Criterion to select the test
•
0 likes•212 views
This document outlines statistical tests to select based on experimental design factors. It recommends using a t-test or Z-test for unmatched or matched data with one variable comparing two samples, one-way ANOVA and Tukey's test for comparing two or more samples with one variable, two-way ANOVA and Tukey's test when having two variables with all categories combined, two-way nested ANOVA when one variable is nested within another, and three-way ANOVA to test hypotheses when having three treatment variables with all categories combined.
Report
Share
Report
Share
Download to read offline
Recommended
T test
This presentation educates you about T-Test, Key takeways, Assumptions for Performing a t-test, Types of t-tests, One sample t-test, Independent two-sample t-test and Paired sample t-test.
For more topics Stay tuned with Learnbay
One way AVOVA
1) Three groups were given different techniques to lower blood pressure and their reductions were recorded.
2) A one-way ANOVA test was conducted to determine if there were differences in the mean reductions among the three groups.
3) The calculated F statistic (9.17) was greater than the critical value (3.8853), so the null hypothesis that the means were equal was rejected. Therefore, there was sufficient evidence that at least one group's mean reduction was different from the others.
T-test in statistics for data science
A t-test is a type of inferential statistic which is used to determine if there is a significant difference between the means of two groups which may be
related in certain features. The T-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population
Anova, ancova
This document provides an overview of common statistical tests used to analyze data, including the t-test, ANOVA, and ANCOVA. It describes the assumptions, test statistics, and SAS code for each test. The t-test is used to compare two population means or determine if two sets of data are significantly different. ANOVA examines differences among group means and can be one-way or two-way. ANCOVA combines aspects of ANOVA and regression by including categorical and continuous predictors to examine the influence of independent variables on a dependent variable while controlling for a covariate.
Analysis of Variance - Meaning and Types
This vides briefed the meaning, Introduction, Definition, Application, Classification and Types of ANOVA.
Video link https://youtu.be/YLHGYVMH2T4
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Wilcoxon signed rank test
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.
Unpaired t Test Family
Comparing the unpaired t test, small sample formula with the one with large sample formula and the Welch's test. The latter is an unpaired t test for samples of unequal sizes with unequal variances.
F test and ANOVA
A brief description of F Test and ANOVA for Msc Life Science students. I have taken the example slides from youtube where an excellent explanation is available.
Here is the link : https://www.youtube.com/watch?v=-yQb_ZJnFXw
Recommended
T test
This presentation educates you about T-Test, Key takeways, Assumptions for Performing a t-test, Types of t-tests, One sample t-test, Independent two-sample t-test and Paired sample t-test.
For more topics Stay tuned with Learnbay
One way AVOVA
1) Three groups were given different techniques to lower blood pressure and their reductions were recorded.
2) A one-way ANOVA test was conducted to determine if there were differences in the mean reductions among the three groups.
3) The calculated F statistic (9.17) was greater than the critical value (3.8853), so the null hypothesis that the means were equal was rejected. Therefore, there was sufficient evidence that at least one group's mean reduction was different from the others.
T-test in statistics for data science
A t-test is a type of inferential statistic which is used to determine if there is a significant difference between the means of two groups which may be
related in certain features. The T-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population
Anova, ancova
This document provides an overview of common statistical tests used to analyze data, including the t-test, ANOVA, and ANCOVA. It describes the assumptions, test statistics, and SAS code for each test. The t-test is used to compare two population means or determine if two sets of data are significantly different. ANOVA examines differences among group means and can be one-way or two-way. ANCOVA combines aspects of ANOVA and regression by including categorical and continuous predictors to examine the influence of independent variables on a dependent variable while controlling for a covariate.
Analysis of Variance - Meaning and Types
This vides briefed the meaning, Introduction, Definition, Application, Classification and Types of ANOVA.
Video link https://youtu.be/YLHGYVMH2T4
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Wilcoxon signed rank test
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.
Unpaired t Test Family
Comparing the unpaired t test, small sample formula with the one with large sample formula and the Welch's test. The latter is an unpaired t test for samples of unequal sizes with unequal variances.
F test and ANOVA
A brief description of F Test and ANOVA for Msc Life Science students. I have taken the example slides from youtube where an excellent explanation is available.
Here is the link : https://www.youtube.com/watch?v=-yQb_ZJnFXw
Contingency table
Contingency tables, or crosstabs, summarize the relationship between categorical variables. They display counts of observations cross-classified by discrete predictors and response variables. Contingency tables are used to assess if factors are related, describe data frequencies and proportions, and test relationships between factors using chi-square tests. They show counts in each cell, and row, column, and total percentages to understand associations between independent variables like exposures, dependent outcome variables, and potential confounders.
Comparing mean IQ of students using one way anova
This document discusses using a one-way ANOVA test to analyze differences in mean IQ scores between students from different disciplines (statistics, maths, and chemistry). It outlines the assumptions of one-way ANOVA, describes the variables used, and provides an example comparing mean IQ scores between the three disciplines. The results of running a one-way ANOVA and post-hoc test in SPSS on sample IQ data are presented. The post-hoc test finds a significant difference in mean IQ scores only between maths and chemistry students.
Anova, ancova, manova thiyagu
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
Parametric vs non parametric test
Parametric and non-parametric tests differ in their assumptions about the population from which data is drawn. Parametric tests assume the population is normally distributed and variables are measured on an interval scale, while non-parametric tests make fewer assumptions. Examples of parametric tests include t-tests and ANOVA, while non-parametric examples include chi-square, Mann-Whitney U, and Wilcoxon signed-rank. Parametric tests are more powerful but rely on stronger assumptions, while non-parametric tests are more flexible but less powerful. Researchers must consider the characteristics of their data and questions being asked to determine the appropriate test.
Student's T Test
The document discusses Student's t-test, which is useful for three situations: when sample sizes are small, when the population standard deviation is unknown, and when comparing two samples. It describes how Student's t-test addresses the problems with small sample sizes that violate the Central Limit Theorem. It also explains how the t-test can be used to estimate an unknown population standard deviation from the sample standard deviation. Finally, it provides examples of using a t-test to compare the means of two samples and of using a paired t-test to compare salaries between two cities for the same jobs.
Anova test
The document discusses ANOVA (analysis of variance) tests, which compare means and variances between multiple populations or samples. ANOVA was developed by R.A. Fisher and is used to test hypotheses about differences between population means. There are one-way and two-way ANOVA tests, and they assume populations are normally distributed with equal variances. The tests examine variation within and between samples to calculate an F-statistic for comparison.
Statistical tests
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.
Contingency tables
1) The document discusses contingency tables and goodness-of-fit tests. It provides objectives, definitions, notation, requirements, and examples for conducting chi-square tests of independence and homogeneity using contingency tables.
2) One example tests whether the success of a treatment for a foot injury depends on the type of treatment administered. The chi-square test rejects independence between treatment and outcome, indicating the claim that they are independent is false.
3) A second example is given where data from 3 hospitals is to be tested to see if the number of patient infections depends on the hospital. The steps for conducting this chi-square test of homogeneity are outlined.
The wilcoxon matched pairs signed-ranks test
The Wilcoxon matched-pairs signed test analyzes ordinal data from repeated measures experiments to identify differences between two dependent samples. It compares pre-test and post-test ratings of social competence for participants in a therapy group for social phobias. Though one participant showed no change, the test statistic for the remaining participants was greater than the critical value, so the null hypothesis of no difference between pre-test and post-test scores could not be rejected. The two months of therapy did not successfully reduce social phobia.
Tugasan kumpulan anova
ANOVA is a statistical technique used to compare the means of three or more groups. It can test if population means are equal or if some are different. The document outlines the steps in ANOVA including describing data, stating hypotheses, calculating test statistics, and making conclusions. It also discusses one-way and two-way ANOVA designs, comparing means between multiple groups while controlling for Type I error, and the calculations involved including sums of squares, degrees of freedom, and F-ratios.
Parametric Statistical tests
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
Class24 chi squaretestofindependenceposthoc
This document provides an overview of the chi-square test of independence through 18 slides. It defines independence, demonstrates it, discusses expected frequencies, and outlines the 5 steps for conducting a chi-square test of independence: 1) checking assumptions, 2) stating hypotheses and significance level, 3) identifying the sampling distribution and test statistic, 4) computing the test statistic, and 5) making a decision and interpreting results. It also covers examining standardized residuals to identify which cells are contributing most to a significant result.
Analysis of variance
This document provides an overview of analysis of variance (ANOVA) techniques. It discusses one-way and two-way ANOVA, including their assumptions, calculations, and applications. For example, it explains how to set up a two-way ANOVA table and calculate values like sums of squares, degrees of freedom, mean squares, and F values. It also gives an example of using one-way ANOVA to analyze differences in crop yields between four plots of land.
Parametric Test
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
Assumptions about parametric and non parametric tests
Parametric tests make specific assumptions about the population parameters, such as the data being normally distributed. Non-parametric tests do not assume a particular distribution and instead focus on differences in medians or proportions. Common assumptions for parametric tests include normality of data, homogeneity of variances between groups, a linear relationship between variables, and independence of observations. Specific parametric tests like the t-test and ANOVA also assume random sampling from a normally distributed population and additivity of treatment effects.
T-Test
Topic: T-Test
Student Name: Rabia
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Analysis of Variance
This document discusses one-way analysis of variance (ANOVA), which allows for comparison of three or more means. It explains that ANOVA is needed when comparing more than two means, as multiple t-tests could lead to complications. The document also outlines the assumptions that must be met to use one-way ANOVA and describes the outputs generated, including descriptive statistics, an ANOVA table showing if group means are statistically significantly different, and a multiple comparisons table indicating which specific groups differ. An example is provided of a manager using one-way ANOVA to compare the productivity of employees who took beginner, intermediate, or advanced spreadsheet training courses.
Parametric tests
Parametric tests are used to analyze normally distributed numerical data and include Students' t-test and analysis of variance (ANOVA). The Students' t-test compares the means of two groups to determine if they are statistically different. ANOVA compares the means of two or more groups and is more efficient than multiple t-tests. It can be used for one-way, multifactor, or repeated measures designs. Both tests make assumptions about the data distribution and variance between groups.
Applied statistics part 3
Applied statistics part 3Mohammad Hadi Farjoo MD, PhD, Shahid behehsti University of Medical Sciences
This document provides an overview of various statistical tests for comparing variables, including t-tests, ANOVA, MANOVA, ANCOVA, and MANCOVA. It defines each test and provides examples of their proper usage. T-tests are used to compare two groups on a continuous variable, including paired and unpaired, parametric and non-parametric versions. ANOVA and MANOVA are used to compare three or more groups and two or more dependent variables, respectively. ANCOVA and MANCOVA control for covariates/confounding variables in one-way and two-way designs with single or multiple dependent variables. Examples and best practices are given for selecting and conducting each type of test.Parametric Test by Vikramjit Singh
This Slides presents different types of Parametric Test- like
T-test,
Parametric Test,
Assumption of Parametric Test,
Paired T Test,
One Sample T Test,
ANOVA,
ANCOVA,
Regression,
Two Way ANOVA,
Repeated Measure ANOVA,
Multiple Regression
Application of-different-statistical-tests-in-fisheries-science
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.
Lec1_Methods-for-Dummies-T-tests-anovas-and-regression.pptx
This document provides an overview of common statistical tests used to analyze quantitative data, including t-tests, ANOVAs, and regression. It defines the assumptions and applications of t-tests (independent samples t-test, paired t-test, one-sample t-test) and ANOVA (one-way, factorial). Linear and multiple regression are introduced as ways to model relationships between continuous variables and test predictions. Examples of research questions and outputs are provided.
More Related Content
What's hot
Contingency table
Contingency tables, or crosstabs, summarize the relationship between categorical variables. They display counts of observations cross-classified by discrete predictors and response variables. Contingency tables are used to assess if factors are related, describe data frequencies and proportions, and test relationships between factors using chi-square tests. They show counts in each cell, and row, column, and total percentages to understand associations between independent variables like exposures, dependent outcome variables, and potential confounders.
Comparing mean IQ of students using one way anova
This document discusses using a one-way ANOVA test to analyze differences in mean IQ scores between students from different disciplines (statistics, maths, and chemistry). It outlines the assumptions of one-way ANOVA, describes the variables used, and provides an example comparing mean IQ scores between the three disciplines. The results of running a one-way ANOVA and post-hoc test in SPSS on sample IQ data are presented. The post-hoc test finds a significant difference in mean IQ scores only between maths and chemistry students.
Anova, ancova, manova thiyagu
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
Parametric vs non parametric test
Parametric and non-parametric tests differ in their assumptions about the population from which data is drawn. Parametric tests assume the population is normally distributed and variables are measured on an interval scale, while non-parametric tests make fewer assumptions. Examples of parametric tests include t-tests and ANOVA, while non-parametric examples include chi-square, Mann-Whitney U, and Wilcoxon signed-rank. Parametric tests are more powerful but rely on stronger assumptions, while non-parametric tests are more flexible but less powerful. Researchers must consider the characteristics of their data and questions being asked to determine the appropriate test.
Student's T Test
The document discusses Student's t-test, which is useful for three situations: when sample sizes are small, when the population standard deviation is unknown, and when comparing two samples. It describes how Student's t-test addresses the problems with small sample sizes that violate the Central Limit Theorem. It also explains how the t-test can be used to estimate an unknown population standard deviation from the sample standard deviation. Finally, it provides examples of using a t-test to compare the means of two samples and of using a paired t-test to compare salaries between two cities for the same jobs.
Anova test
The document discusses ANOVA (analysis of variance) tests, which compare means and variances between multiple populations or samples. ANOVA was developed by R.A. Fisher and is used to test hypotheses about differences between population means. There are one-way and two-way ANOVA tests, and they assume populations are normally distributed with equal variances. The tests examine variation within and between samples to calculate an F-statistic for comparison.
Statistical tests
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.
Contingency tables
1) The document discusses contingency tables and goodness-of-fit tests. It provides objectives, definitions, notation, requirements, and examples for conducting chi-square tests of independence and homogeneity using contingency tables.
2) One example tests whether the success of a treatment for a foot injury depends on the type of treatment administered. The chi-square test rejects independence between treatment and outcome, indicating the claim that they are independent is false.
3) A second example is given where data from 3 hospitals is to be tested to see if the number of patient infections depends on the hospital. The steps for conducting this chi-square test of homogeneity are outlined.
The wilcoxon matched pairs signed-ranks test
The Wilcoxon matched-pairs signed test analyzes ordinal data from repeated measures experiments to identify differences between two dependent samples. It compares pre-test and post-test ratings of social competence for participants in a therapy group for social phobias. Though one participant showed no change, the test statistic for the remaining participants was greater than the critical value, so the null hypothesis of no difference between pre-test and post-test scores could not be rejected. The two months of therapy did not successfully reduce social phobia.
Tugasan kumpulan anova
ANOVA is a statistical technique used to compare the means of three or more groups. It can test if population means are equal or if some are different. The document outlines the steps in ANOVA including describing data, stating hypotheses, calculating test statistics, and making conclusions. It also discusses one-way and two-way ANOVA designs, comparing means between multiple groups while controlling for Type I error, and the calculations involved including sums of squares, degrees of freedom, and F-ratios.
Parametric Statistical tests
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
Class24 chi squaretestofindependenceposthoc
This document provides an overview of the chi-square test of independence through 18 slides. It defines independence, demonstrates it, discusses expected frequencies, and outlines the 5 steps for conducting a chi-square test of independence: 1) checking assumptions, 2) stating hypotheses and significance level, 3) identifying the sampling distribution and test statistic, 4) computing the test statistic, and 5) making a decision and interpreting results. It also covers examining standardized residuals to identify which cells are contributing most to a significant result.
Analysis of variance
This document provides an overview of analysis of variance (ANOVA) techniques. It discusses one-way and two-way ANOVA, including their assumptions, calculations, and applications. For example, it explains how to set up a two-way ANOVA table and calculate values like sums of squares, degrees of freedom, mean squares, and F values. It also gives an example of using one-way ANOVA to analyze differences in crop yields between four plots of land.
Parametric Test
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
Assumptions about parametric and non parametric tests
Parametric tests make specific assumptions about the population parameters, such as the data being normally distributed. Non-parametric tests do not assume a particular distribution and instead focus on differences in medians or proportions. Common assumptions for parametric tests include normality of data, homogeneity of variances between groups, a linear relationship between variables, and independence of observations. Specific parametric tests like the t-test and ANOVA also assume random sampling from a normally distributed population and additivity of treatment effects.
T-Test
Topic: T-Test
Student Name: Rabia
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Analysis of Variance
This document discusses one-way analysis of variance (ANOVA), which allows for comparison of three or more means. It explains that ANOVA is needed when comparing more than two means, as multiple t-tests could lead to complications. The document also outlines the assumptions that must be met to use one-way ANOVA and describes the outputs generated, including descriptive statistics, an ANOVA table showing if group means are statistically significantly different, and a multiple comparisons table indicating which specific groups differ. An example is provided of a manager using one-way ANOVA to compare the productivity of employees who took beginner, intermediate, or advanced spreadsheet training courses.
Parametric tests
Parametric tests are used to analyze normally distributed numerical data and include Students' t-test and analysis of variance (ANOVA). The Students' t-test compares the means of two groups to determine if they are statistically different. ANOVA compares the means of two or more groups and is more efficient than multiple t-tests. It can be used for one-way, multifactor, or repeated measures designs. Both tests make assumptions about the data distribution and variance between groups.
What's hot (18)
Assumptions about parametric and non parametric tests
Assumptions about parametric and non parametric tests
Similar to Criterion to select the test
Applied statistics part 3
Applied statistics part 3Mohammad Hadi Farjoo MD, PhD, Shahid behehsti University of Medical Sciences
This document provides an overview of various statistical tests for comparing variables, including t-tests, ANOVA, MANOVA, ANCOVA, and MANCOVA. It defines each test and provides examples of their proper usage. T-tests are used to compare two groups on a continuous variable, including paired and unpaired, parametric and non-parametric versions. ANOVA and MANOVA are used to compare three or more groups and two or more dependent variables, respectively. ANCOVA and MANCOVA control for covariates/confounding variables in one-way and two-way designs with single or multiple dependent variables. Examples and best practices are given for selecting and conducting each type of test.Parametric Test by Vikramjit Singh
This Slides presents different types of Parametric Test- like
T-test,
Parametric Test,
Assumption of Parametric Test,
Paired T Test,
One Sample T Test,
ANOVA,
ANCOVA,
Regression,
Two Way ANOVA,
Repeated Measure ANOVA,
Multiple Regression
Application of-different-statistical-tests-in-fisheries-science
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.
Lec1_Methods-for-Dummies-T-tests-anovas-and-regression.pptx
This document provides an overview of common statistical tests used to analyze quantitative data, including t-tests, ANOVAs, and regression. It defines the assumptions and applications of t-tests (independent samples t-test, paired t-test, one-sample t-test) and ANOVA (one-way, factorial). Linear and multiple regression are introduced as ways to model relationships between continuous variables and test predictions. Examples of research questions and outputs are provided.
Workshop on Data Analysis and Result Interpretation in Social Science Researc...
Workshop on Data Analysis and Result Interpretation in Social Science Researc...Academy for Higher Education and Social Science Research
This document discusses parametric and nonparametric statistical tests. Parametric tests like the t-test and ANOVA assume a normal distribution of data and compare population means. Nonparametric tests do not assume a normal distribution and can be used when sample sizes are small or distributions are unknown. Specific parametric tests covered include the t-test for comparing two groups, one-way ANOVA for comparing three or more groups on one factor, and two-way ANOVA for examining two factors. Examples of how and when to use these various tests are provided.Hypothesis
In this document, I have tried to illustrate most of the hypothesis testing like 1 sample,2 samples, etc, which I have covered to analyze the machine learning algorithms. I have focused on Independent statistical testing.
Now the question is why we use statistical testing? the answer is that we use statistical testing for significance analysis of our results, which I am going to deliver
1 ANOVA.ppt
The document discusses different types of t-tests and one-way ANOVA for comparing means of continuous outcome data. It describes one sample t-test, paired t-test, two independent samples t-test, and one-way ANOVA. For one-way ANOVA, it outlines the assumptions, definitions, notations, partitioning of total sum of squares, and provides examples to illustrate these statistical tests for comparing several means.
Ttest
The document provides an overview of student's t-test, a statistical technique used to determine if there are significant differences between the means of two groups. It discusses the different types of t-tests, including one-sample, independent samples, and paired/correlated samples t-tests. Key steps for conducting each type of t-test are outlined, including calculating t-values, degrees of freedom, and comparing results to significance levels. The t-test is a commonly used hypothesis testing tool that allows testing assumptions about population means.
Friedman Test- A Presentation
1. The Friedman test should be used to analyze this data because:
- There is one independent variable (hours of food deprivation) with three related conditions (0, 24, 72 hours)
- The dependent variable (food consumption) is ordinal
- The same rats are measured under each of the three conditions
2. To analyze this data, you would use the Friedman test.
3. To determine how strong the relationship is between the three experimental conditions, you would calculate Kendall's W from the results of the Friedman test. Kendall's W ranges from 0-1, with higher values indicating stronger agreement between conditions.
Testing of hypothesis.pptx
This document discusses different types of t-tests used for hypothesis testing, including the independent samples t-test, paired samples t-test, and one sample t-test. It provides examples and steps for performing each test in SPSS. The independent samples t-test is used to compare the means of two independent groups, the paired samples t-test compares means from the same subjects under two conditions, and the one sample t-test tests if a sample mean differs from a hypothesized population mean. Assumptions for each test include normality, independence of observations, and no outliers.
Common Statistical Tests.pdf
Common statistical tests can be used for correlation, comparison of means, regression, or non-parametric analysis. Correlational tests measure the strength of association between variables, comparison of means tests examine differences between group averages, regression assesses how one variable predicts another, and non-parametric tests are used when data does not meet assumptions for standard tests. Specific tests mentioned include the Pearson, Spearman, and chi-square for correlation, paired t-test, independent t-test and ANOVA for means, simple and multiple regression, and the Wilcoxon rank-sum, Wilcoxon sign-rank, and sign tests for non-parametric analysis.
mean comparison.pptx
The document discusses statistical methods for comparing means between groups, including t-tests and analysis of variance (ANOVA). It provides information on different types of t-tests (one sample, independent samples, and paired samples t-tests), assumptions of t-tests, and how to perform t-tests in SPSS. It also covers one-way ANOVA, including its assumptions, components of variation, properties of the F-test, and how to run a one-way ANOVA in SPSS. Examples are provided for each statistical test.
mean comparison.pptx
The document provides information on statistical techniques for comparing means between groups, including t-tests, analysis of variance (ANOVA), and their assumptions and applications. T-tests are used to compare two groups, while ANOVA allows comparison of three or more groups and controls for increased Type I error rates. Steps for conducting t-tests, ANOVA, and post-hoc tests using SPSS are outlined along with examples and interpretations.
Basics of Educational Statistics (T-test)
A t-test is a statistical test used to compare the means of two groups and determine if there is a significant difference between them. It can be used for hypothesis testing to see if a treatment has an effect. There are assumptions that the data is independent, normally distributed, and has similar variances within each group. Different types of t-tests exist depending on the type of data, such as whether the groups are related or independent samples. The t-distribution table provides probabilities for assessing the significance of t-test results.
Student's T-Test
The document provides an overview of the student's t-test, a statistical hypothesis test used to determine if two sets of data are significantly different from each other. It discusses the different types of t-tests, their main uses which include comparing sample means to hypothesized values or between two groups, assumptions of the t-test, and how it relates to the z-test and normal distribution. Examples of one sample, paired, and independent sample t-tests are also provided.
SAS Ttest Procedure.pptx
This document provides information about performing t-tests using the SAS TTEST procedure. It describes the basic syntax of the procedure and how to conduct one-sample, two-sample, and paired t-tests. It also discusses using the procedure to compute power and sample size calculations for comparing means using the SAS POWER procedure.
Summary of statistical tools used in spss
This document provides an overview of statistical analysis tools that can be used to analyze data, including when to use frequencies, descriptive statistics, chi-square tests, Fisher's exact test, one sample t-tests, paired sample t-tests, and independent sample t-tests. It gives examples of different variable types and scales, and outlines the assumptions and requirements for each statistical test.
Non parametric
Non-parametric tests make fewer assumptions about the distribution of data compared to parametric tests. They can be used as alternatives when the assumptions of parametric tests like the t-test, ANOVA, and other tests are violated. Some common non-parametric tests include the Mann-Whitney U test and Wilcoxon signed-rank test for comparing two independent and paired samples respectively, and the Kruskal-Wallis H test for comparing more than two independent samples. These tests convert original data to ranks before performing the statistical analysis to avoid distributional assumptions.
Experimental method
Experimental method involves carefully controlling conditions and manipulating variables to test hypotheses. Key aspects include:
- Controlled conditions allow the investigator to isolate the effects of variables being tested from other potential influences.
- Manipulation involves deliberately introducing, removing, or changing variables to see how a subject or group is affected.
- Variables are categorized as independent variables manipulated by the researcher or dependent variables that are observed to change in response.
- Experiments aim to compare the effects of different treatments or a treatment against no treatment through replication under controlled conditions.
Biostatistics ii
This document discusses various statistical tests that can be used to analyze data, including parametric and nonparametric tests. It provides examples of tests for comparing means from one group, two independent groups, two related groups, and multiple groups. These include t-tests, ANOVA, correlation, regression, chi-square tests, and nonparametric alternatives like the Wilcoxon and Kruskal-Wallis tests. It also discusses assumptions of parametric versus nonparametric tests and selecting the appropriate test based on the scale of the data.
Similar to Criterion to select the test (20)
Application of-different-statistical-tests-in-fisheries-science
Application of-different-statistical-tests-in-fisheries-science
Lec1_Methods-for-Dummies-T-tests-anovas-and-regression.pptx
Lec1_Methods-for-Dummies-T-tests-anovas-and-regression.pptx
Workshop on Data Analysis and Result Interpretation in Social Science Researc...
Workshop on Data Analysis and Result Interpretation in Social Science Researc...
More from Jindal Global University, Sonipat Haryana 131001
Excel financial tools
The document discusses financial functions in Excel used to analyze investment projects, including NPV, IRR, XNPV, and XIRR. NPV compares the present value of cash inflows to outflows to determine if a project is profitable. IRR is the discount rate that makes NPV equal to zero. XNPV and XIRR are used for uneven cash flows. Other functions discussed are PMT, PV, FV, NPER, and RATE.
Fun with maths
The document contains several examples of mathematical patterns and magic tricks involving numbers. Some examples show that multiplying a number by 8 or 9 and adding a corresponding digit produces a predictable outcome. Other examples demonstrate that squaring a number produces the sum of consecutive integers up to that number. The document aims to showcase interesting mathematical properties and patterns in a fun way.
Writing tips
This document provides guidance on various aspects of academic writing structure and content, including:
- Choosing a topic that interests the reader and fits assignment guidelines.
- Components of an introduction such as the purpose, background, and overview of what will be discussed.
- Conducting an initial literature review to provide context and justification for the research.
- Describing materials, procedures, data analysis, and limitations in the methods section.
- Additional tips for writing introductions and avoiding overgeneralizations.
- The importance of proofreading the final paper to fix errors before submission.
Conflict management
This document discusses conflict management and resolution. It defines conflict as a struggle between two interdependent parties with incompatible goals. Conflict can be functional and increase creativity or dysfunctional and damage relationships. The sources of conflict include aggressive behavior, limited resources, and cultural differences. Conflict management strategies include avoiding, competing, accommodating, compromising, and collaborating. Collaboration is the most effective approach as it results in a "win-win" where all parties' interests are satisfied. Active listening, understanding other perspectives, finding areas of agreement, and negotiating are keys skills for successful conflict resolution.
The normal distribution
Group B has a larger standard deviation, so the tails are "fatter" and it is more likely to find extreme scores in Group B compared to Group A.
Citation guide
This document provides an overview and guidelines for five citation styles: Modern Language Association (MLA), American Psychological Association (APA), Chicago Manual of Style (CMS), and Council of Science Editors (CSE). It describes what each style is, when each is used, and how citations and reference lists are formatted according to each style. Key differences between the styles are highlighted.
Structure of synopsis
This document outlines the typical structure of a research synopsis, including: [1] Introduction with background and context; [2] Review of literature to identify prior work and research gaps; [3] Statement of the research problem and objectives; [4] Description of the research methodology; [5] Outline of proposed thesis structure; [6] Timeline for completing the research; and [7] References in a standard citation style. The synopsis provides an overview of the research topic and plan to demonstrate the significance and feasibility of the proposed work.
Mathematics of human brain & human language
Lect. by Prof. Madan M. Gupta,
Intelligent Systems Research Laboratory College of Engineering, University of Saskatchewan Saskatoon, SK., Canada, S7N 5A9 1-(306) 966-5451
More from Jindal Global University, Sonipat Haryana 131001 (8)
Recently uploaded
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.
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...Nguyen Thanh Tu Collection
https://app.box.com/s/qhtvq32h4ybf9t49ku85x0n3xl4jhr15Temple of Asclepius in Thrace. Excavation results
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Stack Memory Organization of 8086 Microprocessor
The stack memory organization of 8086 microprocessor.
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.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
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,
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.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-mule-event-processing-models/
Agenda
● What is event processing in MuleSoft?
● Types of event processing models in Mule 4
● Distinction between the reactive, parallel, blocking & non-blocking processing
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shivani Yasaswi - https://www.linkedin.com/in/shivaniyasaswi/
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
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.)
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Geography as discipline
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.
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh
Recently uploaded (20)
Juneteenth Freedom Day 2024 David Douglas School District
Juneteenth Freedom Day 2024 David Douglas School District
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
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
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Criterion to select the test
- 1. CRITERION TO SELECT THE TEST Experimental Design Selection of Test Using one variable to compare two samples where the data is unmatched t or Z test for unmatched data Using one variable to compare two samples where the data is matched t or Z test for matched data Using one variable to compare two or more samples and to test general and specific hypothesis One-way parametric ANOVA and Tukey's Test Using two variable. Each variable has at least two categories or classes and all categories from one variable are combined with all categories from the second variable. In this case, to test general and specific hypothesis Two-way parametric ANOVA and Tukey's Test Using two variable. Each variable has at least two categories.one variable is randomized or nested with respect to the second variable. In this case, to test hypothesis. Two-way nested ANOVA Using three treatment variables. Each variable has at least two categories and all categories from each variable are combined with all other categories from the other variables. IN this case, to test general and specific hypothesis. Three- way parametric ANOVA