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LOGISTIC REGRESSION

 INTRODUCTION: It is a statistic method that is used for building machine learning models where the dependent variable is binary. It is used to describe data and the relationship between one dependent variable and one or more independent variables. The name "logistic regression" is derived from the concept of the logistic function that is uses. The logistic function is also known as the sigmoid function. The value of this logistic function lies between zero(0) and one(1). It is Supervised Classification Algorithm. The value of the logistic regression must be between 0 and 1, which cannot go beyond this limit, so it forms a curve like the "S" form. The S-form curve is called the Sigmoid function or the logistic function.   LOGISTIC REGRESSION EQUATION: The Logistic regression equation can be obtained from the Linear Regression equation. The mathematical steps to get Logistic Regression equations are given below: The equation of the straight line can be written as:   ...
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STATISTICS IN DATA SCENCE





 
INTRODUCTION:

 

Statistics is a mathematical analysis that uses quantified models and representation for a given set of experimental data or real-life studies. Statistics is a science of data. This is the science of conducting studies of collect, organize, summarize, analyze and draw conclusion from data. It is a mathematical science pertaining to the collection, analysis, interpretation or explanation and presentation of data.

Data may be classified as either quantitative or qualitative. Quantitative data measures how much or how many of something , and qualitative data provide labels, or names, for categories of like items.   

    

  TYPES OF STATISTICS:

  Statistics have majorly categorized into two types:

     1. Descriptive Statistics: Descriptive statistics is a way to organized, represent and describe a collection of data using tables, graphs, and summary measures. Descriptive statistics are also categorized into four different categories:

  • Measures of frequency
  • Measures of dispersion( Range, Variance, Standard Deviation, etc.)
  • Measures of central tendency( Mean, Median, Mode)
  • Measures of position
     2. Inferential Statistics: Inferential statistics is a method that allows us to use information collected from a sample to make decisions, predictions or inferences from a population. It grants us permission to give statements that goes beyond the available data or information.

IMPORTANCE OF STATISTICS:

  • Statistics executes the work simply and gives a transparent picture of the work we do regularly.
  • The statistics methods helps us to examine different areas such as medicines, business, economics, social science and others.
  • Statistics equips us with different kinds of organized data with the help of graphs, tables, diagrams and charts.
  • Statistics helps to understand the variability of the data pattern in a quantitative way.
  • Statistics makes us understand the bulk of data in a simple way.
  • Statistics is the way to collecting accurate quantitative data.


 

    

 












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