J. H. Pollard
A Handbook of Numerical and Statistical Techniques
With Examples Mainly from the Life Sciences
J. H. Pollard
A Handbook of Numerical and Statistical Techniques
With Examples Mainly from the Life Sciences
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This handbook is designed for experimental scientists, particularly those in the life sciences.
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This handbook is designed for experimental scientists, particularly those in the life sciences.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 368
- Erscheinungstermin: 26. August 2008
- Englisch
- Abmessung: 229mm x 152mm x 22mm
- Gewicht: 597g
- ISBN-13: 9780521297509
- ISBN-10: 0521297508
- Artikelnr.: 25428326
- Verlag: Cambridge University Press
- Seitenzahl: 368
- Erscheinungstermin: 26. August 2008
- Englisch
- Abmessung: 229mm x 152mm x 22mm
- Gewicht: 597g
- ISBN-13: 9780521297509
- ISBN-10: 0521297508
- Artikelnr.: 25428326
Part I. Basic numerical techniques: 1. Introduction
2. Errors, mistakes and the arrangement of work
3. The real roots of non-linear equations
4. Simple methods for smoothing crude data
5. The area under a curve
6. Finite differences, interpolation and numerical differentiation
7. Some other numerical techniques
Part II. Basic Statistical techniques: 8. Probability, statistical distributions and moments
9. The normal and related distributions
10. The common discrete distributions
10. The common discrete distributions
11. The Pearson system of probability-density functions
12. Hypothesis testing
13. Point and interval estimation
14. Some special statistical techniques
Part III. The method of least squares: 15. Simple linear regression and the method of least squares
16. Curvilinear regression
17. Multiple linear regression
18. Non-linear regression.
2. Errors, mistakes and the arrangement of work
3. The real roots of non-linear equations
4. Simple methods for smoothing crude data
5. The area under a curve
6. Finite differences, interpolation and numerical differentiation
7. Some other numerical techniques
Part II. Basic Statistical techniques: 8. Probability, statistical distributions and moments
9. The normal and related distributions
10. The common discrete distributions
10. The common discrete distributions
11. The Pearson system of probability-density functions
12. Hypothesis testing
13. Point and interval estimation
14. Some special statistical techniques
Part III. The method of least squares: 15. Simple linear regression and the method of least squares
16. Curvilinear regression
17. Multiple linear regression
18. Non-linear regression.
Part I. Basic numerical techniques: 1. Introduction
2. Errors, mistakes and the arrangement of work
3. The real roots of non-linear equations
4. Simple methods for smoothing crude data
5. The area under a curve
6. Finite differences, interpolation and numerical differentiation
7. Some other numerical techniques
Part II. Basic Statistical techniques: 8. Probability, statistical distributions and moments
9. The normal and related distributions
10. The common discrete distributions
10. The common discrete distributions
11. The Pearson system of probability-density functions
12. Hypothesis testing
13. Point and interval estimation
14. Some special statistical techniques
Part III. The method of least squares: 15. Simple linear regression and the method of least squares
16. Curvilinear regression
17. Multiple linear regression
18. Non-linear regression.
2. Errors, mistakes and the arrangement of work
3. The real roots of non-linear equations
4. Simple methods for smoothing crude data
5. The area under a curve
6. Finite differences, interpolation and numerical differentiation
7. Some other numerical techniques
Part II. Basic Statistical techniques: 8. Probability, statistical distributions and moments
9. The normal and related distributions
10. The common discrete distributions
10. The common discrete distributions
11. The Pearson system of probability-density functions
12. Hypothesis testing
13. Point and interval estimation
14. Some special statistical techniques
Part III. The method of least squares: 15. Simple linear regression and the method of least squares
16. Curvilinear regression
17. Multiple linear regression
18. Non-linear regression.