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    Introduction To Non Parametric Methods Through R Software
    Introduction To Non Parametric Methods Through R Software
    Introduction To Non Parametric Methods Through R Software
    Ebook101 pages39 minutes

    Introduction To Non Parametric Methods Through R Software

    By Editor IJSMI

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    About this ebook

    Statistical Methods are widely used in Medical, Biological, Clinical, Business and Engineering field. The data which form the basis for the statistical methods helps us to take scientific and informed decisions. Statistical methods deal with the collection, compilation, analysis and making inference from the data.

    The book mainly focuses on non-parametric aspects of Statistical methods. Non parametric methods or tests are used when the assumption about the distribution of the variables in the data set is not known or does not follow normal distribution assumption. Non parametric methods are useful to deal with ordered categorical data. When the sample size is large, statistical tests are robust due to the central limit theorem property. When sample size is small one need to use non-parametric tests. Compared to parametric tests, non-parametric tests are less powerful i.e. if we fail to reject the null hypothesis even if it is false. When the data set involves ranks or measured in ordinal scale then non-parametric tests are useful and easy to construct than parametric tests.

    The book uses open source R statistical software to carry out different non-parametric statistical methods with sample datasets.  

    LanguageEnglish
    PublisherIJSMI
    Release dateOct 1, 2022
    ISBN9798215164259
    Introduction To Non Parametric Methods Through R Software
    Author

    Editor IJSMI

    Editor, International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php [email protected]

    Read more from Editor Ijsmi

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      Book preview

      Introduction To Non Parametric Methods Through R Software - Editor IJSMI

      Chapter 1 - Introduction

      (S ome of the contents are adopted from Author’s previous book – Introduction to Statistical Methods)

      Statistics is defined as collection, compilation, analysis and interpretation of data to draw meaningful conclusions and to make informed & scientific decisions.

      Statistical methods help us to answers the commons questions like the following

      How much percentage of males contributes the Population of United States

      What is daily average sales figure of online site

      What is the mortality rate for cancer disease last year

      What is defects rate of items manufactured today

      Is there any difference in survival rate of two treatments of cardiovascular disease

      Which of the two drugs is better in curing the tuberculosis disease

      What will be price of company A stock after one month

      What is the chance that Brazil football team winning the football world cup

      Is there any association between cigarette smoking and lung cancer

      What is the size of male foot ware which needs to be produced more in USA

      The following chapters of the books help the readers to answer the questions like the ones mentioned above with the use of statistical methods, tools and techniques.

      Basic Concepts

      Data

      Data is the most basic form of information and it is present in the form of numbers, text, image and sound.

      Variables

      Variables are quantities whose values vary according to defined rule or scope. For example, age of a person can be defined by the term or variable Age and it can take the numeric values ranging from say 0 to 120 or gender of a patient can be represented by the term or variable gender and it can take text values such as Male or Female in a dataset.

      Constants

      Constants are quantities which can take a constant value in a dataset. Example - Normal body temperature

      Population

      Population is a collection of all possible data related to a specific parameter or point of interest. The population can be definite in numbers or countable or indefinite in number or non-countable. Examples of population are student population of New York, cancer patient population, or population of stars in the Universe.

      Sample

      A subset drawn from the given population is called as sample. Examples of sample - sample of 100 cancer patient drawn from the cancer population or a student sample of size 100 drawn from the student population of New York. 

      Measurement Scales

      Data can be measured in the nominal, ordinal, interval and ratio scales

      Nominal Scale

      Nominal scales measurement is used when the data provides information about characteristic or name of an entity (variable) which is being measured. Example of nominal scale measurements are name of the patient, place of birth etc.  It cannot be useful for further mathematical treatment (addition, subtraction etc.) as it lacks relative or ratio property.

      Ordinal scale

      Ordinal Measurement scale is used when the data indicates only the order or rank and it cannot be useful for further mathematical treatment as it lacks relative or ratio property. Difference between rank 7 and rank 8 is not same as difference between 1 and 2.  Example of ordinal measurement is ranking of treatment or drug in terms of its efficacy.

      Interval scale

      Interval measurement scale is used when the data is used to represent the relative property between the values not the ratio

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