Home > Current Product > Probability model software

Probability theory

Chapter one - The basis of probability distribution simulator

Unit Pages
1 The introduction of probability distribution simulator. 001~001
2 The basic probability distribution name and probability density function.This program has 60 basic probability distribution 002~009
3 The basic probability distribution simulator. 010~089
4 Using the simulation data base to get the estimated function of distribution function, probability density function and the random variable value 090~135

Chapter two - The marginal probability distribution

Unit Pages
1 The introdution. 001~001
2 The marginal probability distribution transformation. 002~219

Chapter three - The two random variables joint probability distribution

Unit Pages
1 The introduction. 001~002
2 The two random variables are iid the special probability distribution. 003~174
3 The two random variables are independent and same probability distribution, but the parameters are different. 175~362
4 The two random variables are independent and different probability distribution. 363~427
5 The two random variables are independent in a given special range. The conditional marginal probability distribution and the joint conditional probability distribution. 428~665
6 There are two dependent random variables. One is priority probability distribution and the other one is conditional probability distribution which parameters is the function of the priority probability distribution. 666~924
7 Bi-vairate Normal distribution. 925~955

Chapter four - The three or more random variables joint probability distribution

Unit Pages
1 The introdution. 0001~0003
2 The three random variables are iid the special probability distribution. 0004~0143
3 The three random variables are independently and different probability distribution. 0144~0182
4 The three random variables are iid the special probability distribution and give a special range. 0183~0220
5 The three random variables are dependently, one is the marginal probability distribution and the others are conditional probability distributions. 0221~0273
6 The three random variables are dependently, one is the marginal probability distribution and the others are conditional probability distributions. The range of random variables is given a special region. 0274~0388
7.1 The four and more random variables are independently and identically distributed a same probability distribution. n=4 0389~0418
7.2 The four and more random variables are independently and identically distributed a same probability distribution. n=10 0419~0537
7.3 The four and more random variables are independently and identically distributed a same probability distribution. n=20 0538~0565
8.1 The four and more random variables are independently and each has a individual probability distribution.n=4 0566~0641
8.2 The four and more random variables are independently and each has a individual probability distribution.n=10 0642~0684
8.3 The four and more random variables are independently and each has a individual probability distribution.n=20 0685~0712
9.1 The four and more random variables are dependently and each has a individual probability distribution.n=4 0713~0782
9.1.7

9.1.8
X1,X2,X3,X4 are dependent random variables. The variance-covariance matrix is known. 0783~1114
9.2 The four and more random variables are dependently and each has a individual probability distribution. n=10 1115~1152
9.3 The four and more random variables are dependently and each has a individual probability distribution. n=20 1153~1197

Chapter five - The Moment of random variables

Unit Pages
1 The introdution. 001~001
2 The computation of moment 002~074
3 correlation coefficient 075~102
4 EE(X2|X1)=E(X2),EE(X3|X1,X2)=E(X3), 103~125
5.1 The sample number is random variable. N~Poisson(lamda=1) 126~287
5.2 The sample number is random variable. N~Binomial(n=10,p=0.8) 288~357
6 gamble---gambler ruin , collecting coupons and the discrete random variable parameter has a priority probability distribution. 358~512

Chapter six - The limiting theory

Unit Pages
1 The theory of law of large number. 001~001
2 Central limit theorem.
 2.1 The sample mean.
 2.2 The sample summation.
002~003
003~059
060~141
2

Central limit theorem.
 2.3 The sample variance.

 2.4 The sample median.
 2.5 student distribution and F distribution

142~221
222~235
236~268
3 The one sample deleted the effect of parameter effect 269~294
4 The one sample deleted the effect of parameter effect 295~397
5

The maximum of samples that is transferred to the exponential.

398~401
6 The minimum of samples that is transferred to the exponential 402~405
7 X1,…,Xn iid ~B(1,p), the discrete random samples functiontransfers to the continuous random variables. 406~408
8 Population mean is zero and Sample variance is replaced by the sample variance. 409~414

Chapter seven - The order statistic

Unit Pages
1 The introdution. 001~001
2 X1,…,Xn iid~f(x) 001~001
2.1 X1,…,X10 iid~U(alpha=-1,beta=1) 002~064
2.2 X1,…,X10 iid~Normal(mu=50,sigma*sigma=4) 065~127
2.3 X1,…,X10 iid~Shifted exponential(lamda=5,c=1) 128~190
2.4 X1,…,X10 iid~Pareto1 (lamda=5,c=1) 191~216
2.5 X1,…,X10 iid~Pareto2 (lamda=5,c=1) 217~242
2.6 X1,…,X10 iid~Rayleigh (lamda=5,c=0) 243~269
2.7 X1,…,X10 iid~DE (lamda=5,c=0) 270~295
2.8 X1,…,X10 iid~Log_normal (mu=1,sigma*sigma=0.25) 296~321
2.9 X1,…,X10 iid~Gamma (alpha=5,beta=2) 322~347
2.10 X1,…,X10 iid~Beta(alpha=5,beta=2) 348~373
2.11 X1,…,X10 iid~Cauchy (mu=0,sigma=1) 374~379
2.12 X1,…,X10 iid~Arcsin (mu=0,c=1) 380~405
3 Xi~f(xi),i=1,2,…,n, X1,X2,…,Xnare independent r.v.'s. 406~457
4 Xi~f(xi),i=1,2,…,n, X1,X2,…,Xnare dependent r.v.'s. 458~496

Chapter - eight The probability model designed

Unit Pages
1 The probability distribution from the mathematical equation. 001~018
2 The Durbin Watson test of the multiple regression. 019~032
3 moving average 033~229
4 moving standard deviation 230~263
5 The depepndeent random variables series. 264~351

Chapter - nine Statistical test statistic and the critical value table

Unit Pages
1 one way ANOVA (analysis of variance) 001~111
2 The two correlation normal population variances test statistic. 112~167
3 The two correlation random variables which one is the uniform probability distribution and the other is normal probability distribution, The both of population variances test statistic. 168~223
4 The two correlation random variables which one is the shifted exponential probability distribution and the other is normal probability distribution, The both of population variances test statistic. 224~279
5 The two correlation random variables which one is the arcsin probability distribution and the other is normal probability distribution, The both of population variances test statistic. 280~335
6 The correlation coefficient of bivariate normal distribution and the transformation of 0.5*ln((1+r)/(1-r)). The random variable is X andthe new pdf of 0.5*ln((1+X)/(1-X)). 336~461
7 The Durbin Watson test in the multiple regression model. 462~474
8 The simple regression ANOVA critical value. 475~597

Chapter - ten The coefficient of random variable

Unit Pages
1 The sample MAD 001~016
2 The sample skewed coefficient 017~025
3 The sample kurtosis coefficient. 026~035
4 The sample C.V.( coefficient of variation). 036~089
5 The sample correlation coefficient. 090~110

Chapter eleven - The Bayesian analysis

Unit Pages
1 The parameter probability distribution is uniform and the population mean is affected by the uniform distribution. Using the sample mean to estimated population mean. 001~107
2 The parameter probability distribution is gamma and the population variance is affected by the gamma distribution. Using the sample variance to estimated population variance. 108~131
3 The parameter probability distribution are uniform and gamma and the population mean is affected by the uniform distribution the population variance is affected by the gamma distribution. Using the sample variance to estimated population mean And population variance. 132~184
4 The special model discussed. 185~198

Applied Statistics

Chapter one - The descriptive statistics

Unit Pages
1.1 The sample data set coefficient in directly. 001~008
1.2 The frequency distribution to analysis data set. 001~008

Chapter two - One normal population analysis

Unit Pages
1 The population mean analysis 001~020
2 The population variance analysis 021~038

Chapter three - Two normal populations analysis

Unit Pages
1 Two independent population means analysis 001~058
2 Two independent population variances analysis 059~063
3 Two dependent population means analysis 064~078

Chapter four - experimental design

Unit Pages
1 one way analysis 001~121
2 Two way analysis 122~255
3 Two way & duplication analysis 256~515
4 One way & repeated measures analysis 516~526
5 Latin square analysis 527~561
6 Three way analysis 562~619

Chapter five - regression analysis

Unit Pages
1 simple linear model 001~133
2 multiple regression analysis about two independent variables 134~681
3 multiple regression analysis 682~1035

Chapter six - goodness of fit test

Unit Pages
1 The pearson chi square test statistic ----The probability of class is known in the first and the frequency table is established by the known probability. 001~062
2 The pearson chi square test statistic ----The probability of class is known in the first and the frequency table is established by the known probability. The estimated value is the best. 063~124
3 The pearson chi square test statistic ----The frequency table is established by the tradition method. 125~186
4 The pearson chi square test statistic ----The frequency table is established by the tradition method. The estimated value is the best. 187~248
5 The K-S method . 249~290
6 The P-P plot method. 291~331
7 The Q-Q plot method. 332~372
8 The likelihood ratio test statistic ----The probability of class is known in the first and the frequency table is established by the known probability. 373~434
9 The likelihood ratio test statistic ----The frequency table is established by the tradition method. 435~496
10 The estimated function of the relative frequency table. 497~688

Chapter seven - Discrete type data test

Unit Pages
1 1.1 goodness of fit test
1.2 independent test
1.3 homogeneous test
1.4 One population proportion test ,W.R.
1.5 One population proportion test ,W.O.R.
1.6 Two independent population proportion test ,W. R.
1.7 Two independent population proportion test ,W.O.R.
1.8 Two dependent population proportion test ,W..R.
001~090