# Chapter 1 : Introduction to Statistics

### Topics covered in this snack-sized chapter:

#### 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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