Statistics
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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 rest 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:
1. To present the
information in a proper tabular, diagrammatical and vivid form.
2. Make simpler
intricate data by making it without difficulty understandable.
3. To help in the
categorization of data.
4. To make available
with the techniques of arriving at comparability’s.
5. To put together
policies in dissimilar schemes.
6. Point toward trend behaviour.
7. To compute
vagueness.
8. Testing assumption.
9. To draw legitimate
inferences.
Limitations:
1. Statistics do not
study individuals.
2. There is no study of
the qualitative phenomenon.
3. Statistical results
drawn are based truly on average.
4. In this, laws are
not accurate.
5. Statistics does not
disclose the entire story.
6. 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 the 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 dataset 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 testing which 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 an 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.
Continuous Distributions |
Describing Data Visually |
One-Sample Hypothesis Tests |
Sampling Distributions and
Estimation |
Statistics Homework Help (Extended) |
Two-Sample Hypothesis Tests |
Types of Statistics |
Uses of Statistics |