MATHEMATICAL SCIENCE STATISTICAL INFERENCE 5 UGC CSIR NET CBSE NET скачать в хорошем качестве

MATHEMATICAL SCIENCE STATISTICAL INFERENCE 5 UGC CSIR NET CBSE NET

MATHEMATICAL SCIENCE STATISTICAL INFERENCE 5 UGC CSIR NET VISIT OUR WEBSITE https://www.souravsirclasses.com/ FOR COMPLETE LECTURES / STUDY MATERIALS /NOTES /GUIDENCE / PAST YEAR SOLVED +SAMPLE PAPAERS /TRICKS /MCQ / SHORT CUT/ VIDEO LECTURES /LIVE + ONLINE CLASSES GIVE US A CALL / WHATSAPP AT 9836793076 Also find us at…. BLOGSPOT http://souravdas3366.blogspot.com/ SLIDES ON COURSES https://www.slideshare.net/Souravdas31 TWITTER   / souravdas3366   FACEBOOK   / sourav-sirs-.  . LINKED IN   / sourav-da.  . GOOGLE PLUS https://plus.google.com/+souravdassou... Statistical science is concerned with the planning of studies, especially with the design of randomized experiments and with the planning of surveys using random sampling. The initial analysis of the data from properly randomized studies often follows the study protocol. The data from a randomized study can be analyzed to consider secondary hypotheses or to suggest new ideas. A secondary analysis of the data from a planned study uses tools from data analysis. Data analysis is divided into: descriptive statistics - the part of statistics that describes data, i.e. summarises the data and their typical properties. inferential statistics - the part of statistics that draws conclusions from data (using some model for the data): For example, inferential statistics involves selecting a model for the data, checking whether the data fulfill the conditions of a particular model, and with quantifying the involved uncertainty (e.g. using confidence intervals). While the tools of data analysis work best on data from randomized studies, they are also applied to other kinds of data --- for example, from natural experiments and observational studies, in which case the inference is dependent on the model chosen by the statistician, and so subjective.[3] Mathematical statistics has been inspired by and has extended many options in applied statistics. Special distributions[edit] Normal distribution (Gaussian distribution), the most common continuous distribution Bernoulli distribution, for the outcome of a single Bernoulli trial (e.g. success/failure, yes/no) Binomial distribution, for the number of "positive occurrences" (e.g. successes, yes votes, etc.) given a fixed total number of independent occurrences Negative binomial distribution, for binomial-type observations but where the quantity of interest is the number of failures before a given number of successes occurs Geometric distribution, for binomial-type observations but where the quantity of interest is the number of failures before the first success; a special c*Discrete uniform distribution, for a finite set of values (e.g. the outcome of a fair die) Continuous uniform distribution, for continuously distributed values Poisson distribution, for the number of occurrences of a Poisson-type event in a given period of time Exponential distribution, for the time before the next Poisson-type event occurs Gamma distribution, for the time before the next k Poisson-type events occur Chi-squared distribution, the distribution of a sum of squared standard normal variables; useful e.g. for inference regarding the sample variance of normally distributed samples (see chi-squared test) Student's t distribution, the distribution of the ratio of a standard normal variable and the square root of a scaled chi squared variable; useful for inference regarding the mean of normally distributed samples with unknown variance (see Student's t-test) Beta distribution, for a single probability (real number between 0 and 1); conjugate to the Bernoulli distribution and binomial distribution, In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution.