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| Neter J., Kutner M.H., Wasserman W. — Applied Linear Regression Models |
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| Предметный указатель |
Regression model, residual analysis for aptness 111—22
Regression model, scope of 30 261
Regression model, second-order autoregressive 460
Regression model, selection of independent variables, all possible regressions 421—429
Regression model, selection of independent variables, backward elimination 435—436
Regression model, selection of independent variables, forward selection 435
Regression model, selection of independent variables, ridge regression 436
Regression model, selection of independent variables, stepwise regression 430—435
Regression model, simple linear in matrix terms 208—210
Regression model, simple linear, error term distribution unspecified 31—35
Regression model, simple linear, normal error terms 48—49
Regression model, simple linear, through origin 160—164
Regression model, simple linear, X is random 83—84
Regression model, uses of 30—31
Regression sum of squares 86
Regression sum of squares, decompositions 284—286
Regression surface 227—228
Regression, through origin 160—164
Replication 124
Residual 43—44
Residual analysis 111—122
Residual in terms of hat matrix 220—221
Residual mean square 47
Residual plot 111—113
Residual sum of squares 47
Residual, deleted 405—406
Residual, properties of 110—111
Residual, standardized 110
Residual, studentized 405
Residual, studentized deleted 406
Residual, variance-covariance matrix of 221 402
Response 41
Response function see "Regression function"
Response surface 227—228
Response variable 25 28
Response, binary or quantal 354
Restricted model 95
Ridge regression 394—400
Ridge regression, use for selecting independent variables 436
Ridge trace 396—397
Roundoff errors in least squares calculations 377—378
Row vector 188
SAS 259
scalar 192
Scalar matrix 197—198
Scatter diagram 25
Scatter plot 25
Scheffe joint estimation procedure for inverse predictions 174
Scheffe joint estimation procedure for prediction of new observations 159—160 246
Scope of model 30 261
Second-order autoregressive error model 460
Second-order regression model 300—301 303—305 see
Selection of independent variables 29 417—419
SENIC data set 533—36
| Serial correlation 444
Simple linear regression model 31 see
Singular matrix 201
SMSA data set 537—541
SPSS 52
Square matrix 187
Standard normal variable 7
Standardized regression coefficient 261—263
Standardized residual 110
Statement confidence coefficient 150
Statistical relation 25—26
Stepwise regression selection procedure 430—435
Studentized deleted residual 406
Studentized residual 405
Sufficient estimator 9
Sum of squares 46
Sum of squares, as quadratic form 215—216
Summation operator 1—2
Symmetric matrix 196
t distribution 8
t distribution, table of percentiles 518—519
t test power function charts 528—529
Third-order regression model 302 see
Total deviation 87
Total sum of squares 85
Total uncorrected sum of squares 90
Transformations of variables 134—141
Transpose of matrix 188—189
Trial 25
Unbiased estimator 9
Unrestricted model 95
Variance inflation factor 391—393
Variance of error term 31 46—48 50
Variance of function of random variables 5—6
Variance of random variable 4
Variance, estimation of, ratio of two 17—18
Variance, estimation of, single 16
Variance, test concerning, ratio of two 18—19
Variance, test concerning, single 16—17
Variance-covariance matrix 206—207
Variance-covariance matrix of regression coefficients 216—217 220 242 263
Variance-covariance matrix of residuals 221 402
Vector 187—188
Vector with all elements 0 198—199
Vector with all elements 1 198
Vector, random 205—208
Weighted least squares 167—172 219—220 263
Westwood Company 25—26
Working — Hotelling confidence region, multiple regression 244
Working — Hotelling confidence region, simple linear regression 154—58
Working — Hotelling confidence region, use for joint estimation of mean responses 157—158 245
z' transformation 503
z' transformation, table 532
Zarthan Company 247
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