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Hypothesis Testing.pptx

This document discusses parametric and non-parametric statistical tests used for hypothesis testing. It defines key concepts like the null hypothesis, alternate hypothesis, and test statistics. It explains parametric tests like the t-test, z-test, F-test, and ANOVA that assume a known population distribution. It also covers non-parametric tests like the chi-square test, Mann-Whitney U test, Wilcoxon signed rank test, sign test, and Kruskal-Wallis test that do not assume a known distribution. Each test is briefly described in terms of its assumptions, test statistic, and decision criteria for rejecting the null hypothesis.

UNIT 5.pptx

This document provides information about non-parametric tests. It begins by explaining that non-parametric tests do not assume a specific distribution or make assumptions about the population. It then discusses tests for normality like the Kolmogorov-Smirnov test and Shapiro-Wilk test. Commonly used non-parametric tests like Spearman's rank correlation, Mann-Whitney U test, and Kruskal-Wallis H test are explained. The chi-square test and assumptions are also covered in detail. Advantages of non-parametric tests include fewer assumptions and applicability to small sample sizes. A disadvantage is they are less powerful than parametric tests.

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.

Descriptive Analysis.pptx

Descriptive analysis and descriptive analytics involve examining and summarizing data using techniques like charts, graphs, and narratives to identify patterns. Common visualization tools include pie charts, bar charts, histograms, and more. Tableau, Excel, and Datawrapper are popular tools that allow users to import data and generate various visualizations. Queries allow users to sort, filter, and extract specific information from large datasets using clauses like ORDER BY and WHERE. Hypothesis testing uses the null and alternative hypotheses to determine if experimental results are statistically significant or due to chance. Analysis of variance (ANOVA) specifically tests hypotheses by comparing means across independent groups.

Parametric tests seminar

This document provides an overview of parametric statistical tests used for analyzing data. It defines descriptive statistics such as measures of central tendency and dispersion. Parametric tests covered include the z-test, t-test, analysis of variance (ANOVA), and correlation. The t-test is used for small samples and compares means, while ANOVA compares multiple group means. Type I and II errors in hypothesis testing are also discussed. The document provides examples of when to use different parametric tests depending on the type of data and number of groups being compared.

chi_square test.pptx

This document provides an overview of the chi-square test and student's t-test. It defines key terms like parametric vs. non-parametric tests and explains the assumptions and applications of each test. For the chi-square test, it outlines the steps to calculate chi-square values and determine whether to accept or reject the null hypothesis based on comparing the calculated and tabular values. For the t-test, it describes the assumptions and types of t-tests, and notes some common uses like comparing group means and testing regression coefficients. Examples are provided to demonstrate calculating chi-square values from observed and expected data.

BBA 020

This document discusses hypothesis testing, including:
- A hypothesis test is a method for making decisions using data to test an unproven statement about a factor or phenomenon.
- The null hypothesis states there is no difference between what is observed and what is expected. The alternative hypothesis specifies an alternative statement.
- Steps in hypothesis testing include formulating the hypotheses, selecting a statistical test, collecting data, determining critical values and probabilities, and deciding whether to reject or fail to reject the null hypothesis.
- Parametric tests assume a known distribution while nonparametric tests make no assumptions. Common tests mentioned include t-tests, z-tests, F-tests, chi-square tests, and rank correlation tests.

Inferential statistics_AAF 500L 2021.ppt

Inferential statistics

Hypothesis Testing.pptx

This document discusses parametric and non-parametric statistical tests used for hypothesis testing. It defines key concepts like the null hypothesis, alternate hypothesis, and test statistics. It explains parametric tests like the t-test, z-test, F-test, and ANOVA that assume a known population distribution. It also covers non-parametric tests like the chi-square test, Mann-Whitney U test, Wilcoxon signed rank test, sign test, and Kruskal-Wallis test that do not assume a known distribution. Each test is briefly described in terms of its assumptions, test statistic, and decision criteria for rejecting the null hypothesis.

UNIT 5.pptx

This document provides information about non-parametric tests. It begins by explaining that non-parametric tests do not assume a specific distribution or make assumptions about the population. It then discusses tests for normality like the Kolmogorov-Smirnov test and Shapiro-Wilk test. Commonly used non-parametric tests like Spearman's rank correlation, Mann-Whitney U test, and Kruskal-Wallis H test are explained. The chi-square test and assumptions are also covered in detail. Advantages of non-parametric tests include fewer assumptions and applicability to small sample sizes. A disadvantage is they are less powerful than parametric tests.

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.

Descriptive Analysis.pptx

Descriptive analysis and descriptive analytics involve examining and summarizing data using techniques like charts, graphs, and narratives to identify patterns. Common visualization tools include pie charts, bar charts, histograms, and more. Tableau, Excel, and Datawrapper are popular tools that allow users to import data and generate various visualizations. Queries allow users to sort, filter, and extract specific information from large datasets using clauses like ORDER BY and WHERE. Hypothesis testing uses the null and alternative hypotheses to determine if experimental results are statistically significant or due to chance. Analysis of variance (ANOVA) specifically tests hypotheses by comparing means across independent groups.

Parametric tests seminar

This document provides an overview of parametric statistical tests used for analyzing data. It defines descriptive statistics such as measures of central tendency and dispersion. Parametric tests covered include the z-test, t-test, analysis of variance (ANOVA), and correlation. The t-test is used for small samples and compares means, while ANOVA compares multiple group means. Type I and II errors in hypothesis testing are also discussed. The document provides examples of when to use different parametric tests depending on the type of data and number of groups being compared.

chi_square test.pptx

This document provides an overview of the chi-square test and student's t-test. It defines key terms like parametric vs. non-parametric tests and explains the assumptions and applications of each test. For the chi-square test, it outlines the steps to calculate chi-square values and determine whether to accept or reject the null hypothesis based on comparing the calculated and tabular values. For the t-test, it describes the assumptions and types of t-tests, and notes some common uses like comparing group means and testing regression coefficients. Examples are provided to demonstrate calculating chi-square values from observed and expected data.

BBA 020

This document discusses hypothesis testing, including:
- A hypothesis test is a method for making decisions using data to test an unproven statement about a factor or phenomenon.
- The null hypothesis states there is no difference between what is observed and what is expected. The alternative hypothesis specifies an alternative statement.
- Steps in hypothesis testing include formulating the hypotheses, selecting a statistical test, collecting data, determining critical values and probabilities, and deciding whether to reject or fail to reject the null hypothesis.
- Parametric tests assume a known distribution while nonparametric tests make no assumptions. Common tests mentioned include t-tests, z-tests, F-tests, chi-square tests, and rank correlation tests.

Inferential statistics_AAF 500L 2021.ppt

Inferential statistics

Statistical analysis

The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.

Chi squared test

The document provides information about the Chi-square test, including:
- It is a non-parametric test used to evaluate categorical data using contingency tables. The test statistic follows a Chi-square distribution.
- It can test for independence between variables and goodness of fit to theoretical distributions.
- Key steps involve calculating expected frequencies, taking the difference between observed and expected, and summing the results.
- The test interprets higher Chi-square values as less likelihood the results are due to chance. Modifications like Yates' correction and Fisher's exact test address limitations for small sample sizes.

Statistical analysis.pptx

1. Statistical analysis involves collecting, organizing, analyzing data, and drawing inferences about populations based on samples. It includes both descriptive and inferential statistics.
2. The document defines key terms used in statistical analysis like population, sample, statistical analysis, and discusses various statistical measures like mean, median, mode, interquartile range, and standard deviation.
3. The purposes of statistical analysis are outlined as measuring relationships, making predictions, testing hypotheses, and summarizing results. Both parametric and non-parametric statistical analyses are discussed.

7- Quantitative Research- Part 3.pdf

This document provides an overview of quantitative data analysis techniques for research. It describes the different levels of measurement (nominal, ordinal, interval, ratio) and key descriptive statistics like measures of central tendency, frequency distributions, and measures of dispersion. It also discusses inferential statistics and common tests like t-tests, ANOVA, correlation, and chi-squared tests. The purpose of statistical analysis is to summarize and make inferences about data to either support or reject research hypotheses.

Statistics

This document provides an overview of common statistical tests used in dentistry research. It first describes descriptive statistics like measures of central tendency, dispersion, position, and outliers. It then discusses inferential statistics including parametric tests like t-tests and ANOVA that assume normal distributions, and non-parametric tests that make fewer assumptions. Specific parametric tests covered are the independent and paired t-tests and ANOVA. Non-parametric tests discussed include the chi-square, Wilcoxon, Mann-Whitney U, and Kruskal-Wallis tests. The document also briefly explains correlation/regression and measures of effect size like relative risk and odds ratios.

Statistic and orthodontic by almuzian

This document discusses different types of data and statistical tests used in orthodontics. It outlines categorical (qualitative) and numerical (quantitative) data, including nominal, ordinal, discrete, and continuous variables. Appropriate statistical tests are described for each data type, such as chi square tests for categorical data and t-tests or ANOVA for numerical data. Key concepts in data summarization are also covered, including measures of central tendency, variability, normal distribution, correlation, and hypothesis testing. The importance of selecting the right analysis method based on data type is emphasized.

tests of significance

This document discusses various statistical tests used to analyze data, including tests of significance, parametric vs. non-parametric tests, and limitations. It provides background on key tests such as:
1) Student's t-test, developed by Gosset, which is used to compare two means from small samples with unknown variances.
2) ANOVA (analysis of variance), developed by Fisher, which compares variance between and within groups to test for significant differences between means of more than two groups.
3) Correlation analysis, which measures the strength and direction of association between two continuous variables using Pearson's correlation coefficient.
4) Chi-square test, which analyzes relationships between categorical variables to

Basic stat analysis using excel

This ppt includes basic concepts about data types, levels of measurements. It also explains which descriptive measure, graph and tests should be used for different types of data. A brief of Pivot tables and charts is also included.

Test of significance in Statistics

This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.

Significance Tests

This document provides an overview of common statistical significance tests including Pearson's chi-square test, t-tests, and analysis of variance (ANOVA). It defines key concepts like significance, level of significance (p-value), and conditions for applying each test. Chi-square can test for goodness of fit, homogeneity, and independence. T-tests compare means between two groups. ANOVA determines if multiple sample means are equal and has assumptions of independence, normality, and equal variances. One-way ANOVA considers one factor between subjects.

Non Parametric Test by Vikramjit Singh

Various types of Non- Parametric Tests have been explained in this slides like Chi-Square, Sign Test, Run Test, Mc. Nemar Test test etc.

Chi square mahmoud

The document discusses Chi-Square tests, which are used when assumptions of normality are violated. It provides requirements for Chi-Square tests, including that variables must be independent and samples sufficiently large. The key steps are outlined: determine appropriate test, establish significance level, formulate hypotheses, calculate test statistic using frequencies, determine degrees of freedom, and compare to critical value. An example compares party membership to opinions on gun control to demonstrate a Chi-Square test of independence.

Univariate Analysis

This document provides an overview of univariate analysis. It defines key terms like variables, scales of measurement, and types of univariate analysis. It describes descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). It also discusses inferential univariate analysis and appropriate statistical tests for different variable types and research questions, including z-tests, t-tests, and chi-square tests. Examples are provided to illustrate calculating and interpreting these statistics.

Stat topics

This document provides an overview of different types of statistical tests used for data analysis and interpretation. It discusses scales of measurement, parametric vs nonparametric tests, formulating hypotheses, types of statistical errors, establishing decision rules, and choosing the appropriate statistical test based on the number and types of variables. Key statistical tests covered include t-tests, ANOVA, chi-square tests, and correlations. Examples are provided to illustrate how to interpret and report the results of these common statistical analyses.

Non parametric test

This document discusses non-parametric tests, which are statistical tests that make fewer assumptions about the population distribution compared to parametric tests. Some key points:
1) Non-parametric tests like the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U-test, and Kruskal-Wallis test are used when the population is not normally distributed or sample sizes are small.
2) They are applied in situations where data is on an ordinal scale rather than a continuous scale, the population is not well defined, or the distribution is unknown.
3) Advantages are that they are easier to compute and make fewer assumptions than parametric tests,

Presentation chi-square test & Anova

The document discusses hypothesis testing using parametric and non-parametric tests. It defines key concepts like the null and alternative hypotheses, type I and type II errors, and p-values. Parametric tests like the t-test, ANOVA, and Pearson's correlation assume the data follows a particular distribution like normal. Non-parametric tests like the Wilcoxon, Mann-Whitney, and chi-square tests make fewer assumptions and can be used when sample sizes are small or the data violates assumptions of parametric tests. Examples are provided of when to use parametric or non-parametric tests depending on the type of data and statistical test being performed.

non parametric test.pptx

This document discusses non-parametric tests and when they should be used. Non-parametric tests make fewer assumptions than parametric tests and can be used when the sample size is small, the population is not normally distributed, or measurements are on an ordinal scale. Common non-parametric tests include the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U test, median test, and Kruskal-Wallis test. These tests do not rely on population parameters and can be used as alternatives to parametric tests like the t-test when parametric assumptions are not met.

Day-2_Presentation for SPSS parametric workshop.pptx

This document provides an overview of parametric statistical tests, including t-tests and analysis of variance (ANOVA). It discusses the concepts of statistical inference, hypothesis testing, null and alternative hypotheses, types of errors, critical regions, p-values, assumptions of t-tests, and procedures for one-sample t-tests, independent and paired t-tests, one-way ANOVA, and repeated measures ANOVA. The document is intended as part of an online workshop on using SPSS for advanced statistical data analysis.

T test

This document provides an overview of a presentation on statistical hypothesis testing using the t-test. It discusses what a t-test is, how to perform a t-test, and provides an example of a t-test comparing spelling test scores of two groups that received different teaching strategies. The document outlines the six steps for conducting statistical hypothesis testing using a t-test: 1) stating the hypotheses, 2) choosing the significance level, 3) determining the critical values, 4) calculating the test statistic, 5) comparing the test statistic to the critical values, and 6) writing a conclusion.

Chi square test social research refer.ppt

This document discusses various statistical tests, including parametric tests that require normally distributed data like t-tests and ANOVA, non-parametric tests that don't require normality like the Mann-Whitney U test, and the chi-square test. It explains that chi-square is used to determine if there is a relationship between two categorical variables by comparing observed and expected frequencies in a contingency table. It provides steps for conducting a chi-square test including stating hypotheses, calculating expected values, determining degrees of freedom, finding the test statistic, and interpreting results. Two examples of applying chi-square to test associations between disease prevalence and other factors are also presented.

melasma(Manguu) poster presentation.pptx

melasma, melasma poster presentation, introduction of melasma, symptoms of melasma, Diagnosis for melasma, causes and risk factors of melasma, drugs used for melasma, mask of pregnancy, chloasma , where did melasma occur, pharmacy, MBBS, medical field, general introduction on melasma ,M. pharmacy .

Aerosols, Propellants and types of propellants , Containers , types of conta...

Aerosols, propellants , types of propellants,
containers ,types of containers, Definition of aerosols, History of aerosols, what is propellent, Types of propellant, liquefied gases propellants, chlorofluoro carbons, hydrocarbons, hydrofluoro alkanes, compressed gasses propellant, blending of propellant, Dalton's law, containers for aerosols, types of containers for aerosols, tin plates steel container, aluminium containers, stainless steel containers, glass containers, uncoated glass container, plastic coated glass container, m. pharm 1 year, pharmaceutics, Novel targeted drug delivery system.

Statistical analysis

The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.

Chi squared test

The document provides information about the Chi-square test, including:
- It is a non-parametric test used to evaluate categorical data using contingency tables. The test statistic follows a Chi-square distribution.
- It can test for independence between variables and goodness of fit to theoretical distributions.
- Key steps involve calculating expected frequencies, taking the difference between observed and expected, and summing the results.
- The test interprets higher Chi-square values as less likelihood the results are due to chance. Modifications like Yates' correction and Fisher's exact test address limitations for small sample sizes.

Statistical analysis.pptx

1. Statistical analysis involves collecting, organizing, analyzing data, and drawing inferences about populations based on samples. It includes both descriptive and inferential statistics.
2. The document defines key terms used in statistical analysis like population, sample, statistical analysis, and discusses various statistical measures like mean, median, mode, interquartile range, and standard deviation.
3. The purposes of statistical analysis are outlined as measuring relationships, making predictions, testing hypotheses, and summarizing results. Both parametric and non-parametric statistical analyses are discussed.

7- Quantitative Research- Part 3.pdf

This document provides an overview of quantitative data analysis techniques for research. It describes the different levels of measurement (nominal, ordinal, interval, ratio) and key descriptive statistics like measures of central tendency, frequency distributions, and measures of dispersion. It also discusses inferential statistics and common tests like t-tests, ANOVA, correlation, and chi-squared tests. The purpose of statistical analysis is to summarize and make inferences about data to either support or reject research hypotheses.

Statistics

This document provides an overview of common statistical tests used in dentistry research. It first describes descriptive statistics like measures of central tendency, dispersion, position, and outliers. It then discusses inferential statistics including parametric tests like t-tests and ANOVA that assume normal distributions, and non-parametric tests that make fewer assumptions. Specific parametric tests covered are the independent and paired t-tests and ANOVA. Non-parametric tests discussed include the chi-square, Wilcoxon, Mann-Whitney U, and Kruskal-Wallis tests. The document also briefly explains correlation/regression and measures of effect size like relative risk and odds ratios.

Statistic and orthodontic by almuzian

This document discusses different types of data and statistical tests used in orthodontics. It outlines categorical (qualitative) and numerical (quantitative) data, including nominal, ordinal, discrete, and continuous variables. Appropriate statistical tests are described for each data type, such as chi square tests for categorical data and t-tests or ANOVA for numerical data. Key concepts in data summarization are also covered, including measures of central tendency, variability, normal distribution, correlation, and hypothesis testing. The importance of selecting the right analysis method based on data type is emphasized.

tests of significance

This document discusses various statistical tests used to analyze data, including tests of significance, parametric vs. non-parametric tests, and limitations. It provides background on key tests such as:
1) Student's t-test, developed by Gosset, which is used to compare two means from small samples with unknown variances.
2) ANOVA (analysis of variance), developed by Fisher, which compares variance between and within groups to test for significant differences between means of more than two groups.
3) Correlation analysis, which measures the strength and direction of association between two continuous variables using Pearson's correlation coefficient.
4) Chi-square test, which analyzes relationships between categorical variables to

Basic stat analysis using excel

This ppt includes basic concepts about data types, levels of measurements. It also explains which descriptive measure, graph and tests should be used for different types of data. A brief of Pivot tables and charts is also included.

Test of significance in Statistics

This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.

Significance Tests

This document provides an overview of common statistical significance tests including Pearson's chi-square test, t-tests, and analysis of variance (ANOVA). It defines key concepts like significance, level of significance (p-value), and conditions for applying each test. Chi-square can test for goodness of fit, homogeneity, and independence. T-tests compare means between two groups. ANOVA determines if multiple sample means are equal and has assumptions of independence, normality, and equal variances. One-way ANOVA considers one factor between subjects.

Non Parametric Test by Vikramjit Singh

Various types of Non- Parametric Tests have been explained in this slides like Chi-Square, Sign Test, Run Test, Mc. Nemar Test test etc.

Chi square mahmoud

The document discusses Chi-Square tests, which are used when assumptions of normality are violated. It provides requirements for Chi-Square tests, including that variables must be independent and samples sufficiently large. The key steps are outlined: determine appropriate test, establish significance level, formulate hypotheses, calculate test statistic using frequencies, determine degrees of freedom, and compare to critical value. An example compares party membership to opinions on gun control to demonstrate a Chi-Square test of independence.

Univariate Analysis

This document provides an overview of univariate analysis. It defines key terms like variables, scales of measurement, and types of univariate analysis. It describes descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). It also discusses inferential univariate analysis and appropriate statistical tests for different variable types and research questions, including z-tests, t-tests, and chi-square tests. Examples are provided to illustrate calculating and interpreting these statistics.

Stat topics

This document provides an overview of different types of statistical tests used for data analysis and interpretation. It discusses scales of measurement, parametric vs nonparametric tests, formulating hypotheses, types of statistical errors, establishing decision rules, and choosing the appropriate statistical test based on the number and types of variables. Key statistical tests covered include t-tests, ANOVA, chi-square tests, and correlations. Examples are provided to illustrate how to interpret and report the results of these common statistical analyses.

Non parametric test

This document discusses non-parametric tests, which are statistical tests that make fewer assumptions about the population distribution compared to parametric tests. Some key points:
1) Non-parametric tests like the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U-test, and Kruskal-Wallis test are used when the population is not normally distributed or sample sizes are small.
2) They are applied in situations where data is on an ordinal scale rather than a continuous scale, the population is not well defined, or the distribution is unknown.
3) Advantages are that they are easier to compute and make fewer assumptions than parametric tests,

Presentation chi-square test & Anova

The document discusses hypothesis testing using parametric and non-parametric tests. It defines key concepts like the null and alternative hypotheses, type I and type II errors, and p-values. Parametric tests like the t-test, ANOVA, and Pearson's correlation assume the data follows a particular distribution like normal. Non-parametric tests like the Wilcoxon, Mann-Whitney, and chi-square tests make fewer assumptions and can be used when sample sizes are small or the data violates assumptions of parametric tests. Examples are provided of when to use parametric or non-parametric tests depending on the type of data and statistical test being performed.

non parametric test.pptx

This document discusses non-parametric tests and when they should be used. Non-parametric tests make fewer assumptions than parametric tests and can be used when the sample size is small, the population is not normally distributed, or measurements are on an ordinal scale. Common non-parametric tests include the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U test, median test, and Kruskal-Wallis test. These tests do not rely on population parameters and can be used as alternatives to parametric tests like the t-test when parametric assumptions are not met.

Day-2_Presentation for SPSS parametric workshop.pptx

This document provides an overview of parametric statistical tests, including t-tests and analysis of variance (ANOVA). It discusses the concepts of statistical inference, hypothesis testing, null and alternative hypotheses, types of errors, critical regions, p-values, assumptions of t-tests, and procedures for one-sample t-tests, independent and paired t-tests, one-way ANOVA, and repeated measures ANOVA. The document is intended as part of an online workshop on using SPSS for advanced statistical data analysis.

T test

This document provides an overview of a presentation on statistical hypothesis testing using the t-test. It discusses what a t-test is, how to perform a t-test, and provides an example of a t-test comparing spelling test scores of two groups that received different teaching strategies. The document outlines the six steps for conducting statistical hypothesis testing using a t-test: 1) stating the hypotheses, 2) choosing the significance level, 3) determining the critical values, 4) calculating the test statistic, 5) comparing the test statistic to the critical values, and 6) writing a conclusion.

Chi square test social research refer.ppt

This document discusses various statistical tests, including parametric tests that require normally distributed data like t-tests and ANOVA, non-parametric tests that don't require normality like the Mann-Whitney U test, and the chi-square test. It explains that chi-square is used to determine if there is a relationship between two categorical variables by comparing observed and expected frequencies in a contingency table. It provides steps for conducting a chi-square test including stating hypotheses, calculating expected values, determining degrees of freedom, finding the test statistic, and interpreting results. Two examples of applying chi-square to test associations between disease prevalence and other factors are also presented.

Statistical analysis

Statistical analysis

Chi squared test

Chi squared test

Statistical analysis.pptx

Statistical analysis.pptx

7- Quantitative Research- Part 3.pdf

7- Quantitative Research- Part 3.pdf

Statistics

Statistics

Statistic and orthodontic by almuzian

Statistic and orthodontic by almuzian

tests of significance

tests of significance

Basic stat analysis using excel

Basic stat analysis using excel

Test of significance in Statistics

Test of significance in Statistics

Significance Tests

Significance Tests

Non Parametric Test by Vikramjit Singh

Non Parametric Test by Vikramjit Singh

Chi square mahmoud

Chi square mahmoud

Univariate Analysis

Univariate Analysis

Stat topics

Stat topics

Non parametric test

Non parametric test

Presentation chi-square test & Anova

Presentation chi-square test & Anova

non parametric test.pptx

non parametric test.pptx

Day-2_Presentation for SPSS parametric workshop.pptx

Day-2_Presentation for SPSS parametric workshop.pptx

T test

T test

Chi square test social research refer.ppt

Chi square test social research refer.ppt

melasma(Manguu) poster presentation.pptx

melasma, melasma poster presentation, introduction of melasma, symptoms of melasma, Diagnosis for melasma, causes and risk factors of melasma, drugs used for melasma, mask of pregnancy, chloasma , where did melasma occur, pharmacy, MBBS, medical field, general introduction on melasma ,M. pharmacy .

Aerosols, Propellants and types of propellants , Containers , types of conta...

Aerosols, propellants , types of propellants,
containers ,types of containers, Definition of aerosols, History of aerosols, what is propellent, Types of propellant, liquefied gases propellants, chlorofluoro carbons, hydrocarbons, hydrofluoro alkanes, compressed gasses propellant, blending of propellant, Dalton's law, containers for aerosols, types of containers for aerosols, tin plates steel container, aluminium containers, stainless steel containers, glass containers, uncoated glass container, plastic coated glass container, m. pharm 1 year, pharmaceutics, Novel targeted drug delivery system.

Main Points of UV and IR spectroscopy BY Puttamreddykavyasri

uv spectroscopy, theory of UV spectroscopy, electron transitions,
beer's law, lambert's law, beer lamberts law, instrumentation of UV spectroscopy, solvent effect on UV analysis, choice of solvent , solvent effect , chromophore, auxochrome, IR spectroscopy, principle of IR spectroscopy, applications of IR, applications of UV, analysis subjects, pharmaceutical analysis M.pharm 1 year, 1 year

preparation and evaluation of polyherbal scented candle using volatile oils e...

enfleurage method, preparation of scented candle , evaluation parameters of volatile oils.

Alternative methods of dissolution testing and meeting dissolution requriments

alternative methods of dissolution,
meeting dissolution requirements,
dissolution, sink conditions, non sink condition, natural convection non sink methods, forced convention non sink methods, forced convection sink devices, continous flow/flow through methods, klein solvmeter method, nelson hanging pellet method, levy staticdisc method, tumbling method, levy or beaker method, rotating disc method, particle sixze method, USP rotating basket method, USP paddle apparatus, Wurster polis adsorption method, partition method, Dialysis methods, Rotating disk apparatus,Pernarowski method
Langenbucher method, Baun and walke, Tingastad and Reigelman, Modified column apparatus, Takenaka method.

Modern pharmacutics consolidation 5 unit.pptx

consolidation parameters, consolidation definition, consolidation parameters,
diffusion parameters,
dissolution parameters,
heckle plot, consolidation process, cold welding, fusion bonding, mechanical theory, inter molecular forces theory, liquid surface film theory, factors effecting consolidation, driving froces that facilitate diffusion, parameters related to diffusion in drug release, parameters in dissolution process, affect of agitation on dissolution, effect of dissolution fluid on dissolution, pH on dissolution fluid, viscosity effect on dissolution, effect of temperature on dissolution, pharmacokinetics parameters, C max, T max, AUC(area under curve), heckel plot, application and limitations of heckel plot, methods to compare dissolution profile, model independent method(F1 AND F2)difference factor, similarity factors, limits of difference factor and similarity factors, higuchi model(Diffusion matrix formulation), korsmeyer peppas model(the power law).

SUNSCREEN, definition, classification, SPF value, history, mechanism, develop...

Sunscreen, SPF value ,definition of sunscreen, classification of sunscreen, mechanism of sunscreen, organic sunscreens, inorganic sunscreens, formulation of sunscreen, UV radiation, effects of UV radiation, spectrum UV radiation, analysis of final product, persistent pigment darkening(PPD)

Occular Drug Delivary system (ODDS)

Occular Drug Delivery System
ODDS
advantages of OODS
disadvantages of ODDS
Factors effecting ODDS
Barriers of drug permeation
methods to overcome barriers
drug delivery system
novel drug delivery system
B. pharmacy
M. pharmacy

Quality control tests for liquid dosage forms

quality control test for liquid dosage forms,in liquid dosage form their are again two types monophasic and biphasic .monophasic conatains syrups,elixiers and biphasic contains suspensions and emulsions

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melasma(Manguu) poster presentation.pptx

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preparation and evaluation of polyherbal scented candle using volatile oils e...

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Modern pharmacutics consolidation 5 unit.pptx

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Quality control tests for liquid dosage forms

Quality control tests for liquid dosage forms

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Telepharmacy/ppt/slides

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June 2024 Oncology Cartoons By Dr Kanhu Charan Patro

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These lecture slides, by Dr Sidra Arshad, offer a simplified look into the mechanisms involved in the regulation of respiration:
Learning objectives:
1. Describe the organisation of respiratory center
2. Describe the nervous control of inspiration and respiratory rhythm
3. Describe the functions of the dorsal and respiratory groups of neurons
4. Describe the influences of the Pneumotaxic and Apneustic centers
5. Explain the role of Hering-Breur inflation reflex in regulation of inspiration
6. Explain the role of central chemoreceptors in regulation of respiration
7. Explain the role of peripheral chemoreceptors in regulation of respiration
8. Explain the regulation of respiration during exercise
9. Integrate the respiratory regulatory mechanisms
10. Describe the Cheyne-Stokes breathing
Study Resources:
1. Chapter 42, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 36, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 13, Human Physiology by Lauralee Sherwood, 9th edition

Lecture 6 -- Memory 2015.pptlearning occurs when a stimulus (unconditioned st...

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This SlideShare presentation provides a comprehensive overview of the Declaration of Helsinki, a foundational document outlining ethical guidelines for conducting medical research involving human subjects.

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The skin is the largest organ and its health plays a vital role among the other sense organs. The skin concerns like acne breakout, psoriasis, or anything similar along the lines, finding a qualified and experienced dermatologist becomes paramount.

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Travel Clinic Cardiff offers comprehensive travel health services, including vaccinations, travel advice, and preventive care for international travelers. Our expert team ensures you are well-prepared and protected for your journey, providing personalized consultations tailored to your destination. Conveniently located in Cardiff, we help you travel with confidence and peace of mind. Visit us: www.nxhealthcare.co.uk

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Obesity is a chronic complex disease defined by excessive fat deposits that can impair health.

Ageing, the Elderly, Gerontology and Public Health

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CBL Seminar 2024_Preliminary Program.pdf

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A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!

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The key to a good grip on asthma is proper knowledge and management strategies. Understanding the patient-specific symptoms and carving out an effective treatment likewise is the best way to keep asthma under control.

STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7

Unit 4: MRA 103T Regulatory affairs
This guideline is directed principally toward new Molecular Entities that are
likely to have significant use in the elderly, either because the disease intended
to be treated is characteristically a disease of aging ( e.g., Alzheimer's disease) or
because the population to be treated is known to include substantial numbers of
geriatric patients (e.g., hypertension).

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5-hydroxytryptamine or 5-HT or Serotonin is a neurotransmitter that serves a range of roles in the human body. It is sometimes referred to as the happy chemical since it promotes overall well-being and happiness.
It is mostly found in the brain, intestines, and blood platelets.
5-HT is utilised to transport messages between nerve cells, is known to be involved in smooth muscle contraction, and adds to overall well-being and pleasure, among other benefits. 5-HT regulates the body's sleep-wake cycles and internal clock by acting as a precursor to melatonin.
It is hypothesised to regulate hunger, emotions, motor, cognitive, and autonomic processes.

acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals

cosmetics and cosmeceuticals
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Physical demands in sports - WCSPT Oslo 2024

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Case presentation On Urinary tract infection

Case presentation On Urinary tract infection

vonoprazan A novel drug for GERD presentation

vonoprazan A novel drug for GERD presentation

June 2024 Oncology Cartoons By Dr Kanhu Charan Patro

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Lecture 6 -- Memory 2015.pptlearning occurs when a stimulus (unconditioned st...

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How to choose the best dermatologists in Indore.

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STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7

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Pharmacology of 5-hydroxytryptamine and Antagonist

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acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals

acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals

- 1. Linearity concept of significance standard deviation,chi square test,students T- test ,ANOVA test Puttamreddy kavyasri M.pharmacy
- 2. C O N T E N T S 01 02 03 04 Introduction Standarrd deviation Chi square test T -test, ANOVA test
- 4. Definition of significance testing: In statistics, it is important to know if the result of an experiment is significant enough or not.In order to measure the significance, there are some predefined tests which could be applied. These tests are called the tests of significance or simply the significance tests. A test of significance is a formal procedure for comparing observed data with a claim (also called a hypothesis), the truth of which is being assessed. • The claim is a statement about a parameter, like the population proportion p or the population mean µ. • The results of a significance test are expressed in terms of a probability that measures how well the data and the claim agree.
- 5. Process of significance testing: In the process of testing for statistical significance, there are the following steps: • Stating a hypothesis for reasearch • Starting a null hypothesis • Selecting a probability of error level • Selecting and computing a satistical significance test • Interpreting the results • The claim tested by a statistical test is called the null hypothesis(Ho) • The test is designed to assess the strength of evidence against the null hypothesis. often the null hypothesis is a statement of “no dofference.” • The claim about the population that evidence is being sought for is the alternative hypothesis(Ha)
- 6. 2 Standard deviation: Statistical measurement of the amount a number varies from the average number in a series.
- 7. In statistics, the standard deviation is a measure of the amount of variation of a random variable expected about its mean. A low standard deviation means that the data is very closely related to the average, thus very reliable. A high standard deviation means that there is a large variance between the data and the statistical average, and is not as reliable. The square root of the mean of he squares of all the values of a series derived from the arithematic mean which is also called as the root mean square deviation. 0 is the smallest value of standard deviation since it cannot be negative. Standard deviation measures the dispersion of data. it shows the average absolute distance of each point from the mean. The greater the value of standard deviation, the further the data tend to be dispersed from the mean. it is most reliable
- 9. 3 Chi square test: A chi-squared test (also chi-square or χ 2 test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large.
- 10. A chi-squared test (symbolically represented as χ2) is basically a data analysis on the basis of observations of a random set of variables. Usually, it is a comparison of two statistical data sets. This test was introduced by Karl Pearson in 1900 for categorical data analysis and distribution. So it was mentioned as Pearson’s chi- squared test. The chi-square test is used to estimate how likely the observations that are made would be, by considering the assumption of the null hypothesis as true.
- 11. CHI SQUARE TEAT is a non parametric test not based on any assumption or distribution of any variable. This statistical test follows a specific distribution know as chi square distribution. In general the tet we use to measure the differences between what is observed and what is expected according to an assumed hypothesis is called the CHI SQUARE TEST. IMPORTANT CHARECTERISTICS OF CHI SQUARE TEST • This test (as non parametric test) is based on frequencies and not on the parameters like mean and standard deviation. • The test is used for testing the hypothesis and is not useful for estimation. • This test can also be applied to a complex contingency table with servral classes and suchh is a very useful test in research work. • This test is an important npn parametric test as no rigid assumptions are necessary in regarad to the type of population,no need of parameter values and relatively less mathematical details are involved.
- 12. Chi square Distribution: If degree of freedom >2 : Distributionis bell shaped If degree of freddom =2 : Distribution is L shaped with maximum ordinate at zero If degree of freedom <2(>0) : Distribution L shaped with infinite ordinate at the origin.
- 13. Applications of chi square test: 2 . . . 1 . . . 3 . . . Goodness of fit of distributions Test of independence of attributes Test of homoge nity
- 14. Formula: X2 = ∑ (o - e) 2 e Where , o = observed frequency e = expected frequency if two distributions (observed and theoretical ) are exactly alike, x2 = 0;(but generally due to sampling errors, x2 is not equal to zero) limitations • Data in form of random sample • Contingency tables larger than2 *2, • Sample total less than 50
- 15. T- TEST: • It is a statistical test • Determine if there is a significant difference between the means of two groups and how they are related. • There are : One sample T- Test Independent T- Test Paired T- Test
- 16. T-test Comparative study of means Mean of group A Mean group B Finds the probability of the differences to have happened by chance Used to compare two samples to determine if they came from the same population.
- 17. T- Test formula: One sample T- test Two same T- test μ - assumed mean s - standard deviation n - sample size x-- observesd mean of the sample
- 18. ANOVA TEST: Analysis of Variance • Variability ratio • There are : One way ANOVA Two way ANOVA Three way ANOVA N way ANOVA Mixed model ANOVA Factorial ANOVA ANOVA (Analysis of Variance) Statistical model Used to Determines Assess the differences between means of two independent groups The effect of the independent variable on the dependent variable
- 19. • Analysis of variance (ANOVA) is a method for testing the hypothesis that there is no difference between two or more population means. variance between + variance with in = Total variance F = Variability between group Variability within groups