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Applied Statistics for Agriculture, Veterinary, Fishery, Dairy and Allied Fields 1st ed. 2016 [Hardback]

  • Formāts: Hardback, 533 pages, height x width: 254x178 mm, weight: 1632 g, 116 Illustrations, color; 185 Illustrations, black and white; XVI, 533 p. 301 illus., 116 illus. in color., 1 Hardback
  • Izdošanas datums: 25-Jan-2017
  • Izdevniecība: Springer, India, Private Ltd
  • ISBN-10: 8132228294
  • ISBN-13: 9788132228295
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Applied Statistics for Agriculture, Veterinary, Fishery, Dairy and Allied Fields 1st ed. 2016Palielināt
  • Formāts: Hardback, 533 pages, height x width: 254x178 mm, weight: 1632 g, 116 Illustrations, color; 185 Illustrations, black and white; XVI, 533 p. 301 illus., 116 illus. in color., 1 Hardback
  • Izdošanas datums: 25-Jan-2017
  • Izdevniecība: Springer, India, Private Ltd
  • ISBN-10: 8132228294
  • ISBN-13: 9788132228295
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9788132228295.html
Keywords:
This book is aimed at a wide range of readers who lack confidence in the mathematical and statistical sciences, particularly in the fields of Agriculture, Veterinary, Fishery, Dairy and other related areas. Its goal is to present the subject of statistics and its useful tools in various disciplines in such a manner that, after reading the book, readers will be equipped to apply the statistical tools to extract otherwise hidden information from their data sets with confidence. Starting with the meaning of statistics, the book introduces measures of central tendency, dispersion, association, sampling methods, probability, inference, designs of experiments and many other subjects of interest in a step-by-step and lucid manner. The relevant theories are described in detail, followed by a broad range of real-world worked-out examples, solved either manually or with the help of statistical packages. In closing, the book also includes a chapter on which statistical packages to use, depending on the users respective requirements.   



 





 

Recenzijas

This is a well-written textbook covering a number of statistical topics suitable for a textbook in an undergraduate program for the areas listed in the title of the book. It is also a suitable textbook in a statistics curriculum. (Morteza Marzjarani, Technometrics, Vol. 60 (1), 2018)

1 Introduction to Statistics and Biostatistics
1(8)
1.1 Introduction
1(1)
1.2 Use and Scope of Statistics
1(1)
1.3 Subject Matter of Statistics
2(1)
1.4 Steps in Statistical Procedure
2(5)
1.5 Limitation of Statistics
7(2)
2 Data-Information and Its Presentation
9(26)
2.1 Data
9(3)
2.2 Character
12(1)
2.3 Variable and Constant
12(1)
2.4 Processing of Data
13(3)
2.5 Classification/Grouping
16(4)
2.5.1 Method of Classification
18(1)
2.5.2 Cumulative Frequency
18(1)
2.5.3 Relative Frequency
18(1)
2.5.4 Frequency Density
18(2)
2.6 Presentation of Data
20(15)
2.6.1 Textual Form
21(1)
2.6.2 Tabular Form
22(2)
2.6.3 Diagrammatic Form
24(11)
3 Summary Statistics
35(42)
3.1 Measures of Central Tendency
36(15)
3.1.1 Arithmetic Mean
37(2)
3.1.2 Geometric Mean
39(3)
3.1.3 Harmonic Mean
42(1)
3.1.4 Use of Different Types of Means
43(1)
3.1.5 Median
44(2)
3.1.6 Partition Values (Percentiles, Deciles, and Quartiles)
46(2)
3.1.7 Mode
48(1)
3.1.8 Midpoint Range
49(1)
3.1.9 Selection of Proper Measure of Central Tendency
50(1)
3.2 Dispersion and Its Measures
51(19)
3.2.1 Absolute Measures of Dispersion
51(11)
3.2.2 Moments
62(7)
3.2.3 Relative Measures of Dispersion
69(1)
3.3 Skewness and Kurtosis
70(7)
3.3.1 Skewness
71(2)
3.3.2 Kurtosis
73(4)
4 Probability Theory and Its Application
77(36)
4.1 Introduction
77(1)
4.2 Types of Set
78(1)
4.3 Properties of Sets
79(1)
4.4 Experiment
80(1)
4.5 Probability Denned
81(4)
4.5.1 Important Results in Probability
82(3)
4.6 Random Variables and Their Probability Distributions
85(1)
4.7 Mean, Variance, and Moments of Random Variable
86(3)
4.8 Moment-Generating Function
89(2)
4.9 Theoretical Probability Distributions
91(15)
4.9.1 Binomial Distribution
91(5)
4.9.2 Poisson Distribution
96(4)
4.9.3 Normal Distribution
100(6)
4.10 Central Limit Theorem
106(1)
4.11 Sampling Distribution
107(6)
4.11.1 x2-Distribution
107(1)
4.11.2 t-Distribution
108(1)
4.11.3 F Distribution
109(1)
4.11.4 Sampling Distribution of Sample Mean and Sample Mean Square
110(1)
4.11.5 Fisher's t-Distribution and Student's t-Distribution
111(2)
5 Population and Sample
113(20)
5.1 Population
113(1)
5.2 Sample
113(2)
5.3 Parameter and Statistic
115(1)
5.4 Estimator
115(1)
5.5 Subject Matter of Sampling
115(1)
5.6 Errors in Sample Survey
116(1)
5.7 Sample Size
116(2)
5.8 Selection of Sample (Sampling Technique)
118(1)
5.9 Different Sampling Techniques
119(14)
5.9.1 Probability Sampling
119(11)
5.9.2 Non-probability Sampling
130(3)
6 Statistical Inference
133(62)
6.1 Introduction
133(7)
6.1.1 Estimation
134(5)
6.1.2 Testing of Hypothesis
139(1)
6.2 Testing of Hypothesis
140(36)
6.2.1 Parametric Tests
141(35)
6.3 Nonparametric Method
176(19)
6.3.1 One Sample Test
176(6)
6.3.2 Two Sample Test
182(13)
7 Correlation Analysis
195(28)
7.1 Introduction
195(1)
7.2 Correlation Coefficient
196(1)
7.3 Properties
197(6)
7.4 Significance of Correlation Coefficients
203(1)
7.5 Correlation Coefficient of Bivariate Frequency Distribution
204(2)
7.6 Limitations
206(1)
7.7 Rank Correlation
207(2)
7.8 Correlation Ratio
209(2)
7.9 Properties of Correlation Ratio
211(1)
7.10 Coefficient of Concurrent Deviation
211(1)
7.11 Calculation of Correlation Coefficient Using MS Excel, SPSS, and SAS
212(11)
8 Regression Analysis
223(54)
8.1 Introduction
223(1)
8.2 Explanation of the Regression Equation
224(1)
8.3 Assumption of Linear Regression Model
224(1)
8.4 Simple Linear Regression Analysis
225(5)
8.5 Properties of Regression Coefficient
230(4)
8.5.1 Regression Coefficient
230(1)
8.5.2 The Sign of the Regression Coefficient
231(1)
8.5.3 Relation Between Correlation Coefficient and the Regression Coefficients
231(1)
8.5.4 Relation Between Regression Coefficients
231(1)
8.5.5 AM and GM of Regression Coefficients
231(1)
8.5.6 Range of Regression Coefficient
231(1)
8.5.7 Effect of Change of Origin and Scale on Regression Coefficient
231(1)
8.5.8 Angle Between Two Lines of Regression
232(1)
8.5.9 Regression with Zero Intercept
232(2)
8.6 Identification of the Regression Equations
234(1)
8.7 Expectations and Variances of the Regression Parameters
235(1)
8.8 Test of Significance for the Regression Coefficient
236(1)
8.9 Multiple Linear Regression Analysis
236(1)
8.10 Multiple Linear Regression Equation Taking Three Variables
237(1)
8.11 Estimation of the Parameters of Linear Regression Model Using OLS Technique in the Matrix Form
238(2)
8.12 Estimation of Regression Coefficients from Correlation Coefficients
240(4)
8.13 Multiple Correlations
244(1)
8.14 The Coefficient of Determination (R2)
245(3)
8.14.1 Interpretation of R2
246(1)
8.14.2 Adjusted R2
247(1)
8.15 Partial Correlation
248(2)
8.16 Some Other Measures of Association
250(1)
8.16.1 Biserial Correlation
250(1)
8.16.2 Tetrachoric Correlation
251(1)
8.16.3 Part Correlation
251(1)
8.17 Worked-Out Example Using the Usual Method of Calculation and with the Help of the Software Packages
251(26)
8.17.1 Calculation of All Possible Correlation Coefficients
252(7)
8.17.2 Calculation of Partial Correlation Coefficients
259(4)
8.17.3 Estimation of Simple Linear Regression
263(6)
8.17.4 Estimation of Multiple Linear Regression Equation
269(8)
9 Analysis of Variance
277(42)
9.1 Introduction
277(1)
9.2 Linear Analysis of Variance Model
278(1)
9.3 Assumptions in Analysis Variance
278(1)
9.4 One-Way Classified Data
279(7)
9.4.1 Analysis of One-Way Classified Data Using MS Excel
284(2)
9.5 Two-Way Classified Data
286(10)
9.5.1 Two-Way Classified Data with One Observation per Cell
288(5)
9.5.2 Analysis of Two-Way Classified Data with One Observation per Cell Using MS Excel
293(3)
9.6 Two-Way Classified Data with More Than One Observation per Cell
296(8)
9.6.1 Analysis of Two-Way Classified Data with More than One Observation per Cell Using MS Excel
301(3)
9.7 Violation of Assumptions in ANOVA
304(7)
9.7.1 Logarithmic Transformation
305(2)
9.7.2 Square Root Transformation
307(2)
9.7.3 Angular Transformation
309(2)
9.8 Effect of Change in Origin and Scale on Analysis of Variance
311(8)
10 Basic Experimental Designs
319(46)
10.1 Introduction
319(3)
10.2 Principles of Design
322(1)
10.3 Uniformity Trial
323(1)
10.4 Optimum Size and Shape of Experimental Units
324(1)
10.5 Layout
325(1)
10.6 Steps in Designing of Experiments
325(1)
10.7 Completely Randomized Design (CRD)
326(12)
10.7.1 Randomization and Layout
326(2)
10.7.2 Statistical Model and Analysis
328(1)
10.7.3 Merits and Demerits of CRD
329(9)
10.8 Randomized Block Design/Randomized Complete Block Design (RBD/RCBD)
338(15)
10.8.1 Experimental Layout
338(2)
10.8.2 Statistical Model and Analysis
340(2)
10.8.3 Merits and Demerits of RBD
342(11)
10.9 Latin Square Design (LSD)
353(5)
10.9.1 Randomization and Layout
354(1)
10.9.2 Statistical Model and Analysis
354(2)
10.9.3 Merits and Demerits of Latin Square Design
356(2)
10.10 Missing Plot Technique
358(7)
10.10.1 Missing Plot Technique in CRD
359(1)
10.10.2 Missing Plot Technique in RBD
359(2)
10.10.3 Missing Plot Technique in LSD
361(4)
11 Factorial Experiment
365(102)
11.1 Introduction
365(2)
11.1.1 Factor and Its Levels
366(1)
11.1.2 Type of Factorial Experiment
366(1)
11.1.3 Effects and Notations in Factorial Experiment
366(1)
11.1.4 Merits of Factorial Experiment
367(1)
11.1.5 Demerits of Factorial Experiment
367(1)
11.2 Two-Factor Factorial Experiments
367(31)
11.2.1 22 Factorial Experiment
367(22)
11.2.2 Two-Factor Asymmetrical (m x n, m ≠ n) Factorial Experiment
389(9)
11.3 Three-Factor Factorial Experiments
398(41)
11.3.1 23 Factorial Experiment
398(24)
11.3.2 m x n x p Asymmetrical Factorial Experiment
422(17)
11.4 Incomplete Block Design
439(13)
11.4.1 Split Plot Design
440(12)
11.5 Strip Plot Design
452(15)
12 Special Experiments and Designs
467(40)
12.1 Introduction
467(1)
12.2 Comparison of Factorial Effects vs. Single Control Treatment
468(4)
12.3 Augmented Designs for the Evaluation of Plant Germplasms
472(10)
12.3.1 Augmented Completely Randomized Design
472(3)
12.3.2 Augmented Randomized Block Design
475(7)
12.4 Combine Experiment
482(18)
12.5 Analysis of Experimental Data Measured Over Time
500(1)
12.5.1 Observations Taken Over Time in RBD
500(1)
12.5.2 Observations Taken Over Time in Two-Factor RBD
500(1)
12.5.3 Observations Taken Over Time in Split Plot Design
501(1)
12.6 Experiments at Farmers' Field
501(6)
12.6.1 Major Considerations During Experimentations at Farmers' Fields
502(5)
13 Use-Misuse of Statistical Packages
507(14)
References 521(8)
Index 529