Examination objectives:

National Unified Entrance Examination for Postgraduates: Statistics Examination for Postgraduates in Applied Statistics is an optional subject for universities and research institutes to recruit postgraduates in Applied Statistics.

Its purpose is to scientifically, fairly and effectively test whether candidates have the basic quality, general ability and training potential necessary for studying for a master's degree in applied statistics, so as to select outstanding talents with development potential for admission.

Cultivate high-level, applied and compound statistical professionals with good professional ethics, legal concept and international vision, and strong ability to analyze and solve practical problems for national economic construction. Examination requirements are some basic statistical methods for collecting, processing and analyzing the data mastered by candidates.

The requirements for candidates are:

1, master the basic methods of data acquisition and processing.

2、? Master the principle of blonde hair and data analysis methods.

3、? Mastered the basic knowledge of probability theory.

4. Have the basic ability to analyze and interpret data by statistical methods.

Statistics:

1. Organization and implementation of investigation.

2、? Probabilistic sampling and non-probabilistic sampling.

3、? Data preprocessing.

4、? Display qualitative data with charts.

5、? Display quantitative data with graphs.

6、? Describe the levels of data by statistics: average, median, quantile and mode.

7、? Describe the differences of data by statistics: range, standard deviation and sample variance.

8、? Basic principles of parameter estimation.

9. Interval estimation of parameters of one population and two populations.

10、? Determination of sample size.

1 1. Basic principles of hypothesis testing.

12, parameter test of one population and two populations.

13, the basic principle of variance analysis.

14, the realization of one-factor and two-factor analysis of variance and the interpretation of the results.

15, the relationship between variables, the difference between correlation and function.

16, estimation and test of linear regression.

17, using residual to test the hypothesis of the model.

18, multiple linear regression model.

19, goodness of fit and significance test of multiple linear regression.

20, multiple * * * linear phenomenon.

2 1, the component of time series.

22, time series prediction method.

Probability theory:

1, event, relation, operation.

2. Probability of events.

3、? Conditional probability and total probability formulas.

4. Definition of random variables.

5. Distribution list and distribution function, discrete uniform distribution, binomial distribution and Poisson distribution of discrete random variables.

6. Probability density function and distribution function, uniform distribution, normal distribution and exponential distribution of continuous random variables.

7. Expectation and variance of random variables.

8. Expectation and variance of random variable function.