goal is to determine if sample characteristics differ enough from the null hypothesis, which states there is no difference or relationship, to justify rejecting the null in favor of the research hypothesis. All inferential tests examine the size of differences or relationships in a sample compared to variability and sample size to evaluate how deviant the results are from what would be expected by chance alone.
This document provides an overview of non-parametric statistics. It defines non-parametric tests as those that make fewer assumptions than parametric tests, such as not assuming a normal distribution. The document compares and contrasts parametric and non-parametric tests. It then explains several common non-parametric tests - the Mann-Whitney U test, Wilcoxon signed-rank test, sign test, and Kruskal-Wallis test - and provides examples of how to perform and interpret each test.
This document provides an overview of non-parametric tests presented by Ms. Prajakta Sawant. It discusses non-parametric tests as distribution-free statistical tests that do not require assumptions about the underlying population distribution. Common non-parametric tests described include the Wilcoxon rank-sum test, Kruskal-Wallis test, Spearman's rank correlation coefficient, and the chi-square test. Examples are provided for each test to illustrate their application and interpretation.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
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.
This document provides an overview of statistical tests and hypothesis testing. It discusses the four steps of hypothesis testing, including stating hypotheses, setting decision criteria, computing test statistics, and making a decision. It also describes different types of statistical analyses, common descriptive statistics, and forms of statistical relationships. Finally, it provides examples of various parametric and nonparametric statistical tests, including t-tests, ANOVA, chi-square tests, correlation, regression, and decision trees.
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.
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
DIstinguish between Parametric vs nonparametric testsai prakash
This document summarizes parametric and nonparametric tests. Parametric tests make assumptions about the population based on known parameters, while nonparametric tests make no assumptions about the population. Some examples of parametric tests provided are t-test, F-test, z-test, and ANOVA, while examples of nonparametric tests include Mann-Whitney, rank sum test, and Kruskal-Wallis test. The key differences between parametric and nonparametric tests are that parametric tests are based on population parameters and distributions while nonparametric tests are not, and parametric tests can only be applied to variable data while nonparametric tests can be used for variable or attribute data.
Advance Statistics - Wilcoxon Signed Rank TestJoshua Batalla
The Wilcoxon signed-rank test is a non-parametric test used to compare two related samples, such as repeated measurements on a single sample, to assess whether their population mean ranks differ. It can be used as a non-parametric alternative to the paired Student's t-test when the population cannot be assumed to be normally distributed. The test involves ranking the differences between pairs of observations and comparing the sum of the ranks of the positive differences to what would be expected if there was no effect. The document provides information on the requirements, formula, and an example application of the Wilcoxon signed-rank test.
The document discusses parametric and non-parametric tests. It provides examples of commonly used non-parametric tests including the Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test. For each test, it gives the steps to perform the test and interpret the results. Non-parametric tests make fewer assumptions than parametric tests and can be used when the data is ordinal or does not meet the assumptions of parametric tests. They provide a distribution-free alternative for analyzing data.
This document discusses parametric tests used for statistical analysis. It introduces t-tests, ANOVA, Pearson's correlation coefficient, and Z-tests. T-tests are used to compare means of small samples and include one-sample, unpaired two-sample, and paired two-sample t-tests. ANOVA compares multiple population means and includes one-way and two-way ANOVA. Pearson's correlation measures the strength of association between two continuous variables. Z-tests compare means or proportions of large samples. Key assumptions and calculations for each test are provided along with examples. The document emphasizes the importance of choosing the appropriate statistical test for research.
This document presents information about regression analysis. It defines regression as the dependence of one variable on another and lists the objectives as defining regression, describing its types (simple, multiple, linear), assumptions, models (deterministic, probabilistic), and the method of least squares. Examples are provided to illustrate simple regression of computer speed on processor speed. Formulas are given to calculate the regression coefficients and lines for predicting y from x and x from y.
This document provides an overview of an introductory biostatistics course. The course covers topics such as descriptive statistics, probability, sampling methods, and probability distributions. Lecture 1 introduces biostatistics and discusses its importance in fields like public health and medicine. Biostatistics is applied to analyze biological and health data and help address questions like disease trends, at-risk populations, and health standards. It aids decision-making under uncertainty and helps identify health issues, evaluate programs, and conduct research.
The Wilcoxon signed-rank test is a non-parametric statistical test used to compare two related samples or repeated measurements on a single sample to assess if their population mean ranks differ. It can be used as an alternative to the paired t-test when the population cannot be assumed to be normally distributed. The test involves ranking the differences between paired observations, ignoring the signs of the differences, and comparing the sum of the ranks of the positive or negative differences to critical values to determine if there are statistically significant differences between the samples. A limitation is that observations with a difference of zero are discarded, which can be a concern if samples come from a discrete distribution.
This document provides an overview of descriptive and inferential statistics, as well as regression analysis. Descriptive statistics summarize and describe data through measures like averages and proportions. Inferential statistics make predictions about larger populations based on samples and allow generalizing beyond the data. Regression analysis helps understand relationships between dependent and independent variables and can be used for prediction and exploring variable relationships. Common uses of these statistical techniques include medical research, demographics, forecasting, and exploring causal relationships.
Through this ppt you could learn what is Wilcoxon Signed Ranked Test. This will teach you the condition and criteria where it can be run and the way to use the test.
Inferential statistics takes data from a sample and makes inferences about the larger population from which the sample was drawn.
Make use of the PPT to have a better understanding of Inferential statistics.
In Hypothesis testing parametric test is very important. in this ppt you can understand all types of parametric test with assumptions which covers Types of parametric, Z-test, T-test, ANOVA, F-test, Chi-Square test, Meaning of parametric, Fisher, one-sample z-test, Two-sample z-test, Analysis of Variance, two-way ANOVA.
Subscribe to Vision Academy for Video assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Statistical tests can be used to analyze data in two main ways: descriptive statistics provide an overview of data attributes, while inferential statistics assess how well data support hypotheses and generalizability. There are different types of tests for comparing means and distributions between groups, determining if differences or relationships exist in parametric or non-parametric data. The appropriate test depends on the question being asked, number of groups, and properties of the data.
Inferential statistics use samples to make generalizations about populations. It allows researchers to test theories designed to apply to entire populations even though samples are used. The goal is to determine if sample characteristics differ enough from the null hypothesis, which states there is no difference or relationship, to justify rejecting the null in favor of the research hypothesis. All inferential tests examine the size of differences or relationships in a sample compared to variability and sample size to evaluate how deviant the results are from what would be expected by chance alone.
This document provides an overview of non-parametric statistics. It defines non-parametric tests as those that make fewer assumptions than parametric tests, such as not assuming a normal distribution. The document compares and contrasts parametric and non-parametric tests. It then explains several common non-parametric tests - the Mann-Whitney U test, Wilcoxon signed-rank test, sign test, and Kruskal-Wallis test - and provides examples of how to perform and interpret each test.
This document provides an overview of non-parametric tests presented by Ms. Prajakta Sawant. It discusses non-parametric tests as distribution-free statistical tests that do not require assumptions about the underlying population distribution. Common non-parametric tests described include the Wilcoxon rank-sum test, Kruskal-Wallis test, Spearman's rank correlation coefficient, and the chi-square test. Examples are provided for each test to illustrate their application and interpretation.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
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.
This document provides an overview of statistical tests and hypothesis testing. It discusses the four steps of hypothesis testing, including stating hypotheses, setting decision criteria, computing test statistics, and making a decision. It also describes different types of statistical analyses, common descriptive statistics, and forms of statistical relationships. Finally, it provides examples of various parametric and nonparametric statistical tests, including t-tests, ANOVA, chi-square tests, correlation, regression, and decision trees.
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.
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
DIstinguish between Parametric vs nonparametric testsai prakash
This document summarizes parametric and nonparametric tests. Parametric tests make assumptions about the population based on known parameters, while nonparametric tests make no assumptions about the population. Some examples of parametric tests provided are t-test, F-test, z-test, and ANOVA, while examples of nonparametric tests include Mann-Whitney, rank sum test, and Kruskal-Wallis test. The key differences between parametric and nonparametric tests are that parametric tests are based on population parameters and distributions while nonparametric tests are not, and parametric tests can only be applied to variable data while nonparametric tests can be used for variable or attribute data.
Advance Statistics - Wilcoxon Signed Rank TestJoshua Batalla
The Wilcoxon signed-rank test is a non-parametric test used to compare two related samples, such as repeated measurements on a single sample, to assess whether their population mean ranks differ. It can be used as a non-parametric alternative to the paired Student's t-test when the population cannot be assumed to be normally distributed. The test involves ranking the differences between pairs of observations and comparing the sum of the ranks of the positive differences to what would be expected if there was no effect. The document provides information on the requirements, formula, and an example application of the Wilcoxon signed-rank test.
The document discusses parametric and non-parametric tests. It provides examples of commonly used non-parametric tests including the Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test. For each test, it gives the steps to perform the test and interpret the results. Non-parametric tests make fewer assumptions than parametric tests and can be used when the data is ordinal or does not meet the assumptions of parametric tests. They provide a distribution-free alternative for analyzing data.
This document discusses parametric tests used for statistical analysis. It introduces t-tests, ANOVA, Pearson's correlation coefficient, and Z-tests. T-tests are used to compare means of small samples and include one-sample, unpaired two-sample, and paired two-sample t-tests. ANOVA compares multiple population means and includes one-way and two-way ANOVA. Pearson's correlation measures the strength of association between two continuous variables. Z-tests compare means or proportions of large samples. Key assumptions and calculations for each test are provided along with examples. The document emphasizes the importance of choosing the appropriate statistical test for research.
This document presents information about regression analysis. It defines regression as the dependence of one variable on another and lists the objectives as defining regression, describing its types (simple, multiple, linear), assumptions, models (deterministic, probabilistic), and the method of least squares. Examples are provided to illustrate simple regression of computer speed on processor speed. Formulas are given to calculate the regression coefficients and lines for predicting y from x and x from y.
This document provides an overview of an introductory biostatistics course. The course covers topics such as descriptive statistics, probability, sampling methods, and probability distributions. Lecture 1 introduces biostatistics and discusses its importance in fields like public health and medicine. Biostatistics is applied to analyze biological and health data and help address questions like disease trends, at-risk populations, and health standards. It aids decision-making under uncertainty and helps identify health issues, evaluate programs, and conduct research.
The Wilcoxon signed-rank test is a non-parametric statistical test used to compare two related samples or repeated measurements on a single sample to assess if their population mean ranks differ. It can be used as an alternative to the paired t-test when the population cannot be assumed to be normally distributed. The test involves ranking the differences between paired observations, ignoring the signs of the differences, and comparing the sum of the ranks of the positive or negative differences to critical values to determine if there are statistically significant differences between the samples. A limitation is that observations with a difference of zero are discarded, which can be a concern if samples come from a discrete distribution.
This document provides an overview of descriptive and inferential statistics, as well as regression analysis. Descriptive statistics summarize and describe data through measures like averages and proportions. Inferential statistics make predictions about larger populations based on samples and allow generalizing beyond the data. Regression analysis helps understand relationships between dependent and independent variables and can be used for prediction and exploring variable relationships. Common uses of these statistical techniques include medical research, demographics, forecasting, and exploring causal relationships.
This document discusses key concepts in biostatistics. It defines biostatistics as the application of statistics in the medical field, involving collecting and analyzing data and interpreting results to make decisions. It describes factors like sample size, study design, and effect size that influence statistical power. Parametric and non-parametric tests are covered, along with the t-test, ANOVA, correlation coefficients, linear regression, the Wilcoxon signed-rank test, and the chi-square test as examples of important statistical analyses. P-values are defined as a measure of how likely observed results would be assuming the null hypothesis is true.
The document provides an overview of quantitative and qualitative data analysis methods. It discusses the differences between quantitative and qualitative data/analysis, as well as various statistical and coding techniques used in each method. For quantitative analysis, it covers descriptive statistics, inferential statistics, univariate analysis including measures of central tendency and variation, bivariate analysis including crosstabulation and correlation, and multivariate analysis including elaboration models. For qualitative analysis, it discusses social anthropological versus interpretivist approaches, the relationship between data and ideas, strengths and weaknesses, and typical analysis steps including coding, data reduction, and conclusion drawing.
This document provides an overview of common statistical tools used for descriptive statistics, inferential statistics, regression analysis, correlation analysis, probability distributions, and sampling techniques. Descriptive statistics summarize and present data through measures like mean, median, mode, and standard deviation. Inferential statistics allow researchers to make generalizations about populations based on samples using techniques like t-tests, ANOVA, and chi-square tests. Regression analysis and correlation analysis examine relationships between variables. Probability distributions assign probabilities to possible outcomes, and sampling techniques select subsets from populations.
The document discusses various techniques for analyzing different types of data in research. It describes statistical procedures like parametric and non-parametric statistics that have assumptions about the type of data. Qualitative data analysis involves deriving categories from the text or applying existing systems. Descriptive research uses frequencies, central tendencies, and variabilities to analyze data. Correlational research examines relationships between variables using correlations. Multivariate research analyzes multiple dependent and independent variables simultaneously using multiple regression, discriminant analysis, and factor analysis. Experimental research compares groups using t-tests and analyzes more than two groups with one-way ANOVA.
Respond using one or more of the following approachesAsk a promickietanger
Respond using one or more of the following approaches:
Ask a probing question, substantiated with additional background information, and evidence.
Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
Group B
Inferential Statistics- Based on probability; used to draw conclusions or make generalizations about a given population or problem.
Example: “What can I infer about 5-minute Apgar scores of premature babies (the population) after calculating a mean Apgar score of 7.5 in a sample of 300 premature babies?” (McGonigle & Mastrain, p. 376, 2017).
Sampling Distributions- A sampling distribution is the frequency distribution of a statistic over many random samples from a single population.
Sampling Distribution of the Mean - as an example we randomly draw test scores from 25 students out of a total group of 5,000. We then calculate the mean, then draw a new group and repeat; each mean will serve as one datum, or data point.
Hypothesis Testing- is the use of statistics to determine the probability that a given hypothesis is true.
Null Hypothesis- the hypothesis that there is no significant difference between specified populations; or differences can be attributed to sampling or experimental error
Type 1 Error- This error occurs when we reject the null hypothesis when we should have retained it.
Type 2 Error- This error occurs when we fail to reject the null hypothesis. In other words, we believe that there isn’t a genuine effect when actually there is one.
Parametric statistics – A class of statistical tests that involve assumptions about the distribution of the variables and the estimation of a parameter.
Nonparametic statistics – A class of statistical tests that do not involve stringent assumptions about the distribution of variables. Between-subject design – A research design in which separate groups of people are compared (e.g. smokers and nonsmokers; intervention and control group subjects). Within-subject design – A research design in which a single group of participants is compared under different conditions or different points in time (e.g. before and after surgery).
Two classes of Statistical Tests:
Parametric tests - tests involving an estimation of a parameter, the use of interval or ratio-level data, and the assumption of normally distributed variables. Include t-tests and ANOVA.
Nonparametric tests - used when the data are nominal or ordinal or when a normal distribution cannot be assumed. Include the Mann-Whitney U test, Wilcoxon signed - rank test, and Kruskal - Wallis test.
Statistical Tests
T-test parametric procedure identifying mean differences for two independent groups, like experiment versus control or dependent groups, like pretreatment and post-treatment scores.
One - way ANOVA - tests the relationship between one categorical independent variable, such as different interventions, and a continuous dependent variable.
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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.
Chapter 13 Data Analysis Inferential Methods and Analysis of Time SeriesInternational advisers
This document discusses inferential statistics and time series analysis. It defines inferential statistics as ways to generalize statistics from a sample to a larger population. Common inferential methods include correlation, linear regression, ANOVA, and time series analysis. Correlation measures relationships between variables while regression predicts outcomes. ANOVA compares group means. Time series analysis models trends, seasonality, and irregular patterns over time.
This document discusses hypothesis testing and the scientific method. It provides details on:
- The key steps of the scientific method including observation, formulation of a question, data collection, hypothesis testing, analysis and conclusion.
- The different types of hypotheses such as simple vs complex, directional vs non-directional, null vs alternative.
- The steps of hypothesis testing including stating the null and alternative hypotheses, using a test statistic, determining the p-value and significance level, and deciding whether to reject or fail to reject the null hypothesis.
- Examples are given to illustrate hypothesis testing and how the p-value is compared to the significance level to determine if the null hypothesis can be rejected.
Methods of Statistical Analysis & Interpretation of Data..pptxheencomm
The document discusses various statistical analysis techniques for making sense of numerical data, including descriptive statistics like measures of central tendency and dispersion to describe basic features of data, and inferential statistics to make predictions about a larger population based on a sample. Common inferential techniques covered are correlation, regression analysis, analysis of variance, and hypothesis testing to compare data against assumptions. The goal of these statistical methods is to derive meaningful insights from research data.
Sampling for Various Kinds of Quantitative Research.pptxTanzeelaBashir1
This document defines key concepts related to sampling for quantitative research. It discusses types of quantitative research designs including survey, experimental, correlational, and causal-comparative research. It also defines sampling, populations, the sampling process, sampling frames, and common sampling techniques. Probability sampling methods allow statistical inference while non-probability sampling does not. Sample size and how it relates to population parameters and statistics are also addressed.
1. Statistical tests are used in fisheries science to test hypotheses and make quantitative decisions about fisheries processes. Common statistical tests include correlation tests, comparison of means tests, regression analyses, and hypothesis tests.
2. The appropriate statistical test to use depends on the research design, data distribution, and variable type. Parametric tests are used for normally distributed data, while non-parametric tests are used when assumptions are not met.
3. Accuracy of statistical tests relies on quality survey data. Both fishery-dependent and fishery-independent data are important, though confounding factors must be considered with dependent data. Proper study design and use of statistics allows prediction of fish production.
Artificial intelligence Types Weak AI vs Strong AI | ANI AGI ASIMuhammad Yousuf Ali
Artificial Intelligence is one of the emerging technology which has influence in our daily lives. Artificial intelligence are divided in to different types by its capacities like Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Super Intelligence(ASI). In other way AI has also divided in to four parts by its functionality, Reactive AI, Limited Memory AI, Theory of Mind AI and Self Aware AI.
This presentation about three important questions related to the Artificial Intelligence.
Q.1 What is Artificial Intelligence?
Q.2 What are the usage and application of AI in our daily life?
Q.3 What are the types of Artificial Intelligence?
Notes of this video lecture please click here
https://profileusuf.wordpress.com/artificial-intelligence-types/
This presentation titled "Systematic Review in the Realm of Social Science "at IFLA webinar titled " “Systematic Review Success: An Introductory Workshop for Librarians & Information Professionals " organized by the IFLA Social Science Library standing committee on 05 June 2024.
200 plus live audience attended this webinar. This presentation discuss the systematic review study in the field of social science domain. How can systematic review done in the filed of social science what is the role of librarian in systematic review study.
Altmetric Trends in Research Baharia University Workshop 22 April 2024.pptxMuhammad Yousuf Ali
This slides about the one of the latest trends in research which is known as Altmetric. try to explore the following questions.
What is Altmetric?
What are the function of Altmetric?
Why is Altmetric Important for the researcher?
Where can A researcher find his or her article research altmetric score?
This presentation about the Augmented Reality :
Augmented reality (AR) is a real-time experience that combines the real world with computer-generated content. This content can include visual, auditory, haptic, somatosensory, and olfactory elements. Augmented reality is an interactive experience that enhances the real world with computer-generated perceptual information. Using software, apps, and hardware such as AR glasses, augmented reality overlays digital content onto real-life environments and objects.
Types of Augmented Reality:-
1. Marker-based AR
2. Markerless AR
3. Projection-based AR
4. Superimposition-based AR
5. Location-based AR
Descriptive Statistics: Types of Descriptive Statistics and it ImportanceMuhammad Yousuf Ali
Descriptive statistics is a branch of statistics that involves summarizing, organizing, and presenting data in a meaningful way. It focuses on describing and analyzing a dataset’s main features and characteristics without making any generalizations or inferences to a larger population.
There are four types of Descriptive Statistics
1. Measure Central Tendency
2. Measures of Variability
3. Measure of Distribution/Frequency Distribution
4. Measure of Position
Scoping Review
Scoping reviews are a "preliminary assessment of potential size and scope of available research literature. Aims to identify nature and extent of research evidence (usually including ongoing research)."
1. What is Scoping Review ?
2. Alternative Name of Scoping Review?
3. Characteristics of Scoping Review?
4. Limitation of Scoping Review?
Service innovation and performance-based evaluation of university libraries i...Muhammad Yousuf Ali
This presentation was presented
PhD Open Defense presentation at The Islamia University Bahawalpur on 31 July 2023. The title PhD study was "Service innovation and performance-based evaluation of university libraries in the
age of Artificial Intelligence". The PhD scholar successfully defended his dissertation.
There are four main types of software.
1. Application Software
2. System Software
3. Programming Software
4. Driver Software
This presentation explain about four main types of software with examples.
Basics of Computing Systems; Identifying Computer SystemsMuhammad Yousuf Ali
This Presentation about the auditing of Information system and this presenation discribe about the computer systems, types of computer and category of computers.
This presentation discuss four key points about the accession register.
What is An Accession Register?
What are the format of Accession Registered?
How many columns in Accession Register?
What is importance of Accession Register?
Artificial Intelligence adoption factor in the University libraries of Pakist...Muhammad Yousuf Ali
This study examines factors influencing the adoption of artificial intelligence (AI) technologies in university libraries in Pakistan using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The study collected survey responses from 187 university librarians and analyzed the data using structural equation modeling to test the UTAUT model. The results found attitude to be a major factor influencing behavioral intention to adopt AI, with performance expectancy and effort expectancy significantly impacting attitude. Social influence was also found to significantly relate to behavioral intention, while facilitating conditions did not relate to behavioral intention. The study concludes attitude is an important variable for AI adoption in Pakistani university libraries based on the UTAUT model.
Video based virtual learning tools Usage by the University students: An OverviewMuhammad Yousuf Ali
This is collaborative work under the guidance of Dr. Salman Bin Naeem and Professor Dr Rubina Bhatti and presented at “1st International conference Interdisciplinary approach in social sciences” 12 November 2021
Linkedin profile: Advantages of LinkedIn Profile and NetworkingMuhammad Yousuf Ali
This presentation describe about the LinkedIn Networking introduction with try to answer the following questions
Q 1 What is LinkedIn?
Q 2 Who can create the Profile on LinkedIn Network?
Q 3 What are the advantages of a LinkedIn Profile?
The document discusses the role of artificial intelligence in library services, describing how AI tools such as chatbots, robotics, natural language processing, and computer vision can assist with tasks like reference services, cataloging, security, and preservation of materials. It provides a brief history of AI in libraries and outlines several common AI tools and their applications in technical and user services.
Artificial Intelligence reached in libraries, different tools of artificial intelligence used in the libraries i.e. Most poplars are
1) System Experts
2) Natural Language Processing
3) Pattern Recognition
4) Robotics
5) Big Data
6) Data Mining
7) Image Processing
further more view the presentation
This presentation about the Data literacy concept and usage in the context of library and Information science professional and Librarianship. Basic core concepts of data literacy are discussed in this presentation.
Google Scholar metric of Pakistani LIS scholars: An overviewMuhammad Yousuf Ali
The document analyzes the Google Scholar metrics of Pakistani LIS scholars. It finds that 45 Pakistani LIS scholars have profiles on Google Scholar, with a total of 858 publications and 4,653 citations. There is a strong correlation found between publications and citations, as well as between citations and h-index/i10-index. However, the presence of Pakistani scholars on Google Scholar is still low, possibly because it lacks features like readership data that other sites provide. The study aims to increase awareness of research metrics and scholarly networking in Pakistan.
Redesigning Education as a Cognitive Ecosystem: Practical Insights into Emerg...Leonel Morgado
Slides used at the Invited Talk at the Harvard - Education University of Hong Kong - Stanford Joint Symposium, "Emerging Technologies and Future Talents", 2025-05-10, Hong Kong, China.
This chapter provides an in-depth overview of the viscosity of macromolecules, an essential concept in biophysics and medical sciences, especially in understanding fluid behavior like blood flow in the human body.
Key concepts covered include:
✅ Definition and Types of Viscosity: Dynamic vs. Kinematic viscosity, cohesion, and adhesion.
⚙️ Methods of Measuring Viscosity:
Rotary Viscometer
Vibrational Viscometer
Falling Object Method
Capillary Viscometer
🌡️ Factors Affecting Viscosity: Temperature, composition, flow rate.
🩺 Clinical Relevance: Impact of blood viscosity in cardiovascular health.
🌊 Fluid Dynamics: Laminar vs. turbulent flow, Reynolds number.
🔬 Extension Techniques:
Chromatography (adsorption, partition, TLC, etc.)
Electrophoresis (protein/DNA separation)
Sedimentation and Centrifugation methods.
Lecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptxArshad Shaikh
*Phylum Arthropoda* includes animals with jointed appendages, segmented bodies, and exoskeletons. It's divided into subphyla like Chelicerata (spiders), Crustacea (crabs), Hexapoda (insects), and Myriapoda (millipedes, centipedes). This phylum is one of the most diverse groups of animals.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003
what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
Rock Art As a Source of Ancient Indian HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
Form View Attributes in Odoo 18 - Odoo SlidesCeline George
Odoo is a versatile and powerful open-source business management software, allows users to customize their interfaces for an enhanced user experience. A key element of this customization is the utilization of Form View attributes.
How to Manage Purchase Alternatives in Odoo 18Celine George
Managing purchase alternatives is crucial for ensuring a smooth and cost-effective procurement process. Odoo 18 provides robust tools to handle alternative vendors and products, enabling businesses to maintain flexibility and mitigate supply chain disruptions.
Ancient Stone Sculptures of India: As a Source of Indian HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
What is the Philosophy of Statistics? (and how I was drawn to it)jemille6
What is the Philosophy of Statistics? (and how I was drawn to it)
Deborah G Mayo
At Dept of Philosophy, Virginia Tech
April 30, 2025
ABSTRACT: I give an introductory discussion of two key philosophical controversies in statistics in relation to today’s "replication crisis" in science: the role of probability, and the nature of evidence, in error-prone inference. I begin with a simple principle: We don’t have evidence for a claim C if little, if anything, has been done that would have found C false (or specifically flawed), even if it is. Along the way, I’ll sprinkle in some autobiographical reflections.
Happy May and Happy Weekend, My Guest Students.
Weekends seem more popular for Workshop Class Days lol.
These Presentations are timeless. Tune in anytime, any weekend.
<<I am Adult EDU Vocational, Ordained, Certified and Experienced. Course genres are personal development for holistic health, healing, and self care. I am also skilled in Health Sciences. However; I am not coaching at this time.>>
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Understanding Vibrations
If not experienced, it may seem weird understanding vibes? We start small and by accident. Usually, we learn about vibrations within social. Examples are: That bad vibe you felt. Also, that good feeling you had. These are common situations we often have naturally. We chit chat about it then let it go. However; those are called vibes using your instincts. Then, your senses are called your intuition. We all can develop the gift of intuition and using energy awareness.
Energy Healing
First, Energy healing is universal. This is also true for Reiki as an art and rehab resource. Within the Health Sciences, Rehab has changed dramatically. The term is now very flexible.
Reiki alone, expanded tremendously during the past 3 years. Distant healing is almost more popular than one-on-one sessions? It’s not a replacement by all means. However, its now easier access online vs local sessions. This does break limit barriers providing instant comfort.
Practice Poses
You can stand within mountain pose Tadasana to get started.
Also, you can start within a lotus Sitting Position to begin a session.
There’s no wrong or right way. Maybe if you are rushing, that’s incorrect lol. The key is being comfortable, calm, at peace. This begins any session.
Also using props like candles, incenses, even going outdoors for fresh air.
(See Presentation for all sections, THX)
Clearing Karma, Letting go.
Now, that you understand more about energies, vibrations, the practice fusions, let’s go deeper. I wanted to make sure you all were comfortable. These sessions are for all levels from beginner to review.
Again See the presentation slides, Thx.
How to Add Customer Note in Odoo 18 POS - Odoo SlidesCeline George
In this slide, we’ll discuss on how to add customer note in Odoo 18 POS module. Customer Notes in Odoo 18 POS allow you to add specific instructions or information related to individual order lines or the entire order.
How to Configure Scheduled Actions in odoo 18Celine George
Scheduled actions in Odoo 18 automate tasks by running specific operations at set intervals. These background processes help streamline workflows, such as updating data, sending reminders, or performing routine tasks, ensuring smooth and efficient system operations.
Happy May and Taurus Season.
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This slide is an exercise for the inquisitive students preparing for the competitive examinations of the undergraduate and postgraduate students. An attempt is being made to present the slide keeping in mind the New Education Policy (NEP). An attempt has been made to give the references of the facts at the end of the slide. If new facts are discovered in the near future, this slide will be revised.
This presentation is related to the brief History of Kashmir (Part-I) with special reference to Karkota Dynasty. In the seventh century a person named Durlabhvardhan founded the Karkot dynasty in Kashmir. He was a functionary of Baladitya, the last king of the Gonanda dynasty. This dynasty ruled Kashmir before the Karkot dynasty. He was a powerful king. Huansang tells us that in his time Taxila, Singhpur, Ursha, Punch and Rajputana were parts of the Kashmir state.