This document discusses statistical methods for comparing means, including t-tests and analysis of variance (ANOVA). It explains how t-tests can be used to compare two means or paired samples, and how ANOVA can compare two or more means. Key assumptions and procedures are outlined for one-sample t-tests, paired t-tests, independent t-tests with equal and unequal variances, and one-way between-subjects ANOVAs.
This presentation contains information about Mann Whitney U test, what is it, when to use it and how to use it. I have also put an example so that it may help you to easily understand it.
The document discusses normal and standard normal distributions. It provides examples of using a normal distribution to calculate probabilities related to bone mineral density test results. It shows how to find the probability of a z-score falling below or above certain values. It also explains how to determine the sample size needed to estimate an unknown population proportion within a given level of confidence.
This document provides information about statistical tests and data analysis presented by Dr. Muhammedirfan H. Momin. It discusses the different types of statistical data, such as qualitative vs quantitative and continuous vs discrete data. It also covers topics like sample data sets, frequency distributions, risk factors for diseases, hypothesis testing, and tests for comparing proportions and means. Specific statistical tests discussed include the z-test and how to calculate test statistics and compare them to critical values to determine statistical significance. Examples are provided to illustrate how to perform these tests to analyze differences between data sets.
Estimation and hypothesis testing 1 (graduate statistics2)Harve Abella
This document discusses two main areas of statistical inference: estimation and hypothesis testing. It provides details on point estimation and confidence interval estimation when estimating population parameters. It also explains the key concepts involved in hypothesis testing such as the null and alternative hypotheses, types of errors, critical regions, test statistics, and p-values. Examples are provided to illustrate estimating population means and proportions as well as conducting hypothesis tests.
The Mann-Whitney U Test is used to compare two independent groups on an ordinal scale. It tests the null hypothesis that there is no difference between the groups' rankings. The document provides an example comparing traditional language learning to immersion learning. Students' Spanish test scores were ranked, and the Mann-Whitney U Test found a significant difference, rejecting the null hypothesis. The immersion group had higher rankings than the traditional group, showing greater Spanish proficiency from immersion learning.
The chi-square test is used to determine if there is a relationship between two categorical variables in two or more independent groups. It can be used when data is arranged in a contingency table with observed and expected frequencies. A sample problem demonstrates how to calculate chi-square by finding the difference between observed and expected counts, squaring these differences, dividing by the expected counts, and summing across all cells. Degrees of freedom and critical values from tables determine whether to reject or fail to reject the null hypothesis of independence. Larger tables can be partitioned into subtables to identify where differences lie. Guidelines are provided for when chi-square or Fisher's exact test should be used based on sample size and expected cell counts.
Researchers tested a new anti-anxiety medication on 200 people and a placebo on another 200 people. 64 of those on the medication and 92 of those on the placebo reported anxiety symptoms. The researchers want to determine if there is a statistically significant difference in reported anxiety between the two groups using a two-sample z-test with an alpha of 0.05. A two-sample z-test is used to compare differences between two sample proportions and determines if any observed difference is likely due to chance or not.
This document discusses statistical methods for comparing means, including t-tests and analysis of variance (ANOVA). It explains how t-tests can be used to compare two means or paired samples, and how ANOVA can compare two or more means. Key assumptions and procedures are outlined for one-sample t-tests, paired t-tests, independent t-tests with equal and unequal variances, and one-way between-subjects ANOVAs.
This presentation contains information about Mann Whitney U test, what is it, when to use it and how to use it. I have also put an example so that it may help you to easily understand it.
The document discusses normal and standard normal distributions. It provides examples of using a normal distribution to calculate probabilities related to bone mineral density test results. It shows how to find the probability of a z-score falling below or above certain values. It also explains how to determine the sample size needed to estimate an unknown population proportion within a given level of confidence.
This document provides information about statistical tests and data analysis presented by Dr. Muhammedirfan H. Momin. It discusses the different types of statistical data, such as qualitative vs quantitative and continuous vs discrete data. It also covers topics like sample data sets, frequency distributions, risk factors for diseases, hypothesis testing, and tests for comparing proportions and means. Specific statistical tests discussed include the z-test and how to calculate test statistics and compare them to critical values to determine statistical significance. Examples are provided to illustrate how to perform these tests to analyze differences between data sets.
Estimation and hypothesis testing 1 (graduate statistics2)Harve Abella
This document discusses two main areas of statistical inference: estimation and hypothesis testing. It provides details on point estimation and confidence interval estimation when estimating population parameters. It also explains the key concepts involved in hypothesis testing such as the null and alternative hypotheses, types of errors, critical regions, test statistics, and p-values. Examples are provided to illustrate estimating population means and proportions as well as conducting hypothesis tests.
The Mann-Whitney U Test is used to compare two independent groups on an ordinal scale. It tests the null hypothesis that there is no difference between the groups' rankings. The document provides an example comparing traditional language learning to immersion learning. Students' Spanish test scores were ranked, and the Mann-Whitney U Test found a significant difference, rejecting the null hypothesis. The immersion group had higher rankings than the traditional group, showing greater Spanish proficiency from immersion learning.
The chi-square test is used to determine if there is a relationship between two categorical variables in two or more independent groups. It can be used when data is arranged in a contingency table with observed and expected frequencies. A sample problem demonstrates how to calculate chi-square by finding the difference between observed and expected counts, squaring these differences, dividing by the expected counts, and summing across all cells. Degrees of freedom and critical values from tables determine whether to reject or fail to reject the null hypothesis of independence. Larger tables can be partitioned into subtables to identify where differences lie. Guidelines are provided for when chi-square or Fisher's exact test should be used based on sample size and expected cell counts.
Researchers tested a new anti-anxiety medication on 200 people and a placebo on another 200 people. 64 of those on the medication and 92 of those on the placebo reported anxiety symptoms. The researchers want to determine if there is a statistically significant difference in reported anxiety between the two groups using a two-sample z-test with an alpha of 0.05. A two-sample z-test is used to compare differences between two sample proportions and determines if any observed difference is likely due to chance or not.
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,
This document provides an overview of various statistical analysis techniques used in inferential statistics, including t-tests, ANOVA, ANCOVA, chi-square, regression analysis, and interpreting null hypotheses. It defines key terms like alpha levels, effect sizes, and interpreting graphs. The overall purpose is to explain common statistical methods for analyzing data and determining the probability that results occurred by chance or were statistically significant.
This document discusses sample size estimation and the factors that influence determining an appropriate sample size for research studies. It provides examples of calculating sample sizes based on prevalence of a disease, mean values, standard deviations, permissible errors, and confidence levels. The key points are:
- Sample size depends on prevalence/magnitude of the attribute being studied, permissible error, and power of the statistical test
- Larger sample sizes are needed to detect smaller differences and have sufficient power
- Examples are provided to demonstrate calculating sample sizes based on prevalence of anemia, mean blood pressure values, and acceptable margins of error
BMI (kg/m2)
22.1
23.4
24.8
26.2
27.6
28.9
30.3
31.6
32.9
34.2
35.5
36.8
38.1
39.4
The sample mean is 29.1 kg/m2 and the sample standard
deviation is 4.2 kg/m2. Test the hypothesis that the
population mean BMI is 30 kg/m2 at 5% level of
significance.
v When to Choose a Statistical Tests OR When NOT to Choose? v Parametric vs. Non-Parametric Tests (Comparison)
v Parameters to check when Choosing a Statistical Test:
- Distribution of Data
- Type of data/Variable
- Types of Analysis (What’s the hypothesis)
- No of groups or data-sets
- Data Group Design
v Snapshot of all statistical test and “How” to Choose using above parameters v Explanation using Examples:
- Mann Whitney U Test
- Wilcoxon Sign Rank Test
- Spearman’s co-relation
- Chi-Square Test
v Conclusion
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 document discusses how to choose the appropriate statistical test based on the characteristics of the data. It outlines several key considerations for selecting a test, including the number and type of variables, whether the data is paired or independent, and if the continuous variables follow a normal distribution. The document then describes many commonly used statistical tests for different types of comparisons, including onesample, bivariate, and multivariate tests. It emphasizes that the correct statistical test must be applied to ensure valid conclusions can be drawn from the data analysis.
This document discusses descriptive statistics for one variable. Descriptive statistics summarize and describe data through measures of central tendency (mean, median, mode), variability (variance, standard deviation), and relative standing (percentiles). The mean is the average value, the median is the middle value, and the mode is the most frequent value. Variance and standard deviation describe how spread out the data is. Percentiles indicate what percentage of values are below a given number. Examples are provided to demonstrate calculating and interpreting these common descriptive statistics.
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.
This document provides an overview of descriptive statistics techniques for summarizing categorical and quantitative data. It discusses frequency distributions, measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and methods for visualizing data through charts, graphs, and other displays. The goal of descriptive statistics is to organize and describe the characteristics of data through counts, averages, and other summaries.
Statistical tests for data involving quantitative dataRizwan S A
This document discusses statistical tests for quantitative data. It begins by defining quantitative variables and different types of quantitative scales. It then discusses prerequisites for choosing a statistical test, including the number of variables, nature of dependent and independent variables, and whether variables are normally distributed. The document outlines various parametric and non-parametric statistical tests for both paired and unpaired quantitative data, including t-tests, ANOVA, correlation, regression, Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test. Examples of applying some of these tests are provided.
1) The document discusses commonly used statistical tests in research such as descriptive statistics, inferential statistics, hypothesis testing, and tests like t-tests, ANOVA, chi-square tests, and normal distributions.
2) It provides examples of how to determine sample sizes needed for adequate power in hypothesis testing and how to perform t-tests to analyze sample means.
3) Key statistical concepts covered include parameters, statistics, measurement scales, type I and II errors, and interpreting results of hypothesis tests.
This document provides an overview of the Z test for two sample means. It defines the Z test, outlines when it is used, and provides the formula and steps to conduct a hypothesis test using the Z test. An example problem is included that tests if there is a significant difference in average monthly family incomes between two neighborhoods using census data from random samples of 100 families each.
Nonparametric tests can analyze ordinal or nominal data without assumptions about the population distribution. They include the chi-square test, Kruskal-Wallis test, Wilcoxon signed-rank test, median test, and sign test. SPSS examples demonstrate using the binomial test to compare a proportion to 50%, the Kolmogorov-Smirnov test to check normality, and Kruskal-Wallis to compare more than two independent groups.
1) The chi-square test is a nonparametric test used to analyze categorical data when assumptions of parametric tests are violated. It compares observed frequencies to expected frequencies specified by the null hypothesis.
2) The chi-square test can test for goodness of fit, evaluating if sample proportions match population proportions. It can also test independence, assessing relationships between two categorical variables.
3) To perform the test, observed and expected frequencies are calculated and entered into the chi-square formula. The resulting statistic is compared to critical values of the chi-square distribution to determine significance.
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.
This document discusses skewness and kurtosis, which are statistical measures of the distribution of a variable. Skewness measures the asymmetry of a distribution and can be positive, negative, or zero. Kurtosis measures the peakedness of a distribution and can be platykurtic (flatter than normal), mesokurtic (normal), or leptokurtic (more peaked than normal). The document provides formulas for calculating skewness using Pearson's, Bowley's, and Kelly's coefficients as well as calculating kurtosis using the fourth standardized moment. Examples of applying skewness and kurtosis to determine if a variable's distribution or resource use is normal are also discussed.
This document provides information about the Kruskal-Wallis H test, a non-parametric method for testing whether samples originate from the same distribution. It describes how the Kruskal-Wallis test is a generalization of the Mann-Whitney U test that allows comparison of more than two independent groups. The test works by ranking all data from lowest to highest and then summing the ranks for each group to calculate the test statistic H, which is compared to a chi-squared distribution to determine whether to reject or fail to reject the null hypothesis that all population medians are equal.
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.
BASIC STATISTICS AND THEIR INTERPRETATION AND USE IN EPIDEMIOLOGY 050822.pdfAdamu Mohammad
This document provides an introduction to basic statistical concepts and their use in epidemiology. It discusses different types of data including categorical, quantitative, discrete, and continuous data. It also covers measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). The document introduces the concepts of skewness and the normal distribution. It then discusses inferential statistics, hypothesis testing, and parametric vs non-parametric tests. Key statistical tests are outlined depending on whether populations are related or independent. The overall goal is to provide health professionals with foundational statistical knowledge for investigating medical science.
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,
This document provides an overview of various statistical analysis techniques used in inferential statistics, including t-tests, ANOVA, ANCOVA, chi-square, regression analysis, and interpreting null hypotheses. It defines key terms like alpha levels, effect sizes, and interpreting graphs. The overall purpose is to explain common statistical methods for analyzing data and determining the probability that results occurred by chance or were statistically significant.
This document discusses sample size estimation and the factors that influence determining an appropriate sample size for research studies. It provides examples of calculating sample sizes based on prevalence of a disease, mean values, standard deviations, permissible errors, and confidence levels. The key points are:
- Sample size depends on prevalence/magnitude of the attribute being studied, permissible error, and power of the statistical test
- Larger sample sizes are needed to detect smaller differences and have sufficient power
- Examples are provided to demonstrate calculating sample sizes based on prevalence of anemia, mean blood pressure values, and acceptable margins of error
BMI (kg/m2)
22.1
23.4
24.8
26.2
27.6
28.9
30.3
31.6
32.9
34.2
35.5
36.8
38.1
39.4
The sample mean is 29.1 kg/m2 and the sample standard
deviation is 4.2 kg/m2. Test the hypothesis that the
population mean BMI is 30 kg/m2 at 5% level of
significance.
v When to Choose a Statistical Tests OR When NOT to Choose? v Parametric vs. Non-Parametric Tests (Comparison)
v Parameters to check when Choosing a Statistical Test:
- Distribution of Data
- Type of data/Variable
- Types of Analysis (What’s the hypothesis)
- No of groups or data-sets
- Data Group Design
v Snapshot of all statistical test and “How” to Choose using above parameters v Explanation using Examples:
- Mann Whitney U Test
- Wilcoxon Sign Rank Test
- Spearman’s co-relation
- Chi-Square Test
v Conclusion
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 document discusses how to choose the appropriate statistical test based on the characteristics of the data. It outlines several key considerations for selecting a test, including the number and type of variables, whether the data is paired or independent, and if the continuous variables follow a normal distribution. The document then describes many commonly used statistical tests for different types of comparisons, including onesample, bivariate, and multivariate tests. It emphasizes that the correct statistical test must be applied to ensure valid conclusions can be drawn from the data analysis.
This document discusses descriptive statistics for one variable. Descriptive statistics summarize and describe data through measures of central tendency (mean, median, mode), variability (variance, standard deviation), and relative standing (percentiles). The mean is the average value, the median is the middle value, and the mode is the most frequent value. Variance and standard deviation describe how spread out the data is. Percentiles indicate what percentage of values are below a given number. Examples are provided to demonstrate calculating and interpreting these common descriptive statistics.
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.
This document provides an overview of descriptive statistics techniques for summarizing categorical and quantitative data. It discusses frequency distributions, measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and methods for visualizing data through charts, graphs, and other displays. The goal of descriptive statistics is to organize and describe the characteristics of data through counts, averages, and other summaries.
Statistical tests for data involving quantitative dataRizwan S A
This document discusses statistical tests for quantitative data. It begins by defining quantitative variables and different types of quantitative scales. It then discusses prerequisites for choosing a statistical test, including the number of variables, nature of dependent and independent variables, and whether variables are normally distributed. The document outlines various parametric and non-parametric statistical tests for both paired and unpaired quantitative data, including t-tests, ANOVA, correlation, regression, Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test. Examples of applying some of these tests are provided.
1) The document discusses commonly used statistical tests in research such as descriptive statistics, inferential statistics, hypothesis testing, and tests like t-tests, ANOVA, chi-square tests, and normal distributions.
2) It provides examples of how to determine sample sizes needed for adequate power in hypothesis testing and how to perform t-tests to analyze sample means.
3) Key statistical concepts covered include parameters, statistics, measurement scales, type I and II errors, and interpreting results of hypothesis tests.
This document provides an overview of the Z test for two sample means. It defines the Z test, outlines when it is used, and provides the formula and steps to conduct a hypothesis test using the Z test. An example problem is included that tests if there is a significant difference in average monthly family incomes between two neighborhoods using census data from random samples of 100 families each.
Nonparametric tests can analyze ordinal or nominal data without assumptions about the population distribution. They include the chi-square test, Kruskal-Wallis test, Wilcoxon signed-rank test, median test, and sign test. SPSS examples demonstrate using the binomial test to compare a proportion to 50%, the Kolmogorov-Smirnov test to check normality, and Kruskal-Wallis to compare more than two independent groups.
1) The chi-square test is a nonparametric test used to analyze categorical data when assumptions of parametric tests are violated. It compares observed frequencies to expected frequencies specified by the null hypothesis.
2) The chi-square test can test for goodness of fit, evaluating if sample proportions match population proportions. It can also test independence, assessing relationships between two categorical variables.
3) To perform the test, observed and expected frequencies are calculated and entered into the chi-square formula. The resulting statistic is compared to critical values of the chi-square distribution to determine significance.
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.
This document discusses skewness and kurtosis, which are statistical measures of the distribution of a variable. Skewness measures the asymmetry of a distribution and can be positive, negative, or zero. Kurtosis measures the peakedness of a distribution and can be platykurtic (flatter than normal), mesokurtic (normal), or leptokurtic (more peaked than normal). The document provides formulas for calculating skewness using Pearson's, Bowley's, and Kelly's coefficients as well as calculating kurtosis using the fourth standardized moment. Examples of applying skewness and kurtosis to determine if a variable's distribution or resource use is normal are also discussed.
This document provides information about the Kruskal-Wallis H test, a non-parametric method for testing whether samples originate from the same distribution. It describes how the Kruskal-Wallis test is a generalization of the Mann-Whitney U test that allows comparison of more than two independent groups. The test works by ranking all data from lowest to highest and then summing the ranks for each group to calculate the test statistic H, which is compared to a chi-squared distribution to determine whether to reject or fail to reject the null hypothesis that all population medians are equal.
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.
BASIC STATISTICS AND THEIR INTERPRETATION AND USE IN EPIDEMIOLOGY 050822.pdfAdamu Mohammad
This document provides an introduction to basic statistical concepts and their use in epidemiology. It discusses different types of data including categorical, quantitative, discrete, and continuous data. It also covers measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). The document introduces the concepts of skewness and the normal distribution. It then discusses inferential statistics, hypothesis testing, and parametric vs non-parametric tests. Key statistical tests are outlined depending on whether populations are related or independent. The overall goal is to provide health professionals with foundational statistical knowledge for investigating medical science.
This document provides an overview of biostatistics. It defines key biostatistics terms like variable, data, and statistics. It discusses principles of biostatistics in research and epidemiology. It describes different data types and scales of measurement. It explains concepts like probability distribution, p-value, confidence interval, measures of central tendency, and measures of dispersion. It provides examples of these statistical techniques and how they are used to analyze and interpret biological data.
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.
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.
This document provides an overview of statistical methods used in research. It discusses descriptive statistics such as frequency distributions and measures of central tendency. It also covers inferential statistics including hypothesis testing, choice of statistical tests, and determining sample size. Various types of variables, measurement scales, charts, and distributions are defined. Inferential topics include correlation, regression, and multivariate techniques like multiple regression and factor analysis.
I do not have enough information to determine what percentage of residents are asleep now versus at the beginning of this talk. As an AI assistant without direct observation of the audience, I do not have data on individual residents' states of alertness over time.
This document provides an overview of analyzing data using SPSS. It discusses exploring the data through descriptive statistics and graphs. Key steps include formulating hypotheses, checking for normality, selecting the appropriate statistical test, and interpreting the results by reporting test statistics, p-values, and whether the null hypothesis is rejected or not. Different types of variables, data, and common statistical tests are defined.
This document provides an overview of descriptive and inferential statistics concepts. It discusses parameters versus statistics, descriptive versus inferential statistics, measures of central tendency (mean, median, mode), variability (standard deviation, range), distributions (normal, positively/negatively skewed), z-scores, correlations, hypothesis testing, t-tests, ANOVA, chi-square tests, and presenting results. Key terms like alpha levels, degrees of freedom, effect sizes, and probabilities are also introduced at a high level.
This document provides an introduction to key concepts in statistics, including scales of measurement for categorical and numerical variables, methods for displaying categorical data, measures of central tendency like mean, median and mode, measures of numerical spread such as range and interquartile range, the concept of association and correlation between variables, and the concept of regression. The document defines key terms and provides examples to illustrate statistical concepts.
Introduction to Data Management in Human EcologyKern Rocke
This document provides an introduction to data management concepts in human ecology. It defines data and describes common data types like qualitative and quantitative data. It also discusses topics like sources of data, types of statistical analyses, strategies for computer-aided analysis, principles of statistical analysis, and interpreting p-values. Examples of statistical programs and various statistical analysis methods for comparing groups and exploring relationships between variables are also outlined.
1) Statistics is the science of collecting, analyzing, and drawing conclusions from data. It is used to understand populations based on samples since directly measuring entire populations is often impossible.
2) There are two main types of data: qualitative data which relates to descriptive characteristics, and quantitative data which can be expressed numerically. Common statistical analyses include calculating the mean, standard deviation, and using t-tests, ANOVA, correlation, and chi-squared tests.
3) Statistical analyses allow researchers to determine uncertainties in measurements, compare groups, identify relationships between variables, and assess whether observed differences are likely due to chance or a factor being studied. Key concepts include null and alternative hypotheses, p-values, and effect size.
This document provides an introduction to statistics and key statistical concepts. It defines important terminology like data, variables, and different types of variables. It explains how to quantify variables as categorical or numerical, and the different scales used to measure data, including nominal, ordinal, interval, and ratio scales. It also outlines different types of data including categorical, discrete, and continuous data. The document concludes by describing common methods to numerically summarize data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation, coefficient of variation).
This document provides an introduction to biostatistics and key concepts. It defines biostatistics as the development and application of statistical techniques to scientific research relating to human life and health. Some key terms discussed include:
- Population, which is the totality of individuals of interest
- Sample, which is a subset of a population
- Variables, which can be qualitative (non-numerical) or quantitative (numerical)
- Levels of measurement for variables, including nominal, ordinal, interval, and ratio scales
- Descriptive methods for qualitative data, including frequency distributions
Biostatistics plays an important role in modern medicine, including determining disease burden, finding new drug treatments, planning resource allocation, and measuring
Summary of statistical tools used in spssSubodh Khanal
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.
TESTS OF SIGNIFICANCE
Deals with techniques to know how far the difference between the estimates of different samples is due to sampling variation.
Standard error (S.E) of Mean = S.D/√n
Standard error (S.E) of Proportion = √pq/n
Tests of significance:
Can be broadly classified into 2 types
1. Parametric tests (or) standard tests of hypothesis
2. Non – Parametric tests (or) distribution free-test of hypothesis
PARAMETRIC TESTS:
Parametric test is a statistical test that makes assumptions about the parameters of the population distribution(s) from which ones data is drawn.
When to use parametric test???
Subjects should be randomly selected
Data should be normally distributed
Homogeneity of variances
The important parametric tests are:
1) z-test
2) t-test
3) ANOVA
4) Pearson correlation coefficient
Z - Test:
This is a most frequently used test in research studies.
Z - test is based on the normal probability distribution and is used for judging the significance of several statistical measures, particularly the mean.
Z - test is used when sample size greater than 30. Test of significance for large samples
Z = observation – mean
SD
Prerequisites to apply z- test
Sample must be selected randomly
Data must be quantitative
Variable is assumed to follow normal distribution in the population
Sample size must be greater than 30. if SD of population is known, z test can be applied even sample size is less than 30
2) t- Test
• In case of samples less than 30 the Z value will not follow the normal distribution
• Hence Z test will not give the correct level of significance
• In such cases students t test is used
• It was given by “WS Gossett” whose pen name was student. So, it is also called as Student test.
There are two types of student t Test
1. Unpaired t test
2. Paired t test
Criteria for applying t- test
1. Random samples
2. Quantitative data
3. Variable normally distributed
4. Sample size less than 30
Unpaired test:
• Applied to unpaired data of independent observation made on individuals of 2 separate groups or samples drawn from the population.
• To test if the difference between the 2 means is real or it can be due to sampling variability.
Paired t - test:
• It is applied to paired data of observation from one sample only (observation before and after taking a drug)
Examples:
1. Pulse rate before and after exertion
2. Plaque scores before and after using oral hygiene aid
3) ANOVA ( Analysis of Variance):
• Investigations may not always be confined to comparison of 2 samples only
• In such cases where more than 2 samples are used ANOVA can be used.
• Also when measurements are influenced by several factors playing their role e.g. factors affecting retention of a denture, ANOVA can be used.
Indications:
To compare more than two sample means
Types:
1. one-way ANIVA
2. Two-way ANOVA
3. Multi-way ANOVA
Pearson’s correlation
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.
This document provides an overview of key concepts in statistical analysis and research methods. It discusses different types of data and variables, levels of measurement, validity and reliability, experimental and correlational research designs, measures of central tendency and dispersion, the normal distribution, hypothesis testing, and types of errors. It also covers effect sizes, correlations, and misconceptions about p-values and statistical significance. The document is serving as a learning module to help prepare students for quantitative methods and research.
Soft Skills as a tool to improve quality of careSmriti Arora
Soft skills are non-technical skills that are important for quality patient care. They include empathy, communication, teamwork, work ethics, adaptability, time management, and critical thinking. Developing soft skills through role playing and simulations can help nurses provide more compassionate and effective care. Soft skills like active listening, clear communication, and cultural sensitivity promote trust between nurses and patients. This leads to greater patient well-being, satisfaction with care, and better health outcomes. Overall, soft skills are as important as technical medical knowledge for delivering high quality care.
Traits of Emotionally intelligent peopleSmriti Arora
Emotional intelligence involves perceiving, controlling, and using emotions effectively. It includes understanding one's own emotions as well as empathizing with others. Nine traits of emotionally intelligent people are discussed. They embrace change, have self-awareness, show empathy, pursue progress over perfection, have balanced lives, are curious learners, are grateful, express themselves assertively, and are receptive to feedback. Research found nurses' emotional intelligence was not related to demographics but was higher in more experienced nurses who felt successful professionally.
Child abuse can take many forms including physical, sexual, and emotional abuse as well as neglect. It is a widespread problem in India with over half of children experiencing some form of abuse. Physical abuse and sexual abuse are particularly common. Proper identification, documentation of injuries, collection of forensic evidence, and multidisciplinary management are important to address this issue. Prevention through education, community support, and strong legal protections can help reduce the incidence of child abuse.
Recent advances in nursing research.pdfSmriti Arora
This document discusses recent advances in nursing research and its application to clinical practice. It covers various topics like the importance of research in evidence-based nursing, different types of research studies, systematic reviews and meta-analyses, use of artificial intelligence and digital tools in research and clinical practice, qualitative research methods, and overcoming barriers to research. It emphasizes the role of nursing research in improving patient care and highlights areas where new technologies can help enhance research and remote monitoring of patients.
This document discusses utilizing patient care data from clinical settings for clinical research purposes. It describes the types of data available, common barriers faced, and the need to obtain proper permissions. A variety of research study designs are possible using this data, including descriptive studies, interventional studies, qualitative research, and quality improvement projects. Case studies, case series, surveys and collaboration are recommended approaches. Addressing barriers like permissions and developing research skills can help facilitate use of this valuable data source.
1) Making breastfeeding and work compatible requires policy changes like longer paid maternity leave, workplace accommodations for pumping and milk storage, and flexible schedules.
2) Employers can help by providing private rooms for pumping, refrigeration, and support from managers and colleagues.
3) Mothers need knowledge of pumping techniques, safe milk storage, and support for addressing issues like sore nipples. With these changes, breastfeeding can be made to work for both mothers and their jobs.
The document describes how to plan and implement an objective structured clinical examination (OSCE) for assessing pediatric nursing students. It discusses:
- The OSCE model based on Miller's hierarchy of clinical competence using simulated practice.
- Skills that can be assessed including clinical skills, decision making, communication, and time management.
- Locations for the OSCE including clinical areas with real patients or simulated labs.
- Steps for planning including time allotted, staffing needs, station types and content, and evaluation criteria.
- Types of stations such as manned stations where students perform skills and unmanned stations involving cases, images, and written responses.
- Examples of station content covering various pediatric topics, skills
This document discusses various tools used to assess development in children. It describes developmental milestones from birth to 3 years of age across different domains. The key tools mentioned include the Neonatal Behavioral Assessment Scale (NBAS), Denver II developmental screening test, Trivandrum Developmental Screening Chart (TDSC), Developmental Assessment Scale for Indian Infants (DASII), and growth charts like WHO charts. The document also discusses factors affecting growth and development as well as developmental theories and principles of child development.
This document discusses evidence-based research (EBR) and its importance in nursing practice. It defines EBR as using scientific research findings to make decisions about patient care rather than relying solely on opinion. The key advantages of EBR include improving clinical outcomes, reducing costs, and enhancing nurses' confidence and critical thinking. The document outlines the 5 steps of EBR - asking questions, acquiring evidence, appraising evidence quality, applying evidence, and assessing outcomes. It also discusses common barriers to implementing EBR and strategies to overcome them, such as promoting a culture of learning and allocating sufficient resources.
The document discusses developmental supportive care (DSC) for preterm infants in the neonatal intensive care unit (NICU). DSC aims to minimize stress and provide developmentally appropriate care by replicating aspects of the womb environment. This includes controlling light, sound, and temperature exposure; providing skin-to-skin contact; assessing infant cues and needs; and clustering care activities to allow for protected sleep. DSC has been shown to reduce stress, support brain development, and improve short- and long-term health, growth, and neurodevelopmental outcomes for preterm infants.
This document provides an overview of quantitative data analysis using SPSS. It discusses the objectives of analyzing how to enter data into SPSS and what statistical tests can be applied, including assessing normality, chi square, correlation coefficients, t-tests, and one-way ANOVA. It defines different types of variables that can be entered into SPSS and the three main types of data analysis: univariate, bivariate, and multivariate. Specific statistical tests are explained like independent t-tests, paired t-tests, correlation, chi square, Fisher's exact test, and one-way ANOVA. Steps for hypothesis testing and assessing normality are also outlined. Examples of transferring data from Excel and running analyses in SPSS are provided.
Governance and leadership can be used as tools for quality enhancement in healthcare. Effective governance includes engaging stakeholders, establishing shared objectives, and practicing prioritized decision-making. Leadership is key to improving outcomes through resource allocation and prioritizing initiatives. Quality management infrastructure is also important, with processes for quality planning, control, and continuous improvement. This includes identifying issues, analyzing problems, testing changes through PDSA cycles, and ensuring social accountability mechanisms for public feedback. Together, good governance, leadership, and quality management can enhance access to safe, effective, and equitable healthcare.
Palliative care aims to improve quality of life and reduce suffering for those with serious illnesses through early identification and treatment of pain and other distressing symptoms. It can be provided in hospitals, outpatient clinics, homes, and hospice centers using an interdisciplinary team approach. While palliative care and hospice care both focus on comfort, palliative care can be provided at any stage of illness and with curative treatment, whereas hospice care is for those with less than 6 months to live who are no longer pursuing curative options. Barriers to palliative care include lack of awareness, competency and funding as well as consumer fears and delays in diagnosis.
A case study involves an in-depth analysis of a single person, group, or event to explore the causes of underlying principles. It is well-suited to report unique cases or adverse drug reactions. Data collection typically includes interviews, observations, records reviews, and other documentation. Case studies can provide rich contextual details to add to the medical literature but lack rigor and cannot be generalized. They are useful for exploring questions of how, why, and what. Consent is typically required unless the subject is publicly known.
The document summarizes the new B.Sc. Nursing curriculum introduced by the Indian Nursing Council. It outlines 10 core competencies that students must master, including patient-centered care, professionalism, teaching and leadership, and evidence-based practice. It describes the curriculum's focus on competency-based learning through simulations and clinical experiences. Elective modules are introduced in the 3rd year and include topics like human values, diabetes care, and health economics. The curriculum uses continuous assessment, MCQs, and OSCE exams. Advantages of the new semester system include reduced stress compared to annual exams and better use of time.
This document provides an overview of modern concepts in childcare for a pediatric nursing course. It discusses internationally accepted children's rights, national policies and agencies related to child welfare in India, key national health programs, and changing trends in child health indicators. It also outlines child morbidity and mortality distribution, important dates related to child health, current trends in pediatric nursing, and ethics considerations in pediatric nursing care. The goal is for students to understand factors influencing child health and their role in promoting children's wellbeing.
At the end of unit 2, the students will be able to:
Appreciate the differences between children and adult
Describe the hospital environment for a sick child
Explain the impact of hospitalization on child
Discuss the grief and bereavement
Outline the role of a child health nurse
Explain the principles of pre- and post-operative care for children
Perform pain assessment in children
Nutritional assessment in children is important to identify those at risk of malnutrition early, identify malnourished children, and develop healthcare plans. Malnutrition can be undernutrition or obesity and is caused by factors like illiteracy, poor diet, low income, and infections. Nutritional assessment uses the ABCD method: anthropometric measurements like height, weight, BMI; biochemical parameters; clinical examination; and dietary surveys like 24-hour recall. Anthropometric measurements are key indicators and World Health Organization classifications are used to grade malnutrition severity. Treatment follows 10 steps over initial stabilization and rehabilitation phases.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
1. Data Analysis
Prof. (Dr.) Smriti Arora
Amity College of Nursing
Amity University Haryana
smritiamit@msn.com, sarora1@ggn.amity.edu
2. Introduction
• Process of converting raw data into meaningful information.
• Based on objectives
• Arranged according to objectives in Sections
• Descriptive and Inferential
• Specify the level of significance
• State null hypothesis (HO)
• Specify how analysis is done- manually or through some software-
SPSS/Strata
• Results must be stated as accepting or rejecting the null hypothesis
3. How to decide what test to use ?
• What type of analysis ? Univariate, Bivariate, multivariate
• How we have measured the variables? Categorical or continuous
• Data is normal or non normal ?
• How many categories of variables ? 2 or >2
• Whether the groups are related or not / within or between
comparison ?
4. Data Analysis
(For any data from any specialty / discipline)
• Uni-variate (one variable at a time)
• Bi-variate (two variables at a time)
• Multi-variate (more than two variables at a time)
May decide to perform one or all of the above depending
on the need
There is no other way of data analysis
6. Univariate Analysis
Categorical variable Quantitative variable
Measures of Central
Tendency
Measures of Locations Measures of Variation
Proportion or
percentages
Rate
Prevalence
Incidence
Mean
Median
Mode
Quartiles (Q1, Q2, Q3)
Deciles (D1, D2, ----, D5, --
--, D9)
Percentiles (P1, P2, ----,
P50, ----, P99)
Range
Quartile Deviation
Mean Deviation
Standard Deviation
Coefficient of Variation
7.
8. Normal distributions with larger
standard deviations are more “spread
out,” while normal distributions with
smaller standard deviations are more
“compact.”
10. • Percentiles are defined as the values that divide the whole series into
100 equal parts.
• So, there are 99 quartiles namely first percentile denoted by P1,
second percentile denoted by P2 …... and 99th percentile denoted by
P99.
• 50th percentile is Median.
• Since it denotes the position of the item in the series, it is a positional
average.
11. Levels of measurement
Nominal level Ordinal scale Interval scale Ratio scale
The nominal type
differentiates
between items or
subjects based only
on their names.
gender,
nationality,
ethnicity,
blood group
It allows for rank order
(1st, 2nd, 3rd, etc.) by
which data can be sorted
such as 'completely
agree', 'mostly agree',
'mostly disagree',
'completely disagree'
when measuring opinion.
Pain
Anxiety
Depression
stress
It allows for the degree of difference
between items, but not the ratio
between them.
Examples include temperature with
the Celsius scale, which has two
defined points (the freezing and
boiling point of water at specific
conditions) and then separated into
100 intervals.
Ratios are not allowed since 20 °C
cannot be said to be "twice as hot"
as 10 °C, nor can
multiplication/division be carried
out
It is the estimation of the ratio
between a magnitude of a
continuous quantity and a unit
magnitude of the same kind
A ratio scale possesses a
meaningful (unique and non-
arbitrary) zero value.
height, weight, KAP scores, BMI.
Ratios are allowed because
having a non-arbitrary zero point
makes it meaningful to say, for
example, that one object has
"twice the length" of another.
14. Assessing normality
• Analyze, Descriptive statistics, Explore
• Plots , normality tests, Histogram
• Kolmogorov Smirnoff test, Shapiro wilk
test
• P value should be above 0.05
• https://www.youtube.com/watch?v=2
GRZ_d4ftoo
15. Inferential Statistics
Parametric tests Nonparametric tests
• Used when data is- normal
• Includes comparisons of means
• Used for continuous data
• Probability sampling
• Parametric tests usually have more
statistical power than
nonparametric tests.
• T test, ANOVA, regression analysis,
Correlation Coefficient
• Non normal
• Comparison of Medians
• Non probability sampling
• Used for categorical
(nominal, ordinal or discrete)
data
• Kruskal Wallis, Mc, Nemar,
Man Whitney U, Friedman
test
16. Hypothesis testing procedure
• State null hypothesis
• Determine level of significance: 0.05 or 0.01
• Select the test statistic
• Compute the test statistic
• Calculate degrees of freedom
• Compare test value with tabled value.
17.
18.
19.
20. Constructing Bivariate Tables
• DV goes in the rows, IV goes in the columns
Music therapy
given
Music therapy not
given
Pain present
Pain absent
21.
22.
23.
24.
25.
26.
27.
28. • Type 1 error: rejecting a null hypothesis when it is true, false positive
• Type 2 error: accepting a null hypothesis when it is false , false negative