What is Statistics Homework Help? |
What is Statistics?
Statistics as a subject is principally the learning wherein which we bring together, investigate, understand and present the data. It can also be effectively seen that the data with which statistics deals admits preparation of the data being accumulated. There rests two of the mythologies wherein statistics and they are inferential and descriptive statistics. Few people reflect on statistics as the branch of science and remaining considers it as a separate mathematical science.
Statistical Methods
All the time more, the statistical methods are being utilized in an assortment of fields of research. Statistical methods largely fall into four categories:
- Descriptive
- Inferential
- Applied
- Analytical
- Inductive
Functions and Limitations of Statistics
Functions:
- To present the information in a proper tabular, diagrammatical and vivid form.
- Make simpler intricate data by making it without difficulty understandable.
- To help in categorization of data.
- To make available with the techniques of arriving at comparability’s.
- To put together policies in dissimilar schemes.
- Point toward trend behavior.
- To compute vagueness.
- Testing assumption.
- To draw legitimate inferences.
Limitations:
- Statistics do not study individuals.
- There is no study of qualitative phenomenon.
- Statistical results drawn are based truly on average.
- In this, laws are not accurate.
- Statistics does not disclose the entire story.
- Statistics is known to be the collection with an agreed point and cannot be at random used to any situation.
Areas of Statistics
- Analysis of Variance (ANOVA): ANOVA is mostly expended so as to weigh up unpredictability of plots in receipt of miscellaneous action to that of plots getting the same treatments. It assists the statisticians to do the psychoanalysis and explanation of their surveillance from the collection of populations.
- Bivariate Regression: It is agreed to be the simplest regression in which there is one rejoinder or needy variable, and one forecaster or autonomous variable, and the connection between the two is constituted by a straight line.
- Chi-Square Test: Chi-square (I) test used to settle on whether there is a noteworthy disparity in between the anticipated and the experiential frequencies in one or more categories.
- Data Collection: This is the process of collecting, classifying and storing data. The most important principle behind data compilation is to get hold of information and carry on with it on confirmation with the outlook of studying, analyzing or by means of it in future course of time.
- Descriptive Statistics: Descriptive statistics is utilized so as to quantitatively portray the key characteristics of data collection. It is used to sum up a data set and as a result, its expansion does not depend on probability theory.
- Discrete Distributions: If a random variable is a discrete variable, its probability distribution is named a discrete distribution.
- Nonparametric Tests: Non-parametric inferential statistical methods are mathematical actions for statistical hypothesis testingwhich build no assumptions on the subject of the probability distributions of the variables being evaluated.
- Probability: It is used in connections with annotations that can be recurring as an infinite number of times underneath in effect to the same conditions. As per a numerical measure of vagueness, it is provided by a very essential branch of statistics called up as the ‘Theory of Probability’.
- Regression: Regression analysis calls for the techniques for modeling and probing into plentiful variables with a meeting point on the connection amid a dependent variable and one or more independent variables.
- Time series analysis: It is all about data sets which are framed up of analogous measurements in use at habitual intervals over time that are used to keep an eye on industrial processes or tracking of the corporate business metrics, sociology, finance, economics, medicine and any other state of affairs in which the user desires to learn or investigate a comparable compute over a time period.
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