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Introduction to Statistics

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Chapter 1 : Introduction to Statistics

Introduction to Statistics arrow_upward

  • Statistics is a branch of mathematics that deals with the collection, organization, and interpretation of data.
  • It is a method of analyzing data.
    • A collection of methods for planning experimenting, obtaining data and summarizing it.
    • Drawing conclusions based on the data.
  • Statistics is a numerical measurement describing some characteristics of a sample.
  • Almost all the fields of study gets benefit from the application of statistical methods. This can include:
    • Marketing (Consumer preferences).
    • Finance (Forecasting).
    • Management (Quality improvement).

    Statistical Methods arrow_upward

  • There are two types of statistical methods:
    • Descriptive Statistics.
    • Inferential (or “analytical”) Statistics.

  • Descriptive Statistics:

  • Descriptive statistics includes statistical procedures that we use to describe the population we are studying.
  • The list of techniques used to summarize data in the form of following:
    • Mean
    • Variance
    • Standard deviation
    • Standard error
    • Median
    • Mode
    • Skew

    Inferential Statistics:

  • Inferential statistics is concerned with making predictions or inferences about a population from observations and analyses of a sample.
  • The methods of inferential statistics are:
    • Estimation of parameters.
    • Hypothesis Testing.

    Graphical Statistics arrow_upward

  • The list of techniques used to summarize data in the form of following:
    • Histogram
    • Boxplot
    • Scatterplot

    Probability Sampling arrow_upward

  • Probability is a numerical measure of the likelihood that an event will occur.
  • Probability sampling is a sampling technique where the samples are gathered in a process that gives all the individuals in the population equal chances of being selected.

  • Types of Probability Sampling arrow_upward

  • Types of Probability Sampling are as shown in the figure:

  • Simple Random Sampling:

  • Every individual or item from the frame has an equal chance of being selected.
  • Selection may be with replacement or without replacement.
    • With replacement: Measured items are returned to the frame.
    • Without replacement: Measured items are not returned to the frame.
    Advantages of Simple Random Sampling:
  • Estimates are easy to calculate.
  • The sample will be free from bias.
  • Disadvantages of Simple Random Sampling:
  • If sampling frame is large, then this method is impracticable.
  • Due to its very randomness, “freak” results can sometimes be obtained that are not representative of the population.

  • Systematic Sampling:

  • A statistical method involving the selection of elements from an ordered sampling frame.
  • Advantages of Systematic Sampling:
  • It is very simple to use.
  • It also saves time and cost.
  • It checks bias in subsequent selections of samples.
  • Disadvantages of Systematic Sampling:
  • There is the possibility of losing vital information from the population.
  • It may not be good for periodic data.

  • Stratified Sampling:

  • Stratified sampling involves dividing the population into groups and then sampling from those different groups depending on a certain set criteria.
  • Advantages of Stratified Sampling:
  • Yields more accurate results than Simple Random Sampling.
  • Disadvantages of Stratified Sampling:
  • It is more complex to organize and analyze the results compared to simple random sampling.

  • Cluster Sampling:

  • Cluster sampling is a sampling technique where the entire population is divided into groups, or clusters, and a random sample of these clusters are selected. All observations in the selected clusters are included in the sample.
  • Advantages of Cluster Sampling:
  • This sampling technique is cheap, quick and easy.
  • Disadvantages of Cluster Sampling:
  • Less efficient (need a larger sample to acquire the same level of precision).

  • Thank You from Kimavi arrow_upward

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