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This test (as a 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 possesses the additive property as has already been explained.
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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.
Intro to tests of significance qualitative
This document discusses tests of significance and summarizes key concepts. It begins by describing qualitative and quantitative data and measures of central tendency. It then discusses sampling variation, the null hypothesis, p-values, and the standard error. The document outlines the steps in hypothesis testing and describes different types of tests including the standard error of difference between two proportions (SEDP) test and the chi-square test. Examples are provided to demonstrate how to calculate test statistics and determine significance. The limitations of the SEDP test are also noted.
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Amrita Kumari from Banaras Hindu University submitted an application discussing parametric tests. Parametric tests were developed by R. Fisher and make assumptions about the population distribution from which a sample is drawn. The key assumptions are that the population is normally distributed, observations are independent, populations have equal variance, and data is on a ratio or interval scale. Parametric tests can be used even when distributions are skewed or variances differ, and they have more statistical power than non-parametric tests. Common parametric tests include t-tests, z-tests, and ANOVA. The document then discusses one-sample, dependent, and independent t-tests in more detail. Both advantages like precision and disadvantages like sensitivity
scope and need of biostatics
This powerpoint presentation gives a brief explanation about the biostatic data .this is quite helpful to individuals to understand the basic research methodology terminologys
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
Research method ch08 statistical methods 2 anova
1) The document discusses various statistical methods including one-way ANOVA, repeated measures ANOVA, and ANCOVA.
2) One-way ANOVA is used to compare the means of three or more independent groups when you have one independent variable with three or more categories and one continuous dependent variable.
3) Repeated measures ANOVA is used when the same subjects are measured under different conditions to assess for main effects and interactions while accounting for the dependency of measurements within subjects.
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The transportation problem is a special type of linear programming problem where the objective is to minimize the cost of distributing a product from a number of sources or origins to a number of destinations.
Because of its special structure, the usual simplex method is not suitable for solving transportation problems. These problems require a special method of solution.
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Reward is an incentive plan to reinforce the desirable behavior of workers or employers and in return for their service to the organization.
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Busniess stats
- 1. CHI SQUARE TEST AND ITS TYPES
- 2. INTRODUCTION • Chi square test, also called Pearson’s chi square test. • First investigated by KARL PEARSON in 1990
- 3. APPLICATION OF CHI SQUARE TEST • Goodness of fit- whether an observed frequency distribution differs from a theoretical distribution. • Homogeneity- compares the distribution of counts for two or more groups using the same categorical variable • Independence (most commonly used)- assesses whether unpaired observation on two variables are independent of each other
- 4. IMPORTANT CHARACTERISTIC OF A CHI SQUARE TEST • This test (as a 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 possesses the additive property as has already been explained. • This test can also be applied to a complex contingency table with several classes and as such is a very useful test in research work. • This test is an important non-parametric test as no rigid assumptions are necessary in regard to the type of population, no need of parameter values and relatively less mathematical details are involved
- 5. APPLICATIONS OF CHI SQUARE TESTING This test can be used in • Goodness of Fit of distribution • Test of independence of attributes • Test of homogeneity
- 6. GOODNESS OF FIT OF DISTRIBUTION This test enables us to see how well does the assumed theorotical distribution (such as binomial distribution Poisson distribution or normal distribution)fit to the observed data The formula for the chi-square statistic used in the chi square test is: Where O=observed frequency E=expected frequency
- 7. GOODNESS OF FIT OF DISTRIBUTION If x2 (calculated)>(tabulated),with (n-1) then null hypothesis is rejected otherwise accepted And if null hypothesis is accepted then it can be concluded that the given distribution follows theoretical distribution.
- 8. TEST OF INDEPENDENCE OF ATTRIBUTES Test enables us to explain whether or not two attributes are associated for instance we may be interested in knowing whether a new medicine is effective controlling fever or not, x2 is useful. in such a situation we proceed with the null hypothesis that the two attributes (medicine and control of fever) are independent which means medicine is not effective.
- 9. TEST OF INDEPENDENCE OF ATTRIBUTES X2 (calculated)> x2 (tabulated) at a certain level of significance for given degrees of freedom, the null hypothesis is rejected, two variables are dependent (the new medicine is effective in controlling the fever)And if , x2 (calculated)<x2 (tabulated) the null hypothesis is accepted 2 variables are independent (so the new medicine is not effective in controlling the fever)
- 10. TEST OF HOMOGENITY This test can also be used to test whether the occurrence of events follow uniformity or not e.g. the admission of patients in government hospital in all days of week is uniform or not can be tested with the help of chi square test.
- 11. WE COLLECT DATA BY FLIPPING THE COIN 200 TIMES. THE COIN LANDED HEADS-UP 108 TIMES AND TAILS-UP 92 TIMES. AT FIRST GLANCE, WE MIGHT SUSPECT THAT THE COIN IS BIASED BECAUSE HEADS RESULTED MORE OFTEN THAN THAN TAILS.
- 13. Heads Tails Total Observed 108 92 200 Expected 100 100 200 Total 208 192 400
- 14. df/pro b. 0.99 0.95 0.90 0.80 0.70 0.50 0.30 0.20 0.10 0.05 1 0.0001 3 0.0039 0.016 0.64 0.15 0.46 1.07 1.64 2.71 3.84 2 0.02 0.10 0.21 0.45 0.71 1.39 2.41 3.22 4.60 5.99 3 0.12 0.35 0.58 1.00 1.42 2.37 3.66 4.64 6.25 7.82 4 0.3 0.71 1.06 1.65 2.20 3.36 4.88 5.99 7.78 9.49 5 0.55 1.14 1.61 2.34 3.00 4.35 6.06 7.29 9.24 11.07