TEST of hypothesis
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This document discusses significance testing and hypothesis testing procedures, including: 1) The objectives of hypothesis testing are to test claims about population parameters by stating hypotheses (H0 and Ha), calculating test statistics, determining p-values, and considering significance levels. 2) Examples of one-sample and two-sample hypothesis tests are provided, including a test of whether a population gained weight on average and a test comparing the means of two samples. 3) Statistical tests discussed include the t-test (for comparing two means), z-test (when population standard deviation is unknown), and chi-square test (for testing goodness of fit to an expected distribution). Formulas and procedures for conducting these tests are outlined.
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The Chi-Square Test for Independence is used to determine if two variables classified in a contingency table are related or independent. It requires a random sample, population size at least 10 times the sample, qualitative variables, and expected frequency in each cell of at least 5. The expected frequency is calculated as the row total multiplied by the column total divided by the total sample size. The degree of freedom is the number of rows minus one multiplied by the number of columns minus one. The null hypothesis is that the two classifications are independent.
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2) Select a significance level, typically 5% or 1%, which affects factors like sample size and variability.
3) Decide on a distribution, usually normal or t, to determine critical values.
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This document discusses the chi-square test, a statistical hypothesis test where the test statistic follows a chi-squared distribution if the null hypothesis is true. It was developed by Karl Pearson in 1900. The chi-square test can be used to examine differences between observed and expected results in a random pattern. It is applicable to testing independence between attributes and goodness of fit to theoretical distributions. Common mistakes in applying the chi-square test include small theoretical frequencies, neglecting non-occurrences, and incorrect determination of degrees of freedom. The chi-square test is used to test significance of sample variance, independence in contingency tables, comparisons of frequency distributions, and goodness of fit.
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The chi-square test can be used to determine if there is a relationship between two categorical variables. It works by comparing observed and expected frequencies if the variables were independent. The chi-square test statistic is calculated by summing the squared differences between observed and expected frequencies divided by the expected frequency for each category. The test statistic value is then compared to a critical value from the chi-square distribution based on the degrees of freedom. A larger test statistic value provides evidence to reject the null hypothesis of independence between the variables. The document provides an example comparing students' living situations and nightly activities, calculating expected and observed frequencies, the chi-square test statistic, and conducting the hypothesis test.
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This document provides an overview of chi square tests as a type of non-parametric statistic. It explains that chi square tests are used to analyze nominal or categorical data by comparing observed frequencies to expected frequencies if the null hypothesis is true. An example is provided to demonstrate how to calculate chi square values from a contingency table and determine significance based on degrees of freedom and p-values. The document also outlines other types of non-parametric tests that can be used depending on whether the data is nominal, ordinal, independent, or matched samples.
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The survey found formal risk management processes are most prevalent at large funds. Half of the medium and small funds have adopted a risk management process or are doing so in 2013. Forty-two percent of respondents employ proprietary and/or third-party risk measurement tools, such as software or data services. Usage of third-party tools is most prevalent at public funds, while endowments and foundations more often use in-house (proprietary) tools.
Corporate and public funds are embracing policy-level approaches to risk management more so than endowments and foundations. Public funds have implemented economic regime asset allocations, risk parity, and risk factor-based asset allocations, while corporate funds favor liability-driven investing and funded status-based glide path de-risking.
Strategy-level approaches to mitigate risk are easier to implement than those that alter the fund’s overall investment policy, and Callan observed higher levels of adoption of strategy changes across fund types. Public funds and foundations and endowments are most heavily implementing or considering real assets, opportunistic fixed income, absolute return and long/short equity. Corporate funds are also embracing absolute return, but long duration is the most favored strategy-level approach used to address risk.
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The survey was conducted in November 2012 and includes responses from 53 fund sponsors representing $576 billion in assets.
Procedure For Hypothesis Testing
The document outlines the steps for hypothesis testing:
1) Make a formal statement of the null hypothesis (H0) and alternative hypothesis (Ha). This clearly defines what is being tested.
2) Select a significance level, typically 5% or 1%, which affects factors like sample size and variability.
3) Decide on a distribution, usually normal or t, to determine critical values.
4) Select a random sample, compute a test statistic, and determine the probability of observing a value at least as extreme as the sample statistic if the null hypothesis is true.
5) Compare the probability to the significance level. If it is equal to or below, reject the null hypothesis in favor
The hypothesis process 2
The document explains the hypothesis testing process used to determine if a weight loss program's claim can be supported. It involves:
1) Defining the null and alternative hypotheses based on the claim and sample data, with the null being that average weight loss is not less than 10 pounds.
2) Calculating a z-score for the sample mean and finding the critical value from the z-table based on a 95% confidence level.
3) Comparing the z-score to the critical value and rejecting the null hypothesis if the z-score is lower, meaning the sample provides evidence against the claim.
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Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
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This document discusses key concepts related to testing hypotheses. It defines a hypothesis as a statement that can be tested scientifically to determine if it is true. The null hypothesis states that there is no effect or relationship, while the alternative hypothesis specifies what the test is designed to detect. Type I and Type II errors occur when the null hypothesis is incorrectly rejected or accepted. The level of significance refers to the probability of a Type I error. One-sided and two-sided tests determine whether the critical values are in one or both tails of the probability distribution. Finally, a decision rule establishes the criteria for rejecting or failing to reject the null hypothesis based on the sample results.
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This document provides an introduction to hypothesis testing including:
1. The 5 steps in a hypothesis test: set up null and alternative hypotheses, define test procedure, collect data, decide whether to reject null hypothesis, interpret results.
2. Large sample tests for the mean involve testing if the population mean is equal to or not equal to a specified value using a test statistic that follows a normal distribution.
3. Type I and Type II errors occur when the decision made based on the hypothesis test does not match the actual truth - a Type I error rejects the null hypothesis when it is true, a Type II error fails to reject the null when it is false. The probability of each error can be minimized by choosing
Test of hypothesis
The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
- A hypothesis aims to explain a phenomenon and can be tested objectively. Common hypotheses compare two groups or variables.
- Statistical hypothesis testing involves a null hypothesis (H0) and alternative hypothesis (Ha). H0 is the initial assumption being tested, while Ha is what would be accepted if H0 is rejected.
- Type I errors incorrectly reject a true null hypothesis. Type II errors fail to reject a false null hypothesis. Hypothesis tests aim to control the probability of type I errors.
- The significance level is the probability of a type I error,
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Day 3 SPSS
Hypothesis testing is a statistical process used to evaluate claims about populations based on sample data. It involves stating a null hypothesis, collecting sample data, selecting a statistical test, and determining whether to reject or fail to reject the null hypothesis. This document provides an overview of common hypothesis tests in SPSS including one-sample t-tests, two-sample t-tests, paired t-tests, ANOVA, and chi-square tests of association. Examples are given using the hsb2.sav dataset to demonstrate how to conduct each test in SPSS and interpret the results.
TEST OF SIGNIFICANCE.pptx
This document discusses hypothesis testing and significance tests. It defines key terms like parameters, statistics, sampling distribution, standard error, null and alternative hypotheses, type I and type II errors. It explains how to set up a hypothesis test, including choosing a significance level and critical value. Both one-tailed and two-tailed tests are described. Finally, it provides an overview of different types of significance tests for both large and small sample sizes.
Parametric test
This document provides an overview of sampling and hypothesis testing in MATLAB. It discusses simple random sampling, random sampling functions in MATLAB, and the basics of hypothesis testing. It then describes various parametric tests that can be performed in MATLAB, including one sample t-test, paired t-test, two sample t-test, z-test, and F-test. Examples of how to perform each test in MATLAB are also provided.
hypothesis test
This document provides an overview of estimation and hypothesis testing. It defines key statistical concepts like population and sample, parameters and estimates, and introduces the two main methods in inferential statistics - estimation and hypothesis testing.
It explains that hypothesis testing involves setting a null hypothesis (H0) and an alternative hypothesis (Ha), calculating a test statistic, determining a p-value, and making a decision to accept or reject the null hypothesis based on the p-value and significance level. The four main steps of hypothesis testing are outlined as setting hypotheses, calculating a test statistic, determining the p-value, and making a conclusion.
Examples are provided to demonstrate left-tailed, right-tailed, and two-tailed hypothesis tests
Nonparametric and Distribution- Free Statistics
This document summarizes several nonparametric statistical tests that do not rely on assumptions about the population distribution, including the sign test, Wilcoxon signed-rank test, median test, Mann-Whitney test, Kolmogorov-Smirnov goodness-of-fit test, and Kruskal-Wallis one-way analysis of variance. It provides details on the theoretical background, test statistics, assumptions, and procedures for the sign test for single samples and paired data. An example application of the paired sign test to a dental study is shown.
Thesigntest
The document discusses the sign test, a nonparametric hypothesis test that does not require assumptions about the population distribution. The sign test can be used to test claims involving matched pairs, nominal data with two categories, or the population median. The document provides guidelines for performing the sign test in each of these cases, including stating hypotheses, determining sample sizes and test statistics, and making conclusions. Examples are also given to illustrate the sign test for matched pairs, nominal data, and testing the population median.
Statistical tests of significance and Student`s T-Test
Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained
Nonparametric and Distribution- Free Statistics _contd
This document summarizes the Wilcoxon signed-rank test, a nonparametric test used to test hypotheses about the median or location of a population when the assumptions of the t-test are not met. It provides an example applying the test to data on cardiac output measurements from 15 patients. The test calculates the differences between each observation and the hypothesized median, ranks the absolute values of the differences, and sums the ranks with positive and negative signs. The smaller of the two sums is the test statistic, which is compared to a critical value to determine if the null hypothesis that the population median equals the hypothesized value can be rejected.
More Statistics
The document discusses various statistical concepts related to hypothesis testing, including:
- Types I and II errors that can occur when testing hypotheses
- How the probability of committing errors depends on factors like the sample size and how far the population parameter is from the hypothesized value
- The concept of critical regions and how they are used to determine if a null hypothesis can be rejected
- The difference between discrete and continuous probability distributions and examples of each
- How an observed test statistic is calculated and compared to a critical value to determine whether to reject or not reject the null hypothesis
HYPOTHESIS TESTING.ppt
This document provides an introduction to hypothesis testing. It discusses key concepts such as the null and alternative hypotheses, types of errors, levels of significance, test statistics, p-values, and decision rules. Examples are provided to demonstrate how to state hypotheses, identify the type of test, find critical values and rejection regions, calculate test statistics and p-values, and make decisions to reject or fail to reject the null hypothesis based on these concepts. The steps outlined include stating the hypotheses, specifying the significance level, determining the test statistic and sampling distribution, finding the p-value or using rejection regions to make a decision, and interpreting what the decision means for the original claim.
BIOSTATISTICS T TEST Z TEST F TEST HYPOTHESIS TYPES OF ERROR.pptx
HYPOTHESIS TESTING
T TEST
Z TEST
TYPES OF ERROR
NORMAL DISTRIBUTION
NUMERICALS OF BIOSTATS
F TEST
NORMAL DISTRIBUTION
A Bayesian Alternative To Null Hypothesis Significance Testing
This document provides an overview of Bayesian statistics as an alternative to null hypothesis significance testing (NHST) which is commonly used in research. It outlines some key criticisms of NHST, such as its overemphasis on arbitrary significance thresholds and issues with statistical power. The document then introduces some key concepts in Bayesian statistics, such as prior probabilities and Bayes factors. It provides a hypothetical example experiment comparing the comprehension of interesting vs boring texts to illustrate applications of NHST and how Bayesian statistics could be used as an alternative.
Chapter 6 Ranksumtest
The document discusses nonparametric tests that can be used when the data distribution is unknown or non-normal. It provides examples of the Wilcoxon signed-rank test to compare two related samples, the Wilcoxon rank-sum test to compare two independent samples, the Kruskal-Wallis H test to compare more than two independent samples, and the Friedman test to compare blocks of data. Multiple comparison tests are also discussed to determine the specific groups that differ when overall differences are found.
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.
Hmisiri nonparametrics book
This document provides an overview of nonparametric statistical methods for analyzing ranked data. It discusses the Wilcoxon rank-sum test and sign test, which are nonparametric alternatives to the t-test that do not assume a normal distribution. The document explains how to rank data values and handle ties. It also provides examples of using the sign test to compare a sample mean to a hypothesized value and interpreting the results.
SNM-1.pdf
This document provides information about statistics and hypothesis testing concepts. It defines key terms like population, sample, parameters, statistics, standard error, random sampling, critical region, acceptance region, one-tailed and two-tailed tests, null and alternative hypotheses, type I and type II errors. It also describes common statistical tests like t-test, F-test, chi-square test and provides their assumptions and uses. Several examples of hypothesis testing problems and their solutions are given to illustrate statistical concepts and procedures.
hypothesisTestPPT.pptx
The document provides information about hypothesis testing and the steps involved. It discusses:
1. Formulating the null and alternative hypotheses, specifying the significance level, and choosing the appropriate test statistic.
2. Examples of hypothesis tests involving means, including the z-test for large samples when the population variance is known, and the t-test for small samples when the population variance is unknown.
3. The calculations and decisions involved in conducting hypothesis tests, such as computing the test statistic, comparing it to the critical value, and determining whether to reject or fail to reject the null hypothesis.
Hypothesis
In this document, I have tried to illustrate most of the hypothesis testing like 1 sample,2 samples, etc, which I have covered to analyze the machine learning algorithms. I have focused on Independent statistical testing.
Now the question is why we use statistical testing? the answer is that we use statistical testing for significance analysis of our results, which I am going to deliver
Chi-Square test.pptx
The document discusses the Chi-Square test. It begins by explaining what the Chi-Square test is and how it was developed. It then provides the formula for computing Chi-Square and explains how the test can be used to determine if there is a significant difference between observed and expected results. Some key applications of the Chi-Square test discussed include testing hypotheses about variance, testing independence of attributes, and testing goodness of fit. Examples are provided to illustrate how to perform Chi-Square tests for different situations.
Testing of Hypothesis, p-value, Gaussian distribution, null hypothesis
This document provides an overview of key concepts in statistical hypothesis testing. It defines what a hypothesis is, the different types of hypotheses (null, alternative, one-tailed, two-tailed), and statistical terms used in hypothesis testing like test statistics, critical regions, significance levels, critical values, type I and type II errors. It also explains the decision making process in hypothesis testing, such as rejecting or failing to reject the null hypothesis based on whether the test statistic falls within the critical region or if the p-value is less than the significance level.
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Statistical tests of significance and Student`s T-Test
Statistical tests of significance and Student`s T-Test
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Nonparametric and Distribution- Free Statistics _contd
BIOSTATISTICS T TEST Z TEST F TEST HYPOTHESIS TYPES OF ERROR.pptx
BIOSTATISTICS T TEST Z TEST F TEST HYPOTHESIS TYPES OF ERROR.pptx
A Bayesian Alternative To Null Hypothesis Significance Testing
A Bayesian Alternative To Null Hypothesis Significance Testing
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This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
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#Prerequisites:
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- Steps:
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- Exploiting IAM PassRole Misconfiguration
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- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
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- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微bwp0011
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4:这个认证书并且可以归档倒地方
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TEST of hypothesis
- 1. SUBJECT: PROBABLITY AND INTRODUCTION TO STATISTICS (CODE-2142505) BE Second Level Second Semester (Self Finance) TOPIC: Test of Hypotheses Prepared By: GUJARAT TECHNOLOGY UNIVERSITY BIRLA VISHVAKARMA MAHAVIDYALAYA (ENGINEERING COLLEGE) VALLABH VIDYANAGAR Name Enrollment No Krushal Kakadiya 130080125006 Dhruvit Kardani 130080125007 Tirth Lad 130080125008 Milan Lakhani 130080125009 Vishal Lapsiwala 130080125010 Guided By: Prof. A.H.Jariya
- 2. Significance Testing Also called “hypothesis testing” Objective: to test a claim about parameter μ Procedure: A. State hypotheses H0 and Ha B. Calculate test statistic C. Convert test statistic to P-value and interpret D. Consider significance level (optional)
- 3. Hypotheses H0 (null hypothesis) claims “no difference” Ha (alternative hypothesis) contradicts the null Example: We test whether a population gained weight on average… H0: no average weight gain in population Ha: H0 is wrong (i.e., “weight gain”) Next collect data quantify the extent to which the data provides evidence against H0
- 4. One-Sample Test of Mean To test a single mean, the null hypothesis is H0: μ = μ0, where μ0 represents the “null value” (null value comes from the research question, not from data!) The alternative hypothesis can take these forms: Ha: μ > μ0 (one-sided to right) or Ha: μ < μ0 (one-side to left) or Ha: μ ≠ μ0 (two-sided) For the weight gain illustrative example: H0: μ = 0 Ha: μ > 0 (one-sided) or Ha: μ ≠ μ0 (two-sided) Note: μ0 = 0 in this example
- 5. Illustrative Example: Weight Gain Let X ≡ weight gain X ~N(μ, σ = 1), the value of μ unknown Under H0, μ = 0 Take SRS of n = 10 σx-bar = 1 / √(10) = 0.316 Thus, under H0 x-bar~N(0, 0.316) . Figure: Two possible xbars when H0 true
- 6. T test for two samples What is the probability that two samples have the same mean? Sample A Sample B 1 1 3 2 5 5 5 4 7 8 9 9 10 10 Sample Mean 5.714286 5.571429
- 7. The T test Analysis Go to the Data tab Click on data analysis Select t-Test for Two-Sample(s) with Equal Variance
- 8. With Our Data and .05 Confidence Level t stat = 0.08 t critical for two- tail (H1 = not equal) = 2.18. T stat < t Critical, so do not reject the null hypothesis of equal means. Also, α is 0.94, which is far larger than .05
- 9. T Test: Two-Sample, Equal Variance If the variances of the two samples are believed to be the same, use this option. It is the strongest t test—most likely to reject the null hypothesis of equality if the means really are different.
- 10. T Test: Two-Sample, Unequal Variance Does not require equal variances Use if you know they are unequal Use is you do not feel that you should assume equality You lose some discriminatory power Slightly less likely to reject the null hypothesis of equality if it is true
- 11. T Test: Two-Sample, Paired In the sampling, the each value in one distribution is paired with a value in the other distribution on some basis. For example, equal ability on some skill.
- 12. z Test for Two Sample Means Population standard deviation is unknown. Must compute the sample variances.
- 13. z test Data tab Data analysis z test sample for two means. 13 Z value is greater than z Critical for two tails (not equal), so reject the null hypothesis of the means being equal. Also, α = 2.31109E-08 < .05, so reject.
- 14. Chi-Square Test A fundamental problem is genetics is determining whether the experimentally determined data fits the results expected from theory.
- 15. Goodness of Fit Mendel has no way of solving this problem. Shortly after the rediscovery of his work in 1900, Karl Pearson and R.A. Fisher developed the “chi-square” test for this purpose. The chi-square test is a “goodness of fit” test: it answers the question of how well do experimental data fit expectations. We start with a theory for how the offspring will be distributed: the “null hypothesis”. We will discuss the offspring of a self-pollination of a heterozygote. The null hypothesis is that the offspring will appear in a ratio of 3/4 dominant to 1/4 recessive.
- 16. Formula The “Χ” is the Greek letter chi; the “∑” is a sigma; it means to sum the following terms for all phenotypes. “obs” is the number of individuals of the given phenotype observed; “exp” is the number of that phenotype expected from the null hypothesis. Note that you must use the number of individuals, the counts, and NOT proportions, ratios, or frequencies. exp exp)( 2 2 obs
Editor's Notes
- Monday, June 29, 2015