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The t-test and z-test are statistical tests used for hypothesis testing. The t-test is used when sample sizes are small (n<=30) and the samples are drawn from a normal population with unknown standard deviation. The z-test is used when sample sizes are large (n>=30) or when the standard deviation is known, regardless of sample size. Seven situations are described that outline when to use the t-test or z-test based on characteristics of the sample and population.

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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

Tests of significance Periodontology

tests of significance in periodontics aspect, tests of significance with common examples, tests in brief, null hypothesis, parametric vs non parametric tests, seminar by sai lakshmi

Sampling and its Types

This document discusses different types of sampling methods used in statistics. It defines sampling as selecting observations from a population to describe and make inferences about the population. There are two main types of sampling: probability sampling, where units have a known chance of being selected, and non-probability sampling, where chance of selection is unknown. Probability sampling methods include simple random sampling, stratified random sampling, cluster sampling, systematic random sampling, and multistage sampling. Non-probability sampling methods include convenience sampling, quota sampling, judgmental sampling, snowball sampling, and self-selection sampling.

Sampling methods

This document discusses different sampling methods used in research. It defines a sample as a unit selected from a population to represent the whole population. Sampling methods are classified as either random or non-random. Random sampling methods give every unit an equal chance of being selected, while non-random methods use criteria to select units. Specific random methods described include simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Non-random methods include judgment sampling, convenience sampling, and snowball sampling. The key is selecting a method that allows the sample to accurately reflect the true characteristics of the population.

Sampling Techniques

Sampling Techniques for population and Material
1) Simple Random Sampling
2) Stratified Sampling
3) Systematic Sampling
4) Cluster Sampling
5) Two stage sampling.

Sampling Distribution

This document discusses sampling distribution about sample mean. It defines key terms like population, sample, sampling units, stratified random sampling, systematic sampling, cluster sampling, probability sampling, non-probability sampling, estimation, estimator, estimate, and sampling distribution. It also discusses the sampling distribution of the sample mean and provides an example to calculate and compare the mean and variance of sample means for sampling with and without replacement.

Sampling methods

This document discusses various sampling methods used in research studies. It begins with defining key terms like population, sampling, target population and sampling frame. It then describes the main types of sampling methods - probability sampling methods like simple random sampling, stratified random sampling and cluster sampling as well as non-probability sampling methods like convenience sampling and snowball sampling. The advantages and limitations of different sampling methods are provided. The document emphasizes that probability sampling allows generalization of results to the target population while non-probability sampling does not. It concludes by noting some sources of error in sampling.

Introduction to basic concept in sampling and sampling techniques

This document provides an overview of sampling techniques for teaching basic statistics. It defines key terms like universe, population, sample, and parameter. It also describes different sampling methods like simple random sampling, stratified random sampling, systematic random sampling, and cluster sampling. The goals of sampling are discussed as reducing costs, increasing efficiency and accuracy compared to a full census. Probability and non-probability samples are also distinguished, with an emphasis on using probability sampling to allow for statistical inference about populations.

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

Tests of significance Periodontology

tests of significance in periodontics aspect, tests of significance with common examples, tests in brief, null hypothesis, parametric vs non parametric tests, seminar by sai lakshmi

Sampling and its Types

This document discusses different types of sampling methods used in statistics. It defines sampling as selecting observations from a population to describe and make inferences about the population. There are two main types of sampling: probability sampling, where units have a known chance of being selected, and non-probability sampling, where chance of selection is unknown. Probability sampling methods include simple random sampling, stratified random sampling, cluster sampling, systematic random sampling, and multistage sampling. Non-probability sampling methods include convenience sampling, quota sampling, judgmental sampling, snowball sampling, and self-selection sampling.

Sampling methods

This document discusses different sampling methods used in research. It defines a sample as a unit selected from a population to represent the whole population. Sampling methods are classified as either random or non-random. Random sampling methods give every unit an equal chance of being selected, while non-random methods use criteria to select units. Specific random methods described include simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Non-random methods include judgment sampling, convenience sampling, and snowball sampling. The key is selecting a method that allows the sample to accurately reflect the true characteristics of the population.

Sampling Techniques

Sampling Techniques for population and Material
1) Simple Random Sampling
2) Stratified Sampling
3) Systematic Sampling
4) Cluster Sampling
5) Two stage sampling.

Sampling Distribution

This document discusses sampling distribution about sample mean. It defines key terms like population, sample, sampling units, stratified random sampling, systematic sampling, cluster sampling, probability sampling, non-probability sampling, estimation, estimator, estimate, and sampling distribution. It also discusses the sampling distribution of the sample mean and provides an example to calculate and compare the mean and variance of sample means for sampling with and without replacement.

Sampling methods

This document discusses various sampling methods used in research studies. It begins with defining key terms like population, sampling, target population and sampling frame. It then describes the main types of sampling methods - probability sampling methods like simple random sampling, stratified random sampling and cluster sampling as well as non-probability sampling methods like convenience sampling and snowball sampling. The advantages and limitations of different sampling methods are provided. The document emphasizes that probability sampling allows generalization of results to the target population while non-probability sampling does not. It concludes by noting some sources of error in sampling.

Introduction to basic concept in sampling and sampling techniques

This document provides an overview of sampling techniques for teaching basic statistics. It defines key terms like universe, population, sample, and parameter. It also describes different sampling methods like simple random sampling, stratified random sampling, systematic random sampling, and cluster sampling. The goals of sampling are discussed as reducing costs, increasing efficiency and accuracy compared to a full census. Probability and non-probability samples are also distinguished, with an emphasis on using probability sampling to allow for statistical inference about populations.

Sampling Methods & Sampling Error PPT - For Seminar

This document discusses various sampling methods used in research including probability sampling techniques like simple random sampling, cluster sampling, systematic sampling, and stratified random sampling. It also covers non-probability sampling methods such as convenience sampling, judgmental sampling, quota sampling, and snowball sampling. The document explains how each method works with examples and concludes by defining sampling error and non-sampling error that can occur in research.

L4 theory of sampling

1. Sampling is the process of selecting a subset of items from a population to gather information about the entire population. It involves selecting a sample using probability or non-probability methods.
2. Probability sampling methods like simple random sampling, systematic sampling, and stratified sampling ensure each item has a known, non-zero chance of being selected. Non-probability methods like convenience sampling and purposive sampling rely on researcher judgment.
3. The central limit theorem states that as sample size increases, the sample mean will approach a normal distribution, allowing inferences about the population mean from a sample. Sampling error is reduced with larger sample sizes.

Sampling methods

This document discusses different sampling methods used in research. It defines population and sample, and explains that sampling is used to select a subset of a population when the entire population is too large. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling uses random selection and allows results to be generalized to the population, while non-probability sampling relies on the researcher's judgment and results cannot be generalized. Specific probability sampling methods described include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. Non-probability sampling methods mentioned are convenience sampling, snowball sampling, quota sampling, and judgmental sampling.

Sampling Theory Part 1

FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
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Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
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Sample method

Meaning of Research
Components of research
Meaning of Sampling
Sample breakdown
influencing factors
Types of Sample
Process of Samples

T11 types of tests

This document discusses parametric and nonparametric statistics, when each should be used, and their relative power. Parametric tests make assumptions about the population that may not always be justified, while nonparametric tests do not rely on these assumptions. Some key points:
1. Parametric tests assume normal distributions and equal variances, while nonparametric tests do not require these assumptions and can be used on small sample sizes or ordinal data.
2. There are nonparametric equivalents to most common parametric tests for comparing groups (independent or dependent samples) or relationships between variables.
3. Advantages of nonparametric tests are they provide exact probabilities regardless of the population distribution and can

Parametric tests

Parametric tests are used to analyze normally distributed numerical data and include Students' t-test and analysis of variance (ANOVA). The Students' t-test compares the means of two groups to determine if they are statistically different. ANOVA compares the means of two or more groups and is more efficient than multiple t-tests. It can be used for one-way, multifactor, or repeated measures designs. Both tests make assumptions about the data distribution and variance between groups.

Sampling, Types of Techniques & Simple Radom sampling

This document discusses simple random sampling. It begins by defining sampling as selecting a subset of units from a population to make inferences about the population. It then explains that simple random sampling (SRS) gives each unit of the population an equal chance of being selected. SRS is an easy method to conduct where numbers are assigned to population units and a random number generator is used to select samples. The key advantages of SRS are that it is easy to conduct and analyze and provides a representative sample.

Types of data sampling

Types of data sampling,probability sampling and non-probability sampling,Simple random sampling,Systematic sampling,Stratified sampling,Clustered sampling,Convenience sampling,Quota sampling,Judgement (or Purposive) Sampling,Snowball sampling,Bias in sampling.

Significance Tests

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

Standard error

Satyaki Aparajit Mishra presented on the topic of standard error and predictability limits. Standard error is used to estimate the standard deviation from a sample. It is calculated by dividing the standard deviation by the square root of the sample size. A larger standard error means the sample mean is less reliable at estimating the population mean. Standard error helps determine how far sample estimates may be from the true population values. Mishra discussed estimating standard error from a single sample and how standard error is used to test hypotheses. He provided an example of testing if a coin flip was unbiased using the standard error of the proportion of heads observed.

Hypothesis testing and parametric test

In the presentation, hypothesis test has been explained with scrap. Tree diagram is there to understand in which situation u can apply which parametric test

Tqm sampling

This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to collecting representative samples and how to select the appropriate sampling method for their research needs.

Sampling and sampling distribution tttt

This document discusses sampling and sampling distributions. It defines sampling as selecting a subset of a population for study. Random sampling gives each member of the population an equal chance of being selected, while non-random sampling uses other factors like convenience. The document outlines the steps in sampling design and describes different random and non-random sampling methods. It also distinguishes between sampling errors and non-sampling errors and discusses the concept of sampling distributions and how they allow inferences to be made about population parameters from sample statistics.

Topic 7 stat inference

The document discusses statistical hypothesis testing. It defines key terms like the null hypothesis, alternative hypothesis, test statistic, rejection region, Type I and Type II errors, significance level, and p-value. It also describes the steps to conduct a hypothesis test including stating the hypotheses, choosing a test statistic, determining critical values, and interpreting the conclusions. Specific hypothesis tests for a population mean are also covered, including tests when the population variance is known versus unknown.

NON-PARAMETRIC TESTS by Prajakta Sawant

This document provides an overview of non-parametric tests presented by Ms. Prajakta Sawant. It discusses non-parametric tests as distribution-free statistical tests that do not require assumptions about the underlying population distribution. Common non-parametric tests described include the Wilcoxon rank-sum test, Kruskal-Wallis test, Spearman's rank correlation coefficient, and the chi-square test. Examples are provided for each test to illustrate their application and interpretation.

Sampling and sampling techniques PPT

This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.

t-test vs ANOVA

T-test and ANOVA are statistical techniques used to test hypotheses and compare population means. The t-test is used to compare the means of two samples or groups, while ANOVA can compare the means of more than two groups. Specifically, the t-test examines whether two sample means are significantly different and assumes a normal distribution and unknown standard deviation. ANOVA compares three or more population means by assessing variation within and between groups, and assumes samples are from normally distributed populations with equal variances. Researchers should use a t-test when comparing only two means and ANOVA when comparing more than two means to avoid increasing the chances of a Type I error.

Complex random sampling designs

This document discusses various complex random sampling designs, including systematic sampling, stratified sampling, cluster sampling, multi-stage sampling, sampling with probability proportional to size, and sequential sampling. It provides details on how each design is implemented and their relative advantages and disadvantages. Complex random sampling designs combine elements of probability and non-probability sampling to select samples.

Sec 1.3 collecting sample data

Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 1: Introduction to Statistics
Section 1.3: Collecting Sample Data

Fuzzy Logic

1. The document proposes a fuzzy logic approach for evaluating loan applications in banks. It considers various factors like age, income, repayment history, account balance, etc. as fuzzy variables.
2. Management's assessment of applicants on these factors are represented as fuzzy subsets. An overall fuzzy decision set is obtained using Hurwicz's rule to identify the most suitable applicant.
3. The technique helps banks select the applicant that best fits prescribed requirements. It can also be used to verify eligibility when new customers apply for loans.

Automation of trade settlement

The document discusses various technologies used for automating trade settlement processes, including invoice creation, delivery, and payment. It describes electronic invoicing and payment solutions from Citi, SWIFT, IATA, and others. It also discusses electronic funds transfer, ACH payments, electronic bill presentment and payment, and services like PayPal that allow online money transfers as alternatives to paper-based payments. The automation of these processes brings benefits like improved cash flow visibility, faster payment times, and lower costs compared to traditional paper-based methods.

Sampling Methods & Sampling Error PPT - For Seminar

This document discusses various sampling methods used in research including probability sampling techniques like simple random sampling, cluster sampling, systematic sampling, and stratified random sampling. It also covers non-probability sampling methods such as convenience sampling, judgmental sampling, quota sampling, and snowball sampling. The document explains how each method works with examples and concludes by defining sampling error and non-sampling error that can occur in research.

L4 theory of sampling

1. Sampling is the process of selecting a subset of items from a population to gather information about the entire population. It involves selecting a sample using probability or non-probability methods.
2. Probability sampling methods like simple random sampling, systematic sampling, and stratified sampling ensure each item has a known, non-zero chance of being selected. Non-probability methods like convenience sampling and purposive sampling rely on researcher judgment.
3. The central limit theorem states that as sample size increases, the sample mean will approach a normal distribution, allowing inferences about the population mean from a sample. Sampling error is reduced with larger sample sizes.

Sampling methods

This document discusses different sampling methods used in research. It defines population and sample, and explains that sampling is used to select a subset of a population when the entire population is too large. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling uses random selection and allows results to be generalized to the population, while non-probability sampling relies on the researcher's judgment and results cannot be generalized. Specific probability sampling methods described include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. Non-probability sampling methods mentioned are convenience sampling, snowball sampling, quota sampling, and judgmental sampling.

Sampling Theory Part 1

FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom

Sample method

Meaning of Research
Components of research
Meaning of Sampling
Sample breakdown
influencing factors
Types of Sample
Process of Samples

T11 types of tests

This document discusses parametric and nonparametric statistics, when each should be used, and their relative power. Parametric tests make assumptions about the population that may not always be justified, while nonparametric tests do not rely on these assumptions. Some key points:
1. Parametric tests assume normal distributions and equal variances, while nonparametric tests do not require these assumptions and can be used on small sample sizes or ordinal data.
2. There are nonparametric equivalents to most common parametric tests for comparing groups (independent or dependent samples) or relationships between variables.
3. Advantages of nonparametric tests are they provide exact probabilities regardless of the population distribution and can

Parametric tests

Parametric tests are used to analyze normally distributed numerical data and include Students' t-test and analysis of variance (ANOVA). The Students' t-test compares the means of two groups to determine if they are statistically different. ANOVA compares the means of two or more groups and is more efficient than multiple t-tests. It can be used for one-way, multifactor, or repeated measures designs. Both tests make assumptions about the data distribution and variance between groups.

Sampling, Types of Techniques & Simple Radom sampling

This document discusses simple random sampling. It begins by defining sampling as selecting a subset of units from a population to make inferences about the population. It then explains that simple random sampling (SRS) gives each unit of the population an equal chance of being selected. SRS is an easy method to conduct where numbers are assigned to population units and a random number generator is used to select samples. The key advantages of SRS are that it is easy to conduct and analyze and provides a representative sample.

Types of data sampling

Types of data sampling,probability sampling and non-probability sampling,Simple random sampling,Systematic sampling,Stratified sampling,Clustered sampling,Convenience sampling,Quota sampling,Judgement (or Purposive) Sampling,Snowball sampling,Bias in sampling.

Significance Tests

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

Standard error

Satyaki Aparajit Mishra presented on the topic of standard error and predictability limits. Standard error is used to estimate the standard deviation from a sample. It is calculated by dividing the standard deviation by the square root of the sample size. A larger standard error means the sample mean is less reliable at estimating the population mean. Standard error helps determine how far sample estimates may be from the true population values. Mishra discussed estimating standard error from a single sample and how standard error is used to test hypotheses. He provided an example of testing if a coin flip was unbiased using the standard error of the proportion of heads observed.

Hypothesis testing and parametric test

In the presentation, hypothesis test has been explained with scrap. Tree diagram is there to understand in which situation u can apply which parametric test

Tqm sampling

This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to collecting representative samples and how to select the appropriate sampling method for their research needs.

Sampling and sampling distribution tttt

This document discusses sampling and sampling distributions. It defines sampling as selecting a subset of a population for study. Random sampling gives each member of the population an equal chance of being selected, while non-random sampling uses other factors like convenience. The document outlines the steps in sampling design and describes different random and non-random sampling methods. It also distinguishes between sampling errors and non-sampling errors and discusses the concept of sampling distributions and how they allow inferences to be made about population parameters from sample statistics.

Topic 7 stat inference

The document discusses statistical hypothesis testing. It defines key terms like the null hypothesis, alternative hypothesis, test statistic, rejection region, Type I and Type II errors, significance level, and p-value. It also describes the steps to conduct a hypothesis test including stating the hypotheses, choosing a test statistic, determining critical values, and interpreting the conclusions. Specific hypothesis tests for a population mean are also covered, including tests when the population variance is known versus unknown.

NON-PARAMETRIC TESTS by Prajakta Sawant

This document provides an overview of non-parametric tests presented by Ms. Prajakta Sawant. It discusses non-parametric tests as distribution-free statistical tests that do not require assumptions about the underlying population distribution. Common non-parametric tests described include the Wilcoxon rank-sum test, Kruskal-Wallis test, Spearman's rank correlation coefficient, and the chi-square test. Examples are provided for each test to illustrate their application and interpretation.

Sampling and sampling techniques PPT

This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.

t-test vs ANOVA

T-test and ANOVA are statistical techniques used to test hypotheses and compare population means. The t-test is used to compare the means of two samples or groups, while ANOVA can compare the means of more than two groups. Specifically, the t-test examines whether two sample means are significantly different and assumes a normal distribution and unknown standard deviation. ANOVA compares three or more population means by assessing variation within and between groups, and assumes samples are from normally distributed populations with equal variances. Researchers should use a t-test when comparing only two means and ANOVA when comparing more than two means to avoid increasing the chances of a Type I error.

Complex random sampling designs

This document discusses various complex random sampling designs, including systematic sampling, stratified sampling, cluster sampling, multi-stage sampling, sampling with probability proportional to size, and sequential sampling. It provides details on how each design is implemented and their relative advantages and disadvantages. Complex random sampling designs combine elements of probability and non-probability sampling to select samples.

Sec 1.3 collecting sample data

Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 1: Introduction to Statistics
Section 1.3: Collecting Sample Data

Sampling Methods & Sampling Error PPT - For Seminar

Sampling Methods & Sampling Error PPT - For Seminar

L4 theory of sampling

L4 theory of sampling

Sampling methods

Sampling methods

Sampling Theory Part 1

Sampling Theory Part 1

Sample method

Sample method

T11 types of tests

T11 types of tests

Parametric tests

Parametric tests

Sampling, Types of Techniques & Simple Radom sampling

Sampling, Types of Techniques & Simple Radom sampling

Types of data sampling

Types of data sampling

Significance Tests

Significance Tests

Standard error

Standard error

Hypothesis testing and parametric test

Hypothesis testing and parametric test

Tqm sampling

Tqm sampling

Sampling and sampling distribution tttt

Sampling and sampling distribution tttt

Topic 7 stat inference

Topic 7 stat inference

NON-PARAMETRIC TESTS by Prajakta Sawant

NON-PARAMETRIC TESTS by Prajakta Sawant

Sampling and sampling techniques PPT

Sampling and sampling techniques PPT

t-test vs ANOVA

t-test vs ANOVA

Complex random sampling designs

Complex random sampling designs

Sec 1.3 collecting sample data

Sec 1.3 collecting sample data

Fuzzy Logic

1. The document proposes a fuzzy logic approach for evaluating loan applications in banks. It considers various factors like age, income, repayment history, account balance, etc. as fuzzy variables.
2. Management's assessment of applicants on these factors are represented as fuzzy subsets. An overall fuzzy decision set is obtained using Hurwicz's rule to identify the most suitable applicant.
3. The technique helps banks select the applicant that best fits prescribed requirements. It can also be used to verify eligibility when new customers apply for loans.

Automation of trade settlement

The document discusses various technologies used for automating trade settlement processes, including invoice creation, delivery, and payment. It describes electronic invoicing and payment solutions from Citi, SWIFT, IATA, and others. It also discusses electronic funds transfer, ACH payments, electronic bill presentment and payment, and services like PayPal that allow online money transfers as alternatives to paper-based payments. The automation of these processes brings benefits like improved cash flow visibility, faster payment times, and lower costs compared to traditional paper-based methods.

Why should you do mba

An MBA degree offers several benefits beyond just increased pay and career opportunities. It allows one to develop important business skills like integrating different areas, managing relationships between functions, and developing a holistic view of an organization. The degree also helps one gain people management skills through group work and exposes students to a diverse range of subjects and perspectives. Completing an MBA can be a way to fulfill one's potential and find work that is personally fulfilling through challenging coursework and networking opportunities. Overall, an MBA is a worthwhile investment that prepares one well for a management career.

Review Z Test Ci 1

1. The document discusses hypothesis testing using the z-test. It outlines the steps of hypothesis testing including stating hypotheses, setting the criterion, computing test statistics, comparing to the criterion, and making a decision.
2. Examples are provided to demonstrate a non-directional and directional z-test, including stating hypotheses, computing test statistics, comparing to criteria, and interpreting results.
3. Key concepts reviewed are the central limit theorem, type I and II errors, significance levels, rejection regions, p-values, and confidence intervals in hypothesis testing.

Summary of kotler's marketing management book

This document provides an overview of Philip Kotler's book on marketing management. It discusses 1) the scope and tasks of marketing, including the different types of markets and decisions that marketers face, 2) core marketing concepts like segmentation, targeting, and positioning, and 3) marketing tools such as the marketing mix and relationship marketing. The summary covers the key topics addressed in Kotler's work on developing customer value and managing the total marketing effort.

Z tests test statistic

This document discusses z-tests and provides examples of how to perform z-tests to test differences between population and sample means. It explains how to test:
- Population or hypothesized mean vs sample mean
- Two sample means when standard deviations are known
- Two sample means when the population standard deviation is known
It then provides three examples applying z-tests: testing if a sample mean is higher than a population mean, comparing two teaching methods, and comparing exam scores between traditional and technology-based teaching methods. The last example asks the reader to help verify the hypothesis at α=0.05 level of significance.

Z test

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.

Hypothesis Testing-Z-Test

Here are the steps to solve this problem:
1. State the hypotheses:
H0: μ = 100
H1: μ ≠ 100
2. The critical values are ±1.96 (two-tailed test, α=0.05)
3. Compute the test statistic:
z = (140 - 100)/15/√40 = 20/15/2 = 4
4. The test statistic is in the critical region, so reject the null hypothesis.
5. There is strong evidence that the medication affected intelligence since the sample mean is much higher than the population mean.

Introduction to t-tests (statistics)

The document discusses different types of t-tests used to determine if the means of two samples are statistically significantly different from each other. It describes paired sample t-tests used to compare means when the same subjects are measured before and after a treatment. It also describes two-sample t-tests used to compare independent samples that may have equal or unequal variances, and whether the tests are one-tailed or two-tailed. Examples are provided of interpreting t-test output and determining if differences are statistically significant based on the t-statistic and p-values. Non-parametric alternatives like the Mann-Whitney U test are also briefly mentioned.

Hypothesis testing ppt final

This document provides an overview of hypothesis testing in inferential statistics. It defines a hypothesis as a statement or assumption about relationships between variables or tentative explanations for events. There are two main types of hypotheses: the null hypothesis (H0), which is the default position that is tested, and the alternative hypothesis (Ha or H1). Steps in hypothesis testing include establishing the null and alternative hypotheses, selecting a suitable test of significance or test statistic based on sample characteristics, formulating a decision rule to either accept or reject the null hypothesis based on where the test statistic value falls, and understanding the potential for errors. Key criteria for constructing hypotheses and selecting appropriate statistical tests are also outlined.

T test

The t-test is used to compare the means of two groups and has three main applications:
1) Compare a sample mean to a population mean.
2) Compare the means of two independent samples.
3) Compare the values of one sample at two different time points.
There are two main types: the independent-measures t-test for samples not matched, and the matched-pair t-test for samples in pairs. The t-test assumes normal distributions and equal variances between groups. Examples are provided to demonstrate hypothesis testing for each application.

Student t-test

The document describes how to perform a student's t-test to compare two samples. It provides steps for both a matched pairs t-test and an independent samples t-test. For a matched pairs t-test, the steps are: 1) state the null and alternative hypotheses, 2) calculate the differences between pairs, 3) calculate the mean difference, 4) calculate the standard deviation of the differences, 5) calculate the standard error, 6) calculate the t value, 7) determine the degrees of freedom, 8) find the critical t value, and 9) determine if there is a statistically significant difference. For an independent samples t-test, similar steps are followed to calculate means, standard deviations, the difference between

Hypothesis testing; z test, t-test. f-test

Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction

Fuzzy Logic

Fuzzy Logic

Automation of trade settlement

Automation of trade settlement

Why should you do mba

Why should you do mba

Review Z Test Ci 1

Review Z Test Ci 1

Summary of kotler's marketing management book

Summary of kotler's marketing management book

Z tests test statistic

Z tests test statistic

Z test

Z test

Hypothesis Testing-Z-Test

Hypothesis Testing-Z-Test

Introduction to t-tests (statistics)

Introduction to t-tests (statistics)

Hypothesis testing ppt final

Hypothesis testing ppt final

T test

T test

Student t-test

Student t-test

Hypothesis testing; z test, t-test. f-test

Hypothesis testing; z test, t-test. f-test

Research methodology

The document summarizes the key differences between a t-test and a z-test. A t-test is used when the sample size is small and the population variance is unknown, while a z-test is used for large sample sizes where the population variance can be assumed to be known. The t-test follows a t-distribution and the z-test follows a normal distribution. Both tests are used to determine if the means of two datasets differ significantly.

Hypothesis testing: A single sample test

This document provides an introduction and overview of a presentation on hypothesis testing for a single sample test. It includes an abstract, introduction, definitions, explanations of the central limit theorem and t-test, assumptions, examples, and a question/answer section on hypothesis testing. A group of 11 students will be presenting on hypothesis testing for a single sample test, including topics like the central limit theorem, t-test, z-test, assumptions of different tests, and examples of applying the tests.

Parametric test

This document provides an overview of parametric statistical tests, including the z-test, t-tests, chi-square test, F-test, and Bartlett's test. It discusses the history and development of the Student's t-test, including its creation by William Gosset under the pseudonym "Student." The t-test is used to compare means between two samples or between a sample and a theoretical population. The document outlines the assumptions, calculations, and interpretations of one-sample, unpaired, and paired t-tests.

Testing of hypothesis and Goodness of fit

Testing of Hypothesis and Goodness of Fit
This document discusses hypothesis testing and goodness of fit. It defines hypothesis testing as a procedure to determine if sample data agrees with a hypothesized population characteristic. The key steps are stating the null and alternative hypotheses, selecting a significance level, determining the test distribution, defining rejection regions, performing the statistical test, and drawing a conclusion. Common hypothesis tests discussed include the Student's t-test and chi-square test of goodness of fit.

T-test

This document discusses different types of t-tests, including one sample t-tests and independent sample t-tests. A one sample t-test compares the mean of a sample to a known population mean. An independent sample t-test compares the means of two independent samples to determine if they are significantly different. Both tests assume the data is continuous and normally distributed. Examples are provided of when each type of t-test would be used.

T test^jsample size^j ethics

The document discusses the t-test, including:
1. It was introduced in 1908 by William Gosset under the pseudonym "Student" to test hypotheses about population means using small samples with unknown standard deviations.
2. The t-test has assumptions such as normality and equal variances that must be met.
3. There are different types of t-tests for different study designs: single sample t-test, independent samples t-test, and paired t-test.
4. Examples are provided to demonstrate how to calculate and interpret t-tests.

Parametric tests seminar

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

Test of significance

The document discusses various statistical tests used for hypothesis testing, including parametric and non-parametric tests. It provides information on descriptive statistics, inferential statistics, and the Gaussian distribution. Key tests covered include the z-test, t-test, chi-square test, ANOVA, and their appropriate uses and calculations. Examples are given to illustrate how to apply and interpret each test.

hypothesis.pptx

Hypothesis testing involves setting up a null hypothesis and alternative hypothesis, determining a significance level, calculating a test statistic, identifying the critical region, computing the test statistic value based on a sample, and making a decision to reject or fail to reject the null hypothesis. The z-test is used when the sample size is large and the population standard deviation is known, while the t-test is used for small samples when the population standard deviation is unknown. Both tests involve calculating a test statistic and comparing it to critical values to determine if there is sufficient evidence to reject the null hypothesis. Limitations include that the tests only indicate differences and not the reasons for them, and inferences are based on probabilities rather than certainty.

Chapter 08

1. The document discusses sampling methods and the central limit theorem. It describes various probability sampling methods like simple random sampling, systematic random sampling, and stratified random sampling.
2. It defines the sampling distribution of the sample mean and explains that according to the central limit theorem, the sampling distribution will follow a normal distribution as long as the sample size is large.
3. The mean of the sampling distribution is equal to the population mean, and its variance is equal to the population variance divided by the sample size. This allows probabilities to be determined about a sample mean falling within a certain range.

Statistical Techniques in Business & Economics (McGRAV-HILL) 12 Edt. Chapter ...

This chapter discusses sampling methods and the central limit theorem. It has five learning goals:
1) Explain why sampling is used instead of studying the entire population.
2) Describe methods for selecting a sample, including random sampling techniques.
3) Define and construct the sampling distribution of the sample mean.
4) Explain the central limit theorem and how it applies to sampling distributions.
5) Use the central limit theorem to find probabilities related to sample means.

Student's T-Test

The document provides an overview of the student's t-test, a statistical hypothesis test used to determine if two sets of data are significantly different from each other. It discusses the different types of t-tests, their main uses which include comparing sample means to hypothesized values or between two groups, assumptions of the t-test, and how it relates to the z-test and normal distribution. Examples of one sample, paired, and independent sample t-tests are also provided.

Sampling

Sampling refers to selecting a subset of a population to make inferences about the whole population. There are two main types of sampling: probability sampling, which aims to be representative, and non-probability sampling. Probability sampling includes random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Non-probability sampling includes convenience sampling and snowball sampling. Sample size, standard error, and confidence levels allow researchers to assess how representative their sample is of the overall population.

Normal & t-test_confidence interval

t-based Confidence Interval for the Mean. The 95% confidence interval provided by ( 7.4) is simple, but not very useful.

Parametric tests

This document provides an overview of parametric statistical tests used in pharmacology research. It introduces biostatistics and common statistical terms. It describes different types of data and measures of central tendency like mean, median, and mode. Parametric tests discussed include the z-test, t-test, and ANOVA. The z-test is used for large samples to compare proportions or means. The t-test is similar but for small samples and includes one-sample, two-sample, and paired t-tests. ANOVA compares multiple group means and includes one-way and two-way ANOVA. Examples are provided to demonstrate how to perform and interpret each test.

SAMPLING Theory.ppt

1) Sampling involves collecting data from a subset of individuals (the sample) rather than from the entire population.
2) There are two main types of sampling: probability sampling, where each individual has a known chance of being selected, and non-probability sampling, where the probability of selection is unknown.
3) Common probability sampling methods include simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Non-probability methods include quota sampling and snowball sampling.

The t test mean comparison 1

The document discusses the t-test, which is a statistical test used to determine if two sets of data are significantly different from each other. It can be used to compare the means of two groups, related groups, or compare a sample to a population. The t-test has assumptions about the data that must be met, such as normal distribution and equal variances. It provides a t-value that can be used to determine if there is a statistically significant difference between groups. The document outlines the different types of t-tests and their appropriate uses and interpretations.

Z-test

This document discusses the Z-test, a statistical test used to compare means and proportions. The Z-test can be used to test if a sample mean differs from a population mean, if two sample means are equal, or if two population proportions are equal. It assumes the population is normally distributed. The steps involve formulating hypotheses, choosing a significance level, calculating the Z-statistic, and comparing it to a critical value to determine if the null hypothesis should be rejected or accepted. The Z-test is useful when sample sizes are large but requires knowing the population standard deviation.

Research methodology module 3

This document contain all topics of research methodology of module-3 according to the syllabus of BPUT odisha. The document is done for the PG and PHD students who are doing research.

RESEARCH METHODOLOGY - 2nd year ppt

This ppt includes Student's T-Test, Paired T-Test, Chi-Square Test, X2 Test for population variance. There Introduction, Characteristics, Assumptions, Applications, and Formulas. This is useful for 2nd year students of BBA or BBM studying research methodology,

Research methodology

Research methodology

Hypothesis testing: A single sample test

Hypothesis testing: A single sample test

Parametric test

Parametric test

Testing of hypothesis and Goodness of fit

Testing of hypothesis and Goodness of fit

T-test

T-test

T test^jsample size^j ethics

T test^jsample size^j ethics

Parametric tests seminar

Parametric tests seminar

Test of significance

Test of significance

hypothesis.pptx

hypothesis.pptx

Chapter 08

Chapter 08

Statistical Techniques in Business & Economics (McGRAV-HILL) 12 Edt. Chapter ...

Statistical Techniques in Business & Economics (McGRAV-HILL) 12 Edt. Chapter ...

Student's T-Test

Student's T-Test

Sampling

Sampling

Normal & t-test_confidence interval

Normal & t-test_confidence interval

Parametric tests

Parametric tests

SAMPLING Theory.ppt

SAMPLING Theory.ppt

The t test mean comparison 1

The t test mean comparison 1

Z-test

Z-test

Research methodology module 3

Research methodology module 3

RESEARCH METHODOLOGY - 2nd year ppt

RESEARCH METHODOLOGY - 2nd year ppt

B2B Marketing - Summary

The document provides an overview of business-to-business (B2B) marketing. It defines business marketing and outlines key differences between B2B and consumer marketing. Specifically, it notes that B2B demand is derived, customers are concentrated, the buying process is complex, technology and customization are important, and total cost of ownership is a key factor. The document also discusses B2B products/services, markets, customers, procurement processes, and challenges of government and institutional procurement.

Alloy Rods Corporation

ppt on "Alloy Rods Corporation" case study.
http://hbr.org/product/alloy-rods-corp/an/586046-PDF-ENG?Ntt=Alloy%2520Rods

Linear Programming Module- A Conceptual Framework

This document provides an overview of linear programming and how to formulate and solve linear programming problems. Key points:
- Linear programming involves optimizing an objective function subject to constraints, where all relationships are linear. It can be used to solve problems like resource allocation.
- To formulate a problem, you identify decision variables, write the objective function and constraints in terms of the variables, and specify non-negativity.
- Graphical methods can solve small 2-variable problems by finding the optimal point in the feasible region bounded by the constraint lines. Larger problems use computer solutions like the simplex method.
- To solve in Excel, you set up the model with decision variables, objective function

Sensitivity analysis

The document is an introduction to sensitivity analysis that contains three exploratory exercises demonstrating how changes to parameter values affect system behavior in system dynamics models. The first exercise explores a lemonade stand model and finds that while parameter changes alter the appearance of behavior, they do not change the overall behavior mode. The second exercise on an epidemics model shows that different parameter changes can create different types of behavior changes. The exercises are intended to help readers understand how to identify important parameters for sensitivity testing and how parameter values can influence system dynamics.

Mountain dew

Ppt on "Mountain Dew: Selecting New Creative" case study. On the HBR case http://hbr.org/product/mountain-dew-selecting-new-creative/an/502040-PDF-ENG

Intel

The Intel Inside campaign was highly successful for Intel. It increased Intel's market capitalization from $1 billion to $5 billion by 2003 and worldwide sales rose 63% in its first year. The campaign made Intel synonymous with processors and helped launch new chip lines. It simplified an understanding of chips for customers. However, the campaign also made diversification difficult without changing the theme. For AMD, copying the campaign would not be as effective since Intel had already established brand recognition, so AMD should focus on value, efficacy, and low-cost markets. Intel segmented based on performance and price to target different market segments.

B2B Marketing - Summary

B2B Marketing - Summary

Alloy Rods Corporation

Alloy Rods Corporation

Linear Programming Module- A Conceptual Framework

Linear Programming Module- A Conceptual Framework

Sensitivity analysis

Sensitivity analysis

Mountain dew

Mountain dew

Intel

Intel

- 1. Some Important notes on use of Student’s T-test and Z-test for Hypothesis Testing: The t-statistic was introduced in 1908 by William Sealy Gosset, a chemist working for the Guinness brewery in Dublin, Ireland ("Student" was his pen name). Basic Underlying Assumptions in T-test are: 1) Samples are independent and randomly drawn from a normal population. 2) Sample size is small. (n<=30) Below are the seven situations which give an idea about when to use T-test or Z-test: • Situation 1 : Samples are independent and randomly drawn from normal population whose mean (µ) and standard deviation (σ) are known Sample size is large i.e. n>=30 Test to be used for Hypothesis testing: Z-test • Situation 2 : Samples are independent and randomly drawn from normal population whose mean (µ) and standard deviation (σ) are known Sample size is small i.e. n<=30 Test to be used for Hypothesis testing: Z-test or T-test • Situation 3 : Samples are independent and randomly drawn from a normal population whose mean (µ) is known but standard deviation (σ) is not known Population size is large If standard deviation of sample is known then use sample standard deviation as best estimate for population standard deviation. Test to be used for Hypothesis testing: Z-test • Situation 4: Samples are independent and randomly drawn from a Normal population whose mean (µ) is known but standard deviation (σ) is not known Population size is small If standard deviation of sample is known then use sample standard deviation as best estimate for population standard deviation. Test to be used for Hypothesis testing: T-test
- 2. • Situation 5: Samples are independent and randomly drawn from any population whose standard deviation (σ) is known and whose sample size is large. Test to be used for Hypothesis testing: Z-test because of central limit theorem • Situation 6: Samples are independent and randomly drawn from any population whose standard deviation (σ) is unknown and whose sample size is large. Use sample standard deviation to approximate to population standard deviation and use Z-test. • Situation 7: Samples are independent and randomly drawn from any population whose standard deviation (σ) is unknown and sample size is small. In this situation, other test like Wilkokson’s Test is used