Statistics; Probability, Inference, and Decision, Volume 2 |
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Page 11
... possible in principle , by far the most studied is the bivariate normal distribution , an example of which is ... possible X values , of Y values , and of possible joint ( X , Y ) events , is infinite , then within any possible row of ...
... possible in principle , by far the most studied is the bivariate normal distribution , an example of which is ... possible X values , of Y values , and of possible joint ( X , Y ) events , is infinite , then within any possible row of ...
Page 42
... possible using a linear prediction rule . However , there is a difficulty with this model and the test it affords : the model itself may be wrong , as in a situation where almost perfect prediction is possible using some rule , but the ...
... possible using a linear prediction rule . However , there is a difficulty with this model and the test it affords : the model itself may be wrong , as in a situation where almost perfect prediction is possible using some rule , but the ...
Page 172
... possible combination of factor levels is observed . It is also possible to consider ex- perimental designs in which only some of the possible combinations are observed . If there are several factors , each with a number of treatment ...
... possible combination of factor levels is observed . It is also possible to consider ex- perimental designs in which only some of the possible combinations are observed . If there are several factors , each with a number of treatment ...
Contents
Regression and Correlation | 1 |
Sampling Theory Experimental Design and Analysis of Variance | 92 |
4 | 102 |
Copyright | |
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Common terms and phrases
analysis of variance apply association assumptions B₁ bivariate normal cell chi-square class intervals cluster sampling column computational consider correlation coefficient degrees of freedom deviations discuss E. S. Pearson E(MS equal error terms estimated regression line example expected frequencies experiment F test factors function given groups independent variable inferences interaction effects Kolmogorov-Smirnov test large samples linear model linear regression matrix mean square multiple regression N2 VN nonlinear normal distribution null hypothesis number of observations pairs parameters particular population distribution possible predicted value probability random variables random-effects model rank regression curve regression model regression problems sample mean sampling distribution sampling plan scores Section simple random sampling SS total statistical statistician stratified sampling sum of squares Suppose test the hypothesis tion treatment effects type of packaging x2 test Y₁ zero Σ Σ ΣΣ ΣΥ ΣΧ