This is a review of Statistic

1. A Review Of Statistic

2. DescriptiveStatisticThistype of statisticis used to describe the main features of a collection of data in quantitative terms. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aim to quantitatively summarize a data set, rather than being used to support inferential statements about the population that the data are thought to represent. Even when a data analysis draws its main conclusions using inductive statistical analysis, descriptive statistics are generally presented along with more formal analyses, to give the audience an overall sense of the data being analyzed. An example of the use of descriptive statistics occurs in medical research studies. In a paper reporting on a study involving human subjects, there typically appears a table giving the overall sample size, sample sizes in important subgroups (e.g. for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of subjects with each gender, and the proportion of subjects with related comorbidities.

3. InferentialStatisticItcomprises the use of statistics and random sampling to make inferences concerning some unknown aspect of a population. It is distinguished from descriptive statistics.There are several distinct schools of thought about the justification of statistical inference. All are based on some idea of what real world phenomena can be reasonably modeled as probability.1.Frequency probability.2.Bayesian probability. 3.Fiducial probability.

4. RegressionRegression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.Ithelps us understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed and 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 held fixed.

5. RegressionRegression analysis is widely used for prediction and it has substantial overlap with the field of machine learning.Regression models: Regressin models involve the following variable:The unknown parameters denoted as β; this may be a scalar or a vector of length k.The independent variables, X.The dependent variable, Y.A regression model relates Y to a function of X and β.

6. RegressionTwotypes.Linear regression: in linear regression, the model specification is that the dependent variable, yi is a linear combination of the parameters (but need not be linear in the independent variables).

7. RegressionNonlinearregression:When the model function is not linear in the parameters, the sum of squares must be minimized by an iterative procedure.