Statistical methods in agriculture and experimental biology / R. Mead, R.N. Curnow, and A.M. Hasted.
Editor: London ; New York : Chapman & Hall, 1993Edición: 2nd edDescripción: xi, 415 p. : il. ; 24 cmISBN: 0412354705 (HB); 0412354802 (PB)Tema(s): Agriculture -- Research -- Statistical methods | Agriculture -- Statistical methods | Biology, Experimental -- Statistical methodsOtra clasificación: 62P10Preface ix 1 , Introduction [1] 1.1 The need for statistics [1] 1.2 The use of computers in statistics [5] 2 Probability and distributions [9] 2.1 Probability [9] 2.2 Populations and samples [12] 2.3 Means and variances [15] 2.4 The Normal distribution [18] 2.5 Sampling distributions [21] 3 Estimation and hypothesis testing [27] 3.1 Estimation of the population mean [27] 3.2 Testing hypotheses about the population mean [28] 3.3 Population variance unknown [32] 3.4 Comparison of samples [35] 3.5 A pooled estimate of variance [37] 4 A simple experiment [41] 4.1 Randomization and replication [41] 4.2 Analysis of a completely randomized design with two treatments [43] 4.3 A completely randomized design with several treatments [46] 4.4 Testing overall variation between the treatments [49] 4.5 Analysis using a statistical package [55] 5 Control of random variation by blocking [59] 5.1 Local control of variation [59] 5.2 Analysis of a randomized block design [61] 5.3 Meaning of the error mean square [66] 5.4 Latin square designs [70] 5.5 Analysis of structured experimental data using a computer package [74] 5.6 Multiple Latin squares designs [76] 5.7 The benefit of blocking and the use of natural blocks [79] 6 Particular questions about treatments [89] 6.1 Treatment structure [89] 6.2 Treatment contrasts [94] 6.3 Factorial treatment structure [97] 6.4 Main effects and interactions [99] 6.5 Analysis of variance for a two-factor experiment [102] 6.6 Computer analysis of factorials [109] 7 More on factorial treatment structure [111] 7.1 More than two factors [111] 7.2 Factors with two levels [112] 7.3 The double benefit of factorial structure [118] 7.4 Many factors and small blocks [120] 7.5 The analysis of confounded experiments [125] 7.6 Split plot experiments [128] 7.7 Analysis of a split plot experiment [130] 8 The assumptions behind the analysis [137] 8.1 Our assumptions [137] 8.2 Normality [138] 8.3 Variance homogeneity [142] 8.4 Additivity [145] 8.5 Transformations of data for theoretical reasons [147] 8.6 A more general form of analysis [151] 8.7 Empirical detection of the failure of assumptions and selection of appropriate transformations [152] 8.8 Practice and presentation [158] 9 Studying linear relationships [161] 9.1 Linear regression [161] 9.2 Assessing the regression line [165] 9.3 Inferences about the slope of a line [167] 9.4 Prediction using a regression line [168] 9.5 Correlation 9.6 Testing whether the regression is linear [177] 9.7 Regression analysis using computer packages [178] 10 More complex relationships [183] 10.1 Making the crooked straight [183] 10.2 Two independent variables [186] 10.3 Testing the components of a multiple relationship [193] 10.4 Multiple regression [204] 10.5 Possible problems in computer multiple regression [211] 11 Linear models [213] 11.1 The use of models [213] 11.2 Models for factors and variables [214] 11.3 Comparison of regressions [220] 11.4 Fitting parallel lines [227] 11.5 Covariance analysis [233] 11.6 Regression in the analysis of treatment variation [240] 12 Non-linear models [247] 12.1 Advantages of linear and non-linear models [247] 12.2 Fitting non-linear models to data [252] 12.3 Inferences about non-linear parameters [258] 12.4 Exponential models [262] 12.5 Inverse polynomial models [267] 12.6 Logistic models for growth curves [274] 13 The analysis of proportions [277] 13.1 Data in the form of frequencies [277] 13.2 The 2x2 contingency table [278] 13.3 More than two situations or more than two outcomes [280] 13.4 General contingency tables [284] 13.5 Estimation of proportions [289] 13.6 Sample sizes for estimating proportions [294] 14 Models and distributions for frequency data [299] 14.1 Models for frequency data [299] 14.2 Testing the agreement of frequency data with simple models [300] 14.3 Investigating more complex models [302] 14.4 The binomial distribution [309] 14.5 The Poisson distribution [316] 14.6 Generalized models for analysing experimental data [323] 14.7 Log-linear models [329] 14.8 Probit analysis [336] 15 Making and analysing many experimental measurements [341] 15.1 Different measurements on the same units [341] 15.2 Interdependence of different variables [342] 15.3 Repeated measurements [346] 15.4 Joint (bivariate) analysis [356] 15.5 Investigating relationships with experimental data [367] 16 Choosing the most appropriate experimental design [373] 16.1 The components of design; units and treatments [373] 16.2 Replication and precision [374] 16.3 Different levels of variation and within-unit replication [377] 16.4 Variance components and split plot designs [381] 16.5 Randomization [384] 16.6 Managing with limited resources [386] 16.7 Factors with quantitative levels [387] 16.8 Screening and selection [389] 17 Sampling finite populations [393] 17.1 Experiments and sample surveys [393] 17.2 Simple random sampling [394] 17.3 Stratified random sampling [397] 17.4 Cluster sampling, multistage sampling and sampling proportional to size [399] 17.5 Ratio and regression estimates [400] References [403] Appendix [405] Index [413]
| Item type | Home library | Shelving location | Call number | Materials specified | Status | Date due | Barcode | Course reserves |
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Instituto de Matemática, CONICET-UNS | Libros ordenados por tema | 62 M479 (Browse shelf) | Available | A-7449 |
Incluye referencias bibliográficas (p. [403]-404) e índice.
Preface ix --
1 , Introduction [1] --
1.1 The need for statistics [1] --
1.2 The use of computers in statistics [5] --
2 Probability and distributions [9] --
2.1 Probability [9] --
2.2 Populations and samples [12] --
2.3 Means and variances [15] --
2.4 The Normal distribution [18] --
2.5 Sampling distributions [21] --
3 Estimation and hypothesis testing [27] --
3.1 Estimation of the population mean [27] --
3.2 Testing hypotheses about the population mean [28] --
3.3 Population variance unknown [32] --
3.4 Comparison of samples [35] --
3.5 A pooled estimate of variance [37] --
4 A simple experiment [41] --
4.1 Randomization and replication [41] --
4.2 Analysis of a completely randomized design with two treatments [43] --
4.3 A completely randomized design with several treatments [46] --
4.4 Testing overall variation between the treatments [49] --
4.5 Analysis using a statistical package [55] --
5 Control of random variation by blocking [59] --
5.1 Local control of variation [59] --
5.2 Analysis of a randomized block design [61] --
5.3 Meaning of the error mean square [66] --
5.4 Latin square designs [70] --
5.5 Analysis of structured experimental data using a computer package [74] --
5.6 Multiple Latin squares designs [76] --
5.7 The benefit of blocking and the use of natural blocks [79] --
6 Particular questions about treatments [89] --
6.1 Treatment structure [89] --
6.2 Treatment contrasts [94] --
6.3 Factorial treatment structure [97] --
6.4 Main effects and interactions [99] --
6.5 Analysis of variance for a two-factor experiment [102] --
6.6 Computer analysis of factorials [109] --
7 More on factorial treatment structure [111] --
7.1 More than two factors [111] --
7.2 Factors with two levels [112] --
7.3 The double benefit of factorial structure [118] --
7.4 Many factors and small blocks [120] --
7.5 The analysis of confounded experiments [125] --
7.6 Split plot experiments [128] --
7.7 Analysis of a split plot experiment [130] --
8 The assumptions behind the analysis [137] --
8.1 Our assumptions [137] --
8.2 Normality [138] --
8.3 Variance homogeneity [142] --
8.4 Additivity [145] --
8.5 Transformations of data for theoretical reasons [147] --
8.6 A more general form of analysis [151] --
8.7 Empirical detection of the failure of assumptions --
and selection of appropriate transformations [152] --
8.8 Practice and presentation [158] --
9 Studying linear relationships [161] --
9.1 Linear regression [161] --
9.2 Assessing the regression line [165] --
9.3 Inferences about the slope of a line [167] --
9.4 Prediction using a regression line [168] --
9.5 Correlation --
9.6 Testing whether the regression is linear [177] --
9.7 Regression analysis using computer packages [178] --
10 More complex relationships [183] --
10.1 Making the crooked straight [183] --
10.2 Two independent variables [186] --
10.3 Testing the components of a multiple relationship [193] --
10.4 Multiple regression [204] --
10.5 Possible problems in computer multiple regression [211] --
11 Linear models [213] --
11.1 The use of models [213] --
11.2 Models for factors and variables [214] --
11.3 Comparison of regressions [220] --
11.4 Fitting parallel lines [227] --
11.5 Covariance analysis [233] --
11.6 Regression in the analysis of treatment variation [240] --
12 Non-linear models [247] --
12.1 Advantages of linear and non-linear models [247] --
12.2 Fitting non-linear models to data [252] --
12.3 Inferences about non-linear parameters [258] --
12.4 Exponential models [262] --
12.5 Inverse polynomial models [267] --
12.6 Logistic models for growth curves [274] --
13 The analysis of proportions [277] --
13.1 Data in the form of frequencies [277] --
13.2 The 2x2 contingency table [278] --
13.3 More than two situations or more than two outcomes [280] --
13.4 General contingency tables [284] --
13.5 Estimation of proportions [289] --
13.6 Sample sizes for estimating proportions [294] --
14 Models and distributions for frequency data [299] --
14.1 Models for frequency data [299] --
14.2 Testing the agreement of frequency data with simple models [300] --
14.3 Investigating more complex models [302] --
14.4 The binomial distribution [309] --
14.5 The Poisson distribution [316] --
14.6 Generalized models for analysing experimental data [323] --
14.7 Log-linear models [329] --
14.8 Probit analysis [336] --
15 Making and analysing many experimental measurements [341] --
15.1 Different measurements on the same units [341] --
15.2 Interdependence of different variables [342] --
15.3 Repeated measurements [346] --
15.4 Joint (bivariate) analysis [356] --
15.5 Investigating relationships with experimental data [367] --
16 Choosing the most appropriate experimental design [373] --
16.1 The components of design; units and treatments [373] --
16.2 Replication and precision [374] --
16.3 Different levels of variation and within-unit replication [377] --
16.4 Variance components and split plot designs [381] --
16.5 Randomization [384] --
16.6 Managing with limited resources [386] --
16.7 Factors with quantitative levels [387] --
16.8 Screening and selection [389] --
17 Sampling finite populations [393] --
17.1 Experiments and sample surveys [393] --
17.2 Simple random sampling [394] --
17.3 Stratified random sampling [397] --
17.4 Cluster sampling, multistage sampling and sampling proportional to size [399] --
17.5 Ratio and regression estimates [400] --
References [403] --
Appendix [405] --
Index [413] --
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