__SSC CGL Posts Preparation: Statistics__

**SSC CGL 2020 preparation **for statistics** **includes conceptual knowledge of the subject. It is not mandatory subject for all candidates. Mock tests in English and **Hindi** **for competitive exams** will do a great help to clear the concepts and practice regularly.

__SSC Preparation: Moments, Skewness and Kurtosis__

**Moments: **It is characteristic of distribution.

· This statistical term is used to define moments of forces in physics.

· These are the constant value which gives idea about type of distribution and its nature.

· It is denoted by µ.

**Skewness: **It is lack of symmetry in distribution.

· Here values of mean, median and mode are not same.

· Skewness is either positive or negative

**Positive Skewnes**s: where value of mean is larger than mode. Here median lies between mean and mode.

**Negative Skewness**: Where value of mode is larger than mean. Here also median lies between mean and mode.

**Kurtosis: **It describes in a frequency curve.

· It shows the degree of concentration either peaked or flat region of a frequency curve.

· This concept is not used to analyze business data generally.

__SSC Preparation: Correlation and Regression__

These two are used to analyze distribution of multiple variable or multivariate distributions. Both used to quantify the direction and strength of the relationship between two numerical values.

**Correlation: **It is a statistical term to determine the association between two variables.

· It represents linear relation.

· It gives a numerical value which shows relationship between two variables.

· The degree of association is measured by a coefficient called correlation coefficient (r).

· On the name of its originator it is also called Pearson's correlation coefficient.

**Regression: **It describes how an independent variable is numerically related to a dependent variable.

· Here both variables are different.

· It is used to find the value of random variables on the basis of a given fixed variable.

· It produces an equation.

· In simple linear regression, the model used to describe the relationship between a single dependent variable y and a single independent variable.

Example: y= a + bx; where x is an independent variable and y is a dependent variable.

__SSC Preparation: Random Variables__

· It is a variable whose values are numerical outcomes of a random phenomenon.

· A random variable is required to be measurable

· Its mathematical application is in probability theory.

· There are two types of random variables present called discrete and continuous.

**Discrete Random Variable: **It takes finite or countable number of distinct values into account like students in a class. Example: 0, 1, 2….n.

· It is defined at specific value.

· It allows the computation of probabilities for individual integer values.

· Possible values of the random variables are then treated as a sample space.

**Continuous Random Variable: **It takes indefinite number of values for measurements.

· These are the variables whose cumulative distribution function is continuous everywhere.

· It is defined over an interval.

· It is represented by area under a curve which is mathematically known as integral.

These are few simple last minute revision roundups for **SSC-CGl2020.**

**All the best for SSC-CGL2020**