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Non-parametric statistics is a branch of statistics that does not require data to be normally distributed. It can be used with ordinal or ranked data and does not assume a particular distribution shape or require parameters like the mean or standard deviation. Common non-parametric tests include rank sum tests like the Wilcoxon-Mann-Whitney U test and the Kruskal-Wallis H test, the chi-square test, and Spearman's rank correlation test. These tests make fewer assumptions about the underlying data distribution compared to parametric tests.

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Non parametric test

The document discusses non-parametric tests and provides information about when to use them. Non-parametric tests make fewer assumptions about the distribution of population values and can be used when sample sizes are small or the data is ordinal. Examples of non-parametric tests provided include the sign test, chi-square test, Mann-Whitney U test, and Kruskal-Wallis test. The general steps to perform a non-parametric test are also outlined.

Data Analysis

this ppt describes about descriptive and inferential statistics, levels of measurement, hypothesis testing, parametric, non parametric tests.

non parametric statistics

This document provides an overview of non-parametric statistics. It defines non-parametric tests as those that make fewer assumptions than parametric tests, such as not assuming a normal distribution. The document compares and contrasts parametric and non-parametric tests. It then explains several common non-parametric tests - the Mann-Whitney U test, Wilcoxon signed-rank test, sign test, and Kruskal-Wallis test - and provides examples of how to perform and interpret each test.

Non parametric study; Statistical approach for med student

Non-parametric statistics are statistical methods that do not rely on assumptions about the probability distributions of the variables being assessed. They make fewer assumptions than parametric tests and can be used with ordinal or nominal data. Some common non-parametric tests include the chi-square test, McNemar's test, sign test, Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis test, and Spearman's rank correlation test. Non-parametric tests are useful when the data is ranked or does not meet the assumptions of parametric tests, as they provide a distribution-free way to perform statistical hypothesis testing.

Parametric Statistical tests

In Hypothesis testing parametric test is very important. in this ppt you can understand all types of parametric test with assumptions which covers Types of parametric, Z-test, T-test, ANOVA, F-test, Chi-Square test, Meaning of parametric, Fisher, one-sample z-test, Two-sample z-test, Analysis of Variance, two-way ANOVA.
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Parametric and Non Parametric methods

This document discusses parametric and nonparametric statistical methods. Parametric methods make assumptions about the population distribution from which the data are drawn, while nonparametric methods make fewer assumptions and do not assume a particular distribution. Nonparametric methods can be used when the data are ordinal or nominal, the sample size is small, or the data do not meet the assumptions of parametric tests like normality. Some common nonparametric tests are the sign test, Wilcoxon signed-rank test, Mann-Whitney U test, and Kruskal-Wallis H test.

Non parametric test

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

Non parametric-tests

The document discusses several non-parametric tests that can be used as alternatives to parametric tests when the assumptions of parametric tests are violated. Specifically, it discusses:
1. The sign test and one sample median test, which can be used instead of t-tests when the data is skewed or not normally distributed.
2. Mood's median test, which compares the medians of two independent samples and is the nonparametric version of a one-way ANOVA.
3. The Kruskal-Wallis test, which determines if there are differences in medians across three or more groups and is the nonparametric version of a one-way ANOVA.

Parametric & non parametric

This document provides an overview of parametric and non-parametric statistical tests. Parametric tests assume the data follows a known distribution (e.g. normal) while non-parametric tests make no assumptions. Common non-parametric tests covered include chi-square, sign, Mann-Whitney U, and Spearman's rank correlation. The chi-square test is described in more detail, including how to calculate chi-square values, degrees of freedom, and testing for independence and goodness of fit.

Non Parametric Tests

v When to Choose a Statistical Tests OR When NOT to Choose? v Parametric vs. Non-Parametric Tests (Comparison)
v Parameters to check when Choosing a Statistical Test:
- Distribution of Data
- Type of data/Variable
- Types of Analysis (What’s the hypothesis)
- No of groups or data-sets
- Data Group Design
v Snapshot of all statistical test and “How” to Choose using above parameters v Explanation using Examples:
- Mann Whitney U Test
- Wilcoxon Sign Rank Test
- Spearman’s co-relation
- Chi-Square Test
v Conclusion

Anova; analysis of variance

The document provides an overview of analysis of variance (ANOVA). It defines ANOVA and discusses its key concepts, including how it was developed by Ronald Fisher. It also covers one-way and two-way ANOVA, describing their techniques and providing examples. The uses, advantages and limitations of ANOVA are outlined.

3.1 non parametric test

The document provides an overview of biostatistics and research methodology topics. It defines key biostatistics concepts like population and parameter. It also introduces several non-parametric tests like the Wilcoxon Rank Sum test, Mann-Whitney U test, Kruskal-Wallis test, and Friedman test. Additionally, it discusses important aspects of research like the need for research, types of research, the research process, and challenges researchers may encounter.

Parametric and non parametric test in biostatistics

This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test

Tugasan kumpulan anova

ANOVA is a statistical technique used to compare the means of three or more groups. It can test if population means are equal or if some are different. The document outlines the steps in ANOVA including describing data, stating hypotheses, calculating test statistics, and making conclusions. It also discusses one-way and two-way ANOVA designs, comparing means between multiple groups while controlling for Type I error, and the calculations involved including sums of squares, degrees of freedom, and F-ratios.

Assumptions about parametric and non parametric tests

Parametric tests make specific assumptions about the population parameters, such as the data being normally distributed. Non-parametric tests do not assume a particular distribution and instead focus on differences in medians or proportions. Common assumptions for parametric tests include normality of data, homogeneity of variances between groups, a linear relationship between variables, and independence of observations. Specific parametric tests like the t-test and ANOVA also assume random sampling from a normally distributed population and additivity of treatment effects.

Parametric test - t Test, ANOVA, ANCOVA, MANOVA

The document discusses various parametric statistical tests including t-tests, ANOVA, ANCOVA, and MANOVA. It provides definitions and assumptions for parametric tests and explains how they can be used to analyze quantitative data that follows a normal distribution. Specific parametric tests covered in detail include the independent samples t-test, paired t-test, one-way ANOVA, two-way ANOVA, and ANCOVA. Examples are provided to illustrate how each test is conducted and how results are interpreted.

Analysis of Variance (ANOVA)

This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines key terms like factors, interactions, F distribution, and multiple comparison tests. For one-way ANOVA, it explains how to test if three or more population means are equal. For two-way ANOVA, it notes you must first test for interactions between two factors before testing their individual effects. The Tukey test is introduced for identifying specifically which group means differ following rejection of a one-way ANOVA null hypothesis.

Statistical methods for the life sciences lb

This document discusses nonparametric statistical methods and rank tests. It begins with an introductory example comparing blood pressure readings before and after surgery using a paired t-test approach and discussing its assumptions. It then introduces nonparametric hypothesis testing as an alternative that does not rely on distributional assumptions. The document outlines what test to use based on factors like the number of groups and whether samples are independent or dependent. It provides detailed examples of the Wilcoxon rank sum test to compare two independent samples and the Kruskal-Wallis and Friedman tests for comparing more than two groups.

Non parametric presentation

Nonparametric tests can analyze ordinal or nominal data without assumptions about the population distribution. They include the chi-square test, Kruskal-Wallis test, Wilcoxon signed-rank test, median test, and sign test. SPSS examples demonstrate using the binomial test to compare a proportion to 50%, the Kolmogorov-Smirnov test to check normality, and Kruskal-Wallis to compare more than two independent groups.

Chi square

The document provides an overview of chi-square tests, including chi-square tests for goodness of fit and tests of independence. It explains that chi-square tests are used with categorical or classified data rather than numerical data. For a chi-square test of goodness of fit, the null hypothesis specifies the expected proportions in different categories. Observed and expected frequencies are calculated and compared using the chi-square statistic. A chi-square test of independence examines whether two categorical variables are related by comparing observed and expected joint frequencies.

Non parametric test

Non parametric test

Data Analysis

Data Analysis

non parametric statistics

non parametric statistics

Non parametric study; Statistical approach for med student

Non parametric study; Statistical approach for med student

Parametric Statistical tests

Parametric Statistical tests

Parametric and Non Parametric methods

Parametric and Non Parametric methods

Non parametric test

Non parametric test

Non parametric-tests

Non parametric-tests

Parametric & non parametric

Parametric & non parametric

Non Parametric Tests

Non Parametric Tests

Anova; analysis of variance

Anova; analysis of variance

3.1 non parametric test

3.1 non parametric test

Parametric and non parametric test in biostatistics

Parametric and non parametric test in biostatistics

Tugasan kumpulan anova

Tugasan kumpulan anova

Assumptions about parametric and non parametric tests

Assumptions about parametric and non parametric tests

Parametric test - t Test, ANOVA, ANCOVA, MANOVA

Parametric test - t Test, ANOVA, ANCOVA, MANOVA

Analysis of Variance (ANOVA)

Analysis of Variance (ANOVA)

Statistical methods for the life sciences lb

Statistical methods for the life sciences lb

Non parametric presentation

Non parametric presentation

Chi square

Chi square

Non-parametric Statistical tests for Hypotheses testing

A complete guidelines for Non-parametric Statistical tests for Hypotheses testing with relevant examples which covers Meaning of non-parametric test, Types of non-parametric test, Sign test, Rank sum test, Chi-square test, Wilcoxon signed-ranks test, Mc Nemer test, Spearman’s rank correlation, statistics,
Subscribe to Vision Academy for Video assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw

biostat__final_ppt_unit_3.pptx

This document provides information on non-parametric tests and introduces research methodology concepts. It discusses non-parametric tests including the Wilcoxon signed rank test, Wilcoxon signed sum test, Kruskal-Wallis test, and Friedman test. It covers assumptions, calculations, and applications of these tests. The document also defines research as a systematic process of investigating topics to advance knowledge through methods like data collection and analysis. It emphasizes the importance of validity and reliability in research.

GROUP 08 .pptx

This document discusses non-parametric tests in research methodology. It introduces non-parametric tests as statistical tests that do not assume a specific distribution or parameters for the population being studied. Some common non-parametric tests are then described in 1-2 sentences each, including the Kolmogorov-Smirnov test, chi-square test, Mann-Whitney U test, and Wilcoxon test. The document concludes that non-parametric tests serve as alternatives to parametric tests like the t-test or ANOVA when their assumptions are not met by the data.

Marketing Research Hypothesis Testing.pptx

This document provides an overview of parametric and non-parametric hypothesis tests. It defines parametric tests as those that assume an underlying normal distribution, and lists common parametric tests like the z-test, t-test, F-test, and ANOVA. Non-parametric tests make no distributional assumptions and common examples discussed include the Mann-Whitney U test, chi-square test, and Kruskal-Wallis test. The document provides details on assumptions and procedures for conducting each of these important statistical hypothesis tests.

Stat topics

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

Parametric vs non parametric test

Parametric and non-parametric tests differ in their assumptions about the population from which data is drawn. Parametric tests assume the population is normally distributed and variables are measured on an interval scale, while non-parametric tests make fewer assumptions. Examples of parametric tests include t-tests and ANOVA, while non-parametric examples include chi-square, Mann-Whitney U, and Wilcoxon signed-rank. Parametric tests are more powerful but rely on stronger assumptions, while non-parametric tests are more flexible but less powerful. Researchers must consider the characteristics of their data and questions being asked to determine the appropriate test.

ritika saini.pptx

The document discusses several non-parametric statistical tests used in psychology:
i) The chi-squared test compares observed and expected categorical data to test hypotheses. It is used to test goodness of fit and independence.
ii) The Wilcoxon signed-rank test compares two matched samples to test the location of a population or compare two populations. It assumes dependent samples, independence, continuous dependent variables, and ordinal measurement.
iii) The Mann-Whitney U test compares two independent samples to test if they come from the same population. It assumes a continuous or ordinal dependent variable, a dichotomous independent variable, independence of observations, and the same or different shape of distributions between groups.

non parametric test.pptx

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

Research Procedure

This part of the thesis describes the methodology section which provides details of the research activities, data collection strategies, and administration of questionnaires and interviews to achieve the study objectives and address the problem. It discusses preparing and testing questionnaires, identifying persons responsible for data collection, and approaches for administering questionnaires and conducting interviews.

Quantitative data analysis

This document provides an overview of quantitative data analysis and statistical tests. It discusses research questions, variables, descriptive and inferential statistics. Common statistical tests are explained like the Mann-Whitney U test, Spearman rank correlation, Kruskal-Wallis test, t-test, Pearson correlation, ANOVA, and chi-square test. Factors to consider when selecting a statistical test are highlighted like level of data, number of groups, independent or related groups, and data distribution. The document emphasizes keeping analyses simple and statistics in context of discussion.

Statistical test

Statistical tests provide a mechanism for making quantitative decisions about processes by determining if there is enough evidence to reject conjectures. Common statistical tests include correlational tests, comparison of means tests, regression tests, and non-parametric tests. Two-sample tests compare two independent samples, while paired tests compare two related samples by looking at differences between pairs. One-tailed and two-tailed tests determine rejection regions. ANOVA tests examine differences between group means. One-way ANOVA compares two independent groups, while two-way ANOVA compares groups with two independent variables and their interactions.

Presentation chi-square test & Anova

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

April Heyward Research Methods Class Session - 8-5-2021

This document provides an overview of key concepts in research methods for public administration, including:
1. Levels of measurement for variables, including nominal, ordinal, interval, and ratio levels. Examples are provided for each level.
2. Common research designs such as experimental, quasi-experimental, cross-sectional, and longitudinal designs.
3. Quantitative data analysis techniques including descriptive statistics, inferential statistics like ANOVA and regression, and correlation analysis. Frequency distributions, measures of central tendency and variability are covered.
4. Confidence intervals and how they are used to estimate population parameters more accurately than point estimates, by providing a probability assessment through setting a confidence level. Common confidence levels like 90%, 95%,

TEST OF SIGNIFICANCE.pptx

This document provides an overview of various statistical tests used for hypothesis testing, including parametric and non-parametric tests. It defines key terms like population, sample, mean, median, mode, and standard deviation. It explains the stages of hypothesis testing including creating the null and alternative hypotheses, determining the significance level, and deciding which statistical test to use based on the type of data and number of samples. Specific tests covered include the z-test, t-test, ANOVA, chi-square test, Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis test, and Friedman test.

T test, independant sample, paired sample and anova

The document discusses various statistical analyses that can be performed in SPSS, including t-tests, ANOVA, and post-hoc tests. It provides details on one-sample t-tests, independent t-tests, paired t-tests, one-way ANOVA tests, and evaluating assumptions like normality. Examples are given on how to conduct these tests in SPSS and how to interpret the output. Guidance is provided on follow-up post-hoc tests that can be used after ANOVA to examine differences between specific groups.

Week 7 spss 2 2013

This document provides an overview of using SPSS to conduct descriptive and inferential statistical analyses. It discusses entering data into SPSS, conducting descriptive analyses, and using SPSS to perform inferential tests including chi-squared tests, correlations, and t-tests. An example research study is described that aims to determine if a particular teaching method leads to higher achievement and satisfaction for visual learners. The document outlines how SPSS can be used to establish causality, describe the sample, test for independence between variables, measure correlations, and test for significant differences in means between groups.

Analysis of data thiyagu

This presentation consist of analysis of data in education aspects. This presentation deals about bivariate, multivariate anlaysis and it is also describes the descriptive and inferential statistics.

Analysis of Data - Dr. K. Thiyagu

This document provides an overview of various statistical techniques for analyzing data, including descriptive statistics, inferential statistics, and different types of tests. It discusses scales of measurement, variables, and statistical methods like t-tests, ANOVA, ANCOVA, MANOVA, and regression. It also covers topics like degrees of freedom, interpretation of results based on table values and p-values, and use of SPSS for conducting analyses like independent and paired t-tests, one-way ANOVA, and post-hoc tests. The document aims to define key statistical concepts and summarize different analytical procedures for working with univariate, bivariate and multivariate data.

Analysis of Data - Dr. K. Thiyagu

The presentation slides describes about the analysis of data. The presentation slides deals about scales of measurement, t test, ANOVA, ANCOVA, MANOVA, regression and SPSS help desk.

non para.doc

This document discusses non-parametric tests and how to use them to compare groups when assumptions of parametric tests are violated. It explains that non-parametric tests like the Wilcoxon and Kruskal-Wallis tests can be used when samples are small or data is not normally distributed. The Kruskal-Wallis test allows comparison of more than two groups by ranking all data and comparing mean ranks between groups. An example compares student grades under different teaching methods using both Kruskal-Wallis and ANOVA tests.

Non-parametric Statistical tests for Hypotheses testing

Non-parametric Statistical tests for Hypotheses testing

biostat__final_ppt_unit_3.pptx

biostat__final_ppt_unit_3.pptx

GROUP 08 .pptx

GROUP 08 .pptx

Marketing Research Hypothesis Testing.pptx

Marketing Research Hypothesis Testing.pptx

Stat topics

Stat topics

Parametric vs non parametric test

Parametric vs non parametric test

ritika saini.pptx

ritika saini.pptx

non parametric test.pptx

non parametric test.pptx

Research Procedure

Research Procedure

Quantitative data analysis

Quantitative data analysis

Statistical test

Statistical test

Presentation chi-square test & Anova

Presentation chi-square test & Anova

April Heyward Research Methods Class Session - 8-5-2021

April Heyward Research Methods Class Session - 8-5-2021

TEST OF SIGNIFICANCE.pptx

TEST OF SIGNIFICANCE.pptx

T test, independant sample, paired sample and anova

T test, independant sample, paired sample and anova

Week 7 spss 2 2013

Week 7 spss 2 2013

Analysis of data thiyagu

Analysis of data thiyagu

Analysis of Data - Dr. K. Thiyagu

Analysis of Data - Dr. K. Thiyagu

Analysis of Data - Dr. K. Thiyagu

Analysis of Data - Dr. K. Thiyagu

non para.doc

non para.doc

Capital structure theories - NI Approach, NOI approach & MM Approach

Capital structure theories - NI Approach, NOI approach & MM Approach. Meaning of capital structure , Features of An Appropriate Capital Structure, Determinants of Capital Structure, Planning the Capital Structure Important Considerations,

Sample and Population in Research - Meaning, Examples and Types

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Sample and Population in Research - Meaning, Examples and Types

Application of Univariate, Bivariate and Multivariate Variables in Business R...

In this ppt you can find the materials relating to Application of Univariate, Bivariate and Multivariate Variables in Business Research. Also What is Variable, Types of Variables, Examples of Independent Variables, Examples of Dependent Variables, Common techniques used in univariate analysis include, Common techniques used in bivariate analysis include, Common techniques used in Multivariate analysis include, Difference B/w Univariate, Bivariate & Multivariate Analysis

INDIAN FINANCIAL SYSTEM CODE

Indian financial system code
IFSC
Meaning of IFSC
How to find IFSC code
Example of IFSC code
Features of IFSC
Benefits of IFSC.

NATIONAL ELECTRONIC FUND TRANSFER

This document discusses National Electronic Funds Transfer (NEFT) in India. It provides information on:
- NEFT is an electronic payment system developed by the Reserve Bank of India that allows individuals and businesses to transfer funds between banks securely and efficiently.
- Transactions are processed in batches throughout the day on a deferred settlement basis.
- NEFT is widely used for salary payments, bill payments, and online shopping due to its fast processing time (within hours) and low transaction fees compared to other electronic payment systems.
- The document provides details on conducting NEFT transactions through various digital and branch-based methods from ICICI Bank and the applicable transaction charges.

PRIVILEGE BANKING

Privilege banking
Privilege banking advantages
Privilege banking disadvantage
Types of privilege bank.

ISLAMIC BANKING

Islamic banks operate based on Islamic principles rather than as money lending institutions. They prohibit interest and instead require profit and loss sharing as well as permissible activities like partnership, sales, agency and rent. To function without interest, Islamic banks provide accounts that share profits and losses from investments rather than guaranteeing fixed interest returns. Islamic banking has expanded globally and differs from conventional banks in adhering to Islamic law.

FOLLOW ON PUBLIC OFFER

Follow on public offer
Meaning
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Diluted FPO
Non - diluted FPO
At - the market offering
Benefits
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TRADE MARKS

This presentation introduces trademarks and their importance. A trademark is any sign that identifies goods from one enterprise and distinguishes them from competitors. Trademarks provide legal protection against fake products, allow customers to easily identify brands, and create goodwill. Essential features of trademarks include being distinctive, easy to pronounce, not descriptive, and satisfying registration requirements. There are different types of trademarks including word marks featuring words or letters, device marks representing logos or designs, service marks identifying services, and collective marks used by groups.

NET BANKING

Net banking
Net banking meaning
How to use net banking
NEFT
RTGS
IMPS
Features of net banking
Advantages of net banking.

CROWD FUNDING

CROWD funding
Types of crowd funding
Donation crowd funding
Reward crowd funding
Equity crowd funding
Debt crowd funding
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INFLATION

Inflation is a worldwide phenomenon where commodity prices are rising and money values are falling. There are two main types of inflation: demand-pull inflation, which occurs when aggregate demand outpaces supply, and cost-push inflation caused by increases in production costs. Inflation can also be categorized by its speed as creeping, walking, running, or galloping depending on the annual growth rate of prices. In conclusion, inflation reduces consumer purchasing power and equilibrium as consumers must cut back on consumption.

VIDEO MARKETING

Video marketing
Strategies of video marketing
Benefits
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How to make videos for business

INTEGRATION OF FINANCIAL MARKET

Integrated financial market
Financial integration
Types of strategies
Direct channel
Indirect channel

STARTUPS IN INDIA

The document provides an overview of startups in India, including key facts and figures as well as challenges. It discusses the three pillars of the National Flagship Initiative called Startup India, launched in 2015 by Prime Minister Narendra Modi, to promote entrepreneurship. These pillars include simplification, handholding, and funding support. It defines what qualifies as a startup and reasons for promoting startups, including generating employment and encouraging innovation. Some top Indian startups highlighted include Ola, Paytm, Oyo Rooms, and Zomato. Common challenges faced by startups are also listed, such as lack of innovation, funding, mentorship, and human resource issues.

ATM

An ATM, or automated teller machine, allows users to access their bank accounts to withdraw cash, check balances, and transfer funds without needing to visit a bank branch. ATMs are installed by banks in various locations and allow any user to withdraw funds from their account, regardless of which bank owns the ATM. Transactions may be subject to fees depending on the bank and number of transactions in a month. To use an ATM, a user inserts their debit card and enters their PIN to access a menu of transaction options on screen. Following the on-screen instructions, a user can withdraw cash, deposit funds or checks, and check their account balance.

NABARD

NABARD
Functions of NABARD
Long term refinance
Interest rates
Developmental functions
Supervisory functions
Government sponsered schemes
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UPI

UPI is a payment system that allows users to link multiple bank accounts to a single smartphone app to transfer funds without needing account numbers or IFSC codes. It offers instant payments through a virtual payment address with authentication using the mobile phone and a 4-6 digit PIN. UPI aims to simplify online payments with a single interface across all NPCI systems while improving security by eliminating the need to share sensitive bank details with others.

National pension scheme

The document discusses the National Pension Scheme (NPS) in India. NPS is a social security program open to both public and private sector employees between 18-60 years old, except armed forces personnel. It is regulated by the Pension Fund Regulatory and Development Authority (PFRDA). To open an NPS account, one can visit a point of presence like a bank or post office either offline or online. A Permanent Retirement Account Number (PRAN) is issued upon registration. There are two tiers of accounts - Tier 1 offers tax benefits and matures at age 60, while Tier 2 is voluntary and does not provide tax benefits. The document outlines the fund managers in the government and non

Green banking

Green banking
Meaning
Features of green banking
Sources of green banking
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Benefits and challenges.

Capital structure theories - NI Approach, NOI approach & MM Approach

Capital structure theories - NI Approach, NOI approach & MM Approach

Sample and Population in Research - Meaning, Examples and Types

Sample and Population in Research - Meaning, Examples and Types

Application of Univariate, Bivariate and Multivariate Variables in Business R...

Application of Univariate, Bivariate and Multivariate Variables in Business R...

INDIAN FINANCIAL SYSTEM CODE

INDIAN FINANCIAL SYSTEM CODE

NATIONAL ELECTRONIC FUND TRANSFER

NATIONAL ELECTRONIC FUND TRANSFER

PRIVILEGE BANKING

PRIVILEGE BANKING

ISLAMIC BANKING

ISLAMIC BANKING

FOLLOW ON PUBLIC OFFER

FOLLOW ON PUBLIC OFFER

TRADE MARKS

TRADE MARKS

NET BANKING

NET BANKING

CROWD FUNDING

CROWD FUNDING

INFLATION

INFLATION

VIDEO MARKETING

VIDEO MARKETING

INTEGRATION OF FINANCIAL MARKET

INTEGRATION OF FINANCIAL MARKET

STARTUPS IN INDIA

STARTUPS IN INDIA

ATM

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UPI

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Green banking

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School Calendar 2024 DO_s2024_008.pdf

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How To Sell Hamster Kombat Coin In Pre-market

How To Sell Hamster Kombat Coin In Pre Market
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Be short to reply to any questions capability customers may have. They may need to realize how the coin works, its destiny capability, or technical details. Make positive you have got the answers equipped.
Talk without delay with involved customers to agree on a charge and finalize the sale. Make sure both facets apprehend how the coins and money could be exchanged.
How To Sell Hamster Kombat Coin In Pre Market
Once everything is settled, move beforehand with the transaction as deliberate. You might switch the cash immediately or use a provider to assist.
Stay in Touch: After the sale, check in with the customer to ensure they were given the coins. If viable, leave feedback in the network to expose you’re truthful.
How To Sell Hamster Kombat Coin In Pre Market
When you need to promote a cryptocurrency like Hamster Kombat Coin earlier than it officially hits the market, you want to connect to ability shoppers in locations wherein early trading occurs. Here’s how you can do it:
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Search for cryptocurrency boards, social media groups (like Discord or Telegram), or special pre-market buying and selling structures wherein new crypto cash are traded. You can search for forums or companies that focus on new or lesser-acknowledged coins.
Join the Right Communities: If you are no longer already a member, be a part of those groups. Be active, share helpful statistics, and display which you recognize your stuff.
Post Your Offer: Once you experience comfortable and feature come to be a acquainted face, put up your offer to sell Hamster Kombat Coin. Be honest about how plenty you have got and the price you need.
Hamster kombat free money Withdraw Easy free $500 mo

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- 2. Non-parametric statistics test Non-parametric statistics is the branch of statistics. It refers to a statistical method in which the data is not required to fit a normal distribution. Nonparametric statistics uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. For example: a survey conveying consumer preferences ranging from like to dislike would be considered ordinal data. Nonparametric statistics does not assume that data is drawn from a normal distribution. Instead, the shape of the distribution is estimated under this form of statistical measurements like descriptive statistics, statistical test, inference statistics and models. There is no assumption of sample size because it’s observed data is quantitative.
- 3. This type of statistics can be used without the mean, sample size, standard deviation or estimation of any other parameters. The non-parametric test are called as “distribution-free” test since they make no assumptions regarding the population distribution. It is test may be applied ranking test. They are easier to explain and easier to understand but one should not forget the fact that they usually less efficient/powerful as they are based on no assumptions. Non-parametric test is always valid, but not always efficient. Types of Non-parametric statistics test Rank sum test Chi-square test Spearman’s rank correlation
- 4. Rank sum test Rank sum tests are U test (Wilcoxon-Mann-Whitney test) H test (Kruskal-Wallis test) U test: It is a non-parametric test. This test is determine whether two independent samples have been drawn from the same population. The data that can be ranked i.e., order from lowest to highest (ordinal data).
- 5. U test For example The values of one sample 53, 38, 69, 57, 46 The values of another sample 44, 40, 61, 53, 32 We assign the ranks to all observations, adopting low to high ranking process and given items belong to a single sample. Size of sample in ascending order Rank 32 1 38 2 40 3 44 4 46 5 53 6.5 53 6.5 57 8 61 9 69 10
- 6. Kruskal-Wallis H test H test: The Kruskal-Wallis H test (also called as the “one- Way ANOVA on ranks”) is a rank-based non parametric test that can be used to determine if there are statistically significant difference between two or more groups of an independent variable on a continuous or ordinal dependent variable. For example: H test to understand whether exam performance, measured on a continuous scale from 0-100, differed based on test anxiety levels(i.e., dependent variable would be “exam performance” and independent variable would be “test axiety level”, which has three independent groups: students with “low”, “medium” and “high” test anxiety levels).
- 7. Chi square test The chi-square test is a non-parametric test. It is used mainly when dealing with a nominal variable. The chi-square test is mainly 2 methods. Goodness of fit: Goodness of fit refers to whether a significant difference exists between an observed number and an expected number of responses, people or other objects. For example: suppose that we flip a coin 20 times and record the frequency of occurrence of heads and tails. Then we should expect 10 heads and 10 tails. Let us suppose our coin-flipping experiment yielded 12 heads and 8 tails. Our expected frequencies (10-10) and our observed frequencies (12-8). Independence: the independence of test is difference between the frequencies of occurrence in two or more categories with two or more groups.
- 8. Spearman’s rank correlation test-In this method a measure of association that is based on the ranks of the observations and not on the numerical values of the data. It was developed by famous Charles spearman in the early 1990s and such it is also known as spearman’s rank correlation co-efficient. English (marks) Maths (marks) Rank (English) Rank (maths) Difference of ranks 56 66 9 4 5 75 70 3 2 1 45 40 10 10 0 71 60 4 7 3 62 65 6 5 1 64 56 5 9 16 58 59 8 8 0 80 77 1 1 0 76 67 2 3 1 61 63 7 6 1