Statistics; Probability, Inference, and Decision, Volume 2 |
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Page 20
... given a value of the other variable . For example , suppose that he wants to predict the sales of a product , given the price of the product . In this case we say that price is the independent variable and that the other variable ...
... given a value of the other variable . For example , suppose that he wants to predict the sales of a product , given the price of the product . In this case we say that price is the independent variable and that the other variable ...
Page 61
... given by Equation ( 10.20.2 ) ? The first coefficient represents the expected value of Y given that all of the independent variables are equal to zero . For j≥ 2 , the coefficient 8 ; represents the expected change in Y due to a one ...
... given by Equation ( 10.20.2 ) ? The first coefficient represents the expected value of Y given that all of the independent variables are equal to zero . For j≥ 2 , the coefficient 8 ; represents the expected change in Y due to a one ...
Page 109
... given a placebo , which is similar in appearance to the three drugs but contains no medication ( this makes the conditions in the four groups appear more nearly identical to the patients than if nothing was given to the fourth group ) ...
... given a placebo , which is similar in appearance to the three drugs but contains no medication ( this makes the conditions in the four groups appear more nearly identical to the patients than if nothing was given to the fourth group ) ...
Contents
Regression and Correlation | 1 |
Sampling Theory Experimental Design and Analysis of Variance | 92 |
4 | 102 |
Copyright | |
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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