Content Introduction
Application Statistical Methods is based on the graduate teaching experience accumulated by edits for many years, and is prepared with reference to the basic requirements of the Ministry of Education's "Master's Application Statistics Courses". As a technological, economic, management, agronomics, doctoral degree, doctoral student and senior undergraduate student learning statistical analysis method (or application mathematical statistics) courses, can also be used as relevant disciplines and engineering technicians Reference book.
Book catalog
Chapter 1 Probability Theory Basic Basic
§1.1 Random Event and Its Probability
One, Sample Space and Random Event
II, the probability of the event
three, conditional probability and multiplication formula
four, the independence of the event
§1.2 random variable And its distribution
1. Random variables and distribution functions
two, multi-dimensional random variables and distributions thereof
3, random variable function distribution
§1.3 Random variables
1. Mathematical expectations
two, variance
three, random variable "Standardization" and moment
4, mathematical expectations and variance of common distribution and variance
5, covariance and correlation coefficient
6, multi-dimensional random variable digital characteristics
1.4 Extremely limited preliminary
1. Convergence of random variable sequence
two, large law
three, central extreme limit
Exercise 1 / P>
Chapter 2 Mathematical Basic Concepts and Sampling Distribution
2.1 Mathematical Statistics Basic Concept
1, Overall and Samples
2, statistics
2.2 Experience distribution function and histogram
1. Experience distribution function
two, histogram
< P> §2.3 Common Probability Distribution1, X2 Distribution
2, T Distribution
3, F Distribution
four, Position point of probability distribution
§2.4 Sampling Distribution
1, the normal sample distribution
2, some sampling distribution of non-state / p>
exercises 2
Chapter IV parameter estimate
3.1 estimate
1, mission method
two , Greatly likelihood estimation method
three, Bayes estimation
§3.2 Evaluation criteria
1, non-boreII, effectiveness
three, consistency
§3.3 estimation
§3.4 Regional overall parameters estimate
< P> First, the overall situationtwo, the overall situation
3.5 Assembly of non-normal parameters Estimate
1, index distribution parameters Interval estimated
2, 0-1 Interval estimation of parameters
3.6 single-side confidence interval
exercises three
Four chapters hypothesis test
4.1 Assumption test Basic concept
1. Problem proposing
II, assumptioning the basic principle
Third, assume the two categories of test Error
4, hypotheses the general step of the test
4.2 hypothetical inspection of a single normal overall parameter
1. Assumption test of a single normal overall mean < / p>
two, the hypothetical assay of single normal variance
4.3 hypothetical test of two normal overall parameters
1, two normal overall mean Assumption test
two, the hypothetical assay of two normal variance
4.4 Assumption test of the non-state overall parameter
1, index distribution parameters Assumption test
two, 0-1 distribution parameters hypothesis test
exercise four
Chapter 5 regression analysis
5.1 Analysis
1. Simple correlation coefficient
2, the inspection of the correlation coefficient5.2 Linear regression model
1.
II, the basic concept of regression analysis
three, linear regression model
5.3 minimum multiplier estimate and its nature
one , Least squares estimate
two, one-unit linear regression
three, minimum multiplier estimation properties
5.4 regression equation and regression coefficient test
1.
2, the F Test of the Regression Equation
Third, the Significant Inspection of Regression Coefficients
5.5 Due to Variables Prediction
1. Point prediction
two, interval prediction
§5.6 self-variable selection and gradual return
one, self Criteria
2, select the optimal regression equation
three, gradually return
§5.7 nonlinear regression
one, Larid nonlinear model
two, general nonlinear regression model
exercise five
Chapter 6 Non-parameter statistics preliminary
< P> 6.1 Non-parametric hypothesis test1, distribution function combination verification
2, the independent test of the listing table
three, consistency Inspection
4 Parameter regression model
exercise six
Seventh chapter variance analysis and orthogonal test design
7.1 single factor variance analysis
one , Single factor test
two
1. Double-factor analysis of interaction
2, no interaction of biplomerative variance analysis
7.3 is trying Design
1, 正 交 交表
2, orthogonal test without interaction
three, interacting orthogonal test
Exercise Seven / P>
Chapter 8 Multivatical Analysis
8.1 Multi-Wide Radio Variable
1. Multi-Wide Radio Variable
Two , Multi-normal distribution
three, sampling and statistics
four, parameter estimation
8.2 discriminant analysis and cluster analysis
1. Distance
2 The principle of analysis
2, the sample main component calculation step
exercise eight
Appendix I common distribution parameters, estimated quantity and digital features - Overview P>
Appendix II Common distribution table
Schedule 1 Poisson distribution table
Schedule 2 Standard normal distribution table
Schedule 3t distribution Upper point table
Schedule 4X2 distribution upper point table
Schedule 5F distribution upper point table
Schedule 6 Related Coefficient Test Table
Schedule 7 Symbol Table
Schedule 8 Symbol Rank and Table
Appendix III 正 交
exercise answer