A basic course in statistics / G.M. Clarke and D. Cooke.

By: Clarke, G. M. (Geoffrey Mallin).

Contributor(s): Cooke, D. (Dennis), 1929-.

Material type: materialTypeLabel

BookPublisher: London : Arnold, c2004Edition: 5th ed.Description: xxv, 734 p. : ill. ; 25 cm. + pbk.ISBN: 0340814063 ; 9780340814062.Subject(s): Mathematical statistics

DDC classification: 519.5

Contents:

Populations and variates -- Measures of the centre of a set of observations -- Samples and populations -- The measurement of variability -- Looking at data -- Probability -- Probabilities of compound events -- Discrete random variables -- Expectation of random variables -- Joint distributions -- Estimation -- Collecting data -- Significance testing -- Continuous random variables -- The normal distribution -- Sampling distributions of means and related quantities -- Significance tests using the normal distribution -- Estimation of intervals and parameters -- Hypothesis tests using the x2 distribution -- The Poisson distribution -- Correlation -- The analysis of variance -- Simple linear regression -- Multiple regression.

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Enhanced descriptions from Syndetics:

The strength of this text, now in its fifth edition, is its applicability across a range of disciplines, making it the perfect choice as an introductory textbook for first year students. In this new edition, the computing content is based on Minitab, Release 14, with its extensive facilities in graphical and descriptive statistics.

Bibliography: (pages 684-685) and index.

CIT Module STAT 6001 - Supplementary reading

CIT Module STAT 6003 - Supplementary reading

CIT Module STAT 7001 - Recommended reading

CIT Module BIOT 6005 - Supplementary reading

Table of contents provided by Syndetics

Preface to the fifth edition (p. xv)
Introduction (p. xvii)
List of projects (p. xxii)
List of data sets on the web (p. xxiii)
Notation (p. xxiv)
1 Populations and variates (p. 1)
1.1 A statistical investigation (p. 1)
1.2 Constructing tables and graphs (p. 6)
1.3 Tables and diagrams (p. 12)
1.4 The use of computers (p. 14)
1.5 Introduction to MINITAB (p. 15)
1.6 MINITAB: Graphs, diagrams and tables (p. 17)
1.7 Exercises on Chapter 1 (p. 19)
1.8 Computing exercises (p. 21)
1.9 Projects (p. 22)
Summary (p. 23)
2 Measures of the centre of a set of observations (p. 24)
2.1 Introduction (p. 24)
2.2 The median (p. 24)
2.3 The arithmetic mean (p. 26)
2.4 [Sigma]-notation (p. 29)
2.5 The mean of grouped data (p. 32)
2.6 Coding (p. 33)
2.7 The mode (p. 36)
2.8 The geometric mean (p. 37)
2.9 Weighted averages and index numbers (p. 37)
2.10 MINITAB: Calculation of mean and median; dot plots (p. 39)
2.11 Exercises on Chapter 2 (p. 41)
2.12 Computing exercises (p. 45)
2.13 Projects (p. 46)
Summary (p. 47)
3 Samples and populations (p. 48)
3.1 A sampling investigation (p. 48)
3.2 Some methods of choosing a sample (p. 49)
3.3 Completing the investigation (p. 52)
3.4 Random number (p. 53)
3.5 Pseudo-random numbers (p. 54)
3.6 Systematic samples (p. 55)
3.7 Some useful terms (p. 56)
3.8 Sampling with and without replacement (p. 57)
3.9 MINITAB: Random numbers; sampling (p. 57)
3.10 Exercises on Chapter 3 (p. 58)
3.11 Computing exercises (p. 60)
3.12 Projects (p. 61)
Summary (p. 62)
4 The measurement of variability (p. 63)
4.1 Introduction (p. 63)
4.2 Range (p. 63)
4.3 Mean deviation (p. 64)
4.4 Variance and standard deviation (p. 65)
4.5 Variance and standard deviation of a grouped frequency distribution (p. 69)
4.6 The inter-quartile range (or mid-spread) (p. 72)
4.7 Quantiles (p. 74)
4.8 MINITAB: The two variances; box-and-whisker plots (p. 79)
4.9 Exercises on Chapter 4 (p. 80)
4.10 Computing exercises (p. 81)
4.11 Projects (p. 83)
Summary (p. 84)
5 Looking at data (p. 85)
5.1 Introduction (p. 85)
5.2 Data preparation and scrutiny (p. 86)
5.3 Data description (p. 89)
5.4 Conclusion (p. 94)
5.5 MINITAB: Scatter and time series plots; five-number summary (p. 94)
5.6 Exercises on Chapter 5 (p. 95)
5.7 Project (p. 98)
Summary (p. 98)
Revision Exercises A (p. 99)
6 Probability (p. 106)
6.1 Introduction (p. 106)
6.2 Winning the toss (p. 108)
6.3 Terminology and notation (p. 110)
6.4 Probability when outcomes are not equally likely (p. 115)
6.5 Definition of probability (p. 117)
6.6 Setting up probability models (p. 118)
6.7 Counting methods (p. 119)
6.8 Use of counting methods in probability (p. 123)
6.9 Simulation (p. 125)
6.10 MINITAB: Generating random samples (p. 125)
6.11 Exercises on Chapter 6 (p. 126)
6.12 Computing exercises (p. 127)
6.13 Projects (p. 127)
Summary (p. 128)
7 Probabilities of compound events (p. 130)
7.1 Introduction (p. 130)
7.2 The intersection of two events (p. 130)
7.3 The union of two events (p. 131)
7.4 Theorems on the probability of the union of two events (p. 133)
7.5 Complementary events (p. 136)
7.6 The intersection and union of three or more events (p. 138)
7.7 Conditional probability (p. 140)
7.8 Independence (p. 144)
7.9 Inverse probability (p. 150)
7.10 Simulation (p. 156)
7.11 MINITAB: Entering patterned data and coding (p. 158)
7.12 Exercises on Chapter 7 (p. 159)
7.13 Computing exercises (p. 161)
7.14 Projects (p. 161)
Summary (p. 163)
8 Discrete random variables (p. 165)
8.1 Predicting the number of left-handed children in a class (p. 165)
8.2 Random variables (p. 168)
8.3 Families of random variables (p. 170)
8.4 The discrete uniform distribution (p. 171)
8.5 The Bernoulli distribution (p. 172)
8.6 The binomial distribution (p. 172)
8.7 The geometric distribution (p. 176)
8.8 Acceptance sampling in quality control (p. 178)
8.9 Cumulative distribution functions and medians (p. 185)
8.10 Distribution of a function g(R) given the distribution of R (p. 187)
8.11 Choice of a random sample from a distribution (p. 188)
8.12 MINITAB: Probability functions; sampling a discrete distribution (p. 190)
8.13 Exercises on Chapter 8 (p. 191)
8.14 Computing exercises (p. 194)
8.15 Projects (p. 194)
Summary (p. 195)
9 Expectation of random variables (p. 197)
9.1 Expected values (p. 197)
9.2 The means of the binomial and geometric distributions (p. 199)
9.3 Expectation of a linear function of a random variable (p. 201)
9.4 The expected value of a function of a random variable (p. 203)
9.5 Variance as an expected value (p. 208)
9.6 The variance of the binomial distribution (p. 209)
9.7 Variance of a linear function of a random variable (p. 210)
9.8 Average sample number in acceptance sampling (p. 212)
9.9 Probability generating functions (p. 215)
9.10 MINITAB: Standardization of a variate (p. 218)
9.11 Exercises on Chapter 9 (p. 218)
9.12 Computing exercises (p. 221)
9.13 Projects (p. 221)
Summary (p. 222)
Revision Exercises B (p. 223)
10 Joint distributions (p. 229)
10.1 Bivariate distributions (p. 229)
10.2 Mean of the sum of two random variables (p. 233)
10.3 Mean of the product of two independent random variables (p. 237)
10.4 The variance of the sum of two independent random variables (p. 240)
10.5 Exercises on Chapter 10 (p. 243)
10.6 Computing exercises (p. 245)
10.7 Projects (p. 246)
Summary (p. 246)
11 Estimation (p. 248)
11.1 Distribution of estimates of a proportion (p. 248)
11.2 Distribution of estimates of a mean (p. 251)
11.3 The mean and variance of X (p. 252)
11.4 Unbiased estimators (p. 254)
11.5 Unbiased estimation of variance (p. 255)
11.6 MINITAB: Sample variance (p. 257)
11.7 Exercises on Chapter 11 (p. 257)
11.8 Computing exercises (p. 259)
11.9 Projects (p. 260)
Summary (p. 261)
12 Collecting data (p. 263)
12.1 Introduction (p. 263)
12.2 Surveys (p. 263)
12.3 Stratified random sampling (p. 265)
12.4 Experiments (p. 266)
12.5 Basic principles of experimental design (p. 267)
12.6 Exercises on Chapter 12 (p. 269)
12.7 Projects (p. 271)
Summary (p. 272)
13 Significance testing (p. 273)
13.1 Testing a hypothesis (p. 273)
13.2 Another alternative hypothesis (p. 277)
13.3 Two types of error (p. 282)
13.4 Significance (p. 283)
13.5 The Wilcoxon rank-sum (or Mann-Whitney U) test (p. 284)
13.6 The Wilcoxon signed-rank test (p. 290)
13.7 Type II errors and the power of a test (p. 293)
13.8 Summary and definitions (p. 297)
13.9 MINITAB: Nonparametric tests (p. 298)
13.10 Exercises on Chapter 13 (p. 299)
13.11 Computing exercises (p. 302)
13.12 Projects (p. 303)
Summary (p. 304)
Revision Exercises C (p. 304)
14 Continuous random variables (p. 312)
14.1 Modelling continuous variates (p. 312)
14.2 The specification of continuous random variables (p. 318)
14.3 The mean and variance of continuous random variables (p. 321)
14.4 Use of the expectation operator with continuous random variables (p. 323)
14.5 The continuous uniform distribution (p. 325)
14.6 The exponential distribution (p. 327)
14.7 Uses of the exponential distribution (p. 328)
14.8 The moment generating function (p. 332)
14.9 Sums of independent random variables (p. 335)
14.10 Distribution of a function of a random variable (p. 339)
14.11 MINITAB: Continuous random variables (p. 340)
14.12 Exercises on Chapter 14 (p. 341)
14.13 Computing exercises (p. 344)
14.14 Projects (p. 344)
Summary (p. 345)
15 The normal distribution (p. 347)
15.1 Introduction (p. 347)
15.2 The standard normal distribution (p. 348)
15.3 The normal distribution as a model for data (p. 350)
15.4 Derivation of mean and variance (p. 352)
15.5 The calculation and use of standard variates when data are normally distributed (p. 354)
15.6 Exploring data (p. 359)
15.7 Residuals (p. 360)
15.8 Graphical study of symmetry and normality (p. 362)
15.9 MINITAB: The normal distribution (p. 365)
15.10 Exercises on Chapter 15 (p. 366)
15.11 Computing exercises (p. 368)
15.12 Projects (p. 368)
Summary (p. 369)
16 Sampling distributions of means and related quantities (p. 371)
16.1 The central limit theorem (p. 371)
16.2 Distribution of sample total (p. 374)
16.3 Normal approximation to the binomial distribution (p. 376)
16.4 Proportions (p. 380)
16.5 Distribution of linear combinations of normal random variables (p. 381)
16.6 Control charts (p. 384)
16.7 MINITAB: Control charts (p. 394)
16.8 Exercises on Chapter 16 (p. 394)
16.9 Computing exercises (p. 397)
16.10 Projects (p. 398)
Summary (p. 399)
Revision Exercises D (p. 399)
17 Significance tests using the normal distribution (p. 407)
17.1 Introduction (p. 407)
17.2 The standard normal distribution (p. 408)
17.3 A single observation from N ([mu], [sigma superscript 2]) (p. 410)
17.4 The mean of n observations from N ([mu], [sigma superscript 2]) (p. 412)
17.5 Difference between two means from normal distributions (p. 414)
17.6 Large-sample tests (p. 416)
17.7 One-tail and two-tail tests (p. 418)
17.8 Paired comparison data (p. 419)
17.9 Tests for proportions (p. 422)
17.10 Normal approximations to nonparametric test statistics (p. 424)
17.11 Test on sample mean when variance estimated from sample (p. 426)
17.12 Paired comparisons using estimated variance (p. 428)
17.13 Tests on means of two unpaired samples using estimated variances (p. 430)
17.14 Inference when the null hypothesis is the hypothesis of interest (p. 433)
17.15 Parametric versus nonparametric tests (p. 434)
17.16 MINITAB: Significance tests on means and proportions (p. 435)
17.17 Exercises on Chapter 17 (p. 438)
17.18 Computing exercises (p. 442)
17.19 Projects (p. 443)
Summary (p. 443)
18 Estimation of intervals and parameters (p. 445)
18.1 Introduction (p. 445)
18.2 Confidence interval for mean of a normal distribution (p. 446)
18.3 Confidence interval for population mean based on a large sample (p. 448)
18.4 Confidence interval for a population proportion (p. 449)
18.5 Confidence intervals for means of normal distributions when the variance [sigma superscript 2] is unknown and the sample size is small (p. 451)
18.6 Investigation of a null hypothesis (p. 451)
18.7 Confidence interval for variance of a normal distribution (p. 455)
18.8 General methods of estimating parameters (p. 457)
18.9 MINITAB: Confidence intervals for means and proportions; t, X[superscript 2] and F (p. 465)
18.10 Exercises on Chapter 18 (p. 466)
18.11 Computing exercises (p. 470)
18.12 Projects (p. 471)
Summary (p. 471)
19 Hypothesis tests using the X[superscript 2] distribution (p. 473)
19.1 Random digits (p. 473)
19.2 The X[superscript 2] family of distributions (p. 475)
19.3 The rule for finding degrees of freedom in X[superscript 2] tests (p. 477)
19.4 Why the X[superscript 2] statistic has an approximate X[superscript 2] distribution (p. 478)
19.5 Testing the fit of a theoretical distribution to observed data (p. 480)
19.6 Testing the fit of data to a binomial distribution (p. 482)
19.7 Contingency tables (p. 484)
19.8 The 2 x 2 table (p. 488)
19.9 Testing the fit of a continuous distribution (p. 491)
19.10 MINITAB: Chi-squared tests (p. 494)
19.11 Exercises on Chapter 19 (p. 496)
19.12 Computing exercises (p. 499)
19.13 Projects (p. 499)
Summary (p. 500)
Revision Exercises E (p. 500)
20 The Poisson distribution (p. 511)
20.1 Introduction (p. 511)
20.2 Two mathematical results (p. 511)
20.3 The binomial distribution as n to [infinity], [pi] to 0 (p. 511)
20.4 The Poisson distribution as the outcome of a random process (p. 515)
20.5 The conditions for a Poisson distribution (p. 517)
20.6 Conditions in which a Poisson model breaks down (p. 518)
20.7 The mean and variance of the Poisson distribution (p. 519)
20.8 The sum of two Poisson random variables (p. 521)
20.9 Tests of goodness-of-fit to a Poisson distribution (p. 524)
20.10 The normal approximation to the Poisson distribution (p. 526)
20.11 Testing a hypothesis about the mean in a Poisson distribution (p. 527)
20.12 Comparing two means (p. 528)
20.13 Giving limits to an estimated mean (p. 529)
20.14 The 'index of dispersion' (p. 531)
20.15 Significance tests when X[superscript 2] has a very large number of degrees of freedom (p. 532)
20.16 MINITAB: The Poisson distribution (p. 533)
20.17 Exercises on Chapter 20 (p. 534)
20.18 Computing exercises (p. 537)
20.19 Projects (p. 537)
Summary (p. 538)
21 Correlation (p. 540)
21.1 Introduction (p. 540)
21.2 Scatter diagrams (p. 540)
21.3 The correlation coefficient (p. 544)
21.4 Correlation between two random variables (p. 548)
21.5 Testing values of r for significance (p. 550)
21.6 Fallacies in interpreting calculated correlation coefficients (p. 551)
21.7 Comparing two correlation coefficients (p. 557)
21.8 Confidence interval for [rho] (p. 558)
21.9 Rank correlation (p. 560)
21.10 Testing r[subscript s] for significance (p. 562)
21.11 MINITAB: Correlation coefficients (p. 564)
21.12 Exercises on Chapter 21 (p. 564)
21.13 Computing exercises (p. 568)
21.14 Projects (p. 569)
Summary (p. 570)
22 The analysis of variance (p. 571)
22.1 Introduction (p. 571)
22.2 Comparison of the means of populations: one-way analysis of variance (p. 573)
22.3 Confidence intervals and tests on means (p. 577)
22.4 The F-distribution and the F-test (p. 579)
22.5 A mathematical model for comparing population means (p. 581)
22.6 Two-way analysis of variance (p. 583)
22.7 Design of experiments (p. 590)
22.8 MINITAB: Analysis of variance; F-distribution (p. 591)
22.9 Exercises on Chapter 22 (p. 593)
22.10 Computing exercises (p. 597)
22.11 Projects (p. 598)
Summary (p. 599)
23 Simple linear regression (p. 601)
23.1 Introduction (p. 601)
23.2 Fitting a straight line when there is error in only one variable (p. 601)
23.3 Fitting a straight line that need not pass through the origin (p. 606)
23.4 The distributions of a and b (p. 609)
23.5 Regression (p. 613)
23.6 Prediction of the total score on two dice (p. 614)
23.7 Prediction of son's height from father's height (p. 615)
23.8 Two possible regression lines (p. 620)
23.9 An analysis of variance for regression (p. 622)
23.10 Transformations to make a model 'linear' (p. 627)
23.11 MINITAB: Simple linear regression (p. 630)
23.12 Exercises on Chapter 23 (p. 631)
23.13 Computing exercises (p. 636)
23.14 Projects (p. 638)
Summary (p. 639)
Revision Exercises F (p. 640)
24 Multiple regression (p. 649)
24.1 Introduction (p. 649)
24.2 Estimation of unknown parameters (p. 651)
24.3 Interpretation by analysis of variance (p. 654)
24.4 Mathematical derivation of analysis of variance identity (p. 658)
24.5 Confidence intervals and t-tests for the regression coefficients (p. 660)
24.6 Regression with more than two explanatory variates (p. 661)
24.7 Regression by computer (p. 662)
24.8 Checking the fit of the regression (p. 663)
24.9 General strategies (p. 663)
24.10 MINITAB: Multiple regression (p. 665)
24.11 Exercises on Chapter 24 (p. 666)
24.12 Computing exercises (p. 668)
24.13 Project (p. 671)
Summary (p. 671)
Appendix (p. 674)
I The binomial series expansion (p. 674)
II The exponential function (p. 675)
III Derivatives and integrals of the exponential function (p. 676)
IV Integrals related to the normal distribution (p. 676)
V The limit of (1 + x/n)[superscript n] as n to [infinity] (p. 678)
VI A derivation of the Poisson distribution (p. 679)
VII Partial differentiation (p. 679)
VIII Selection of MINITAB menus (p. 681)
Bibliography (p. 684)
I Particular topics (p. 684)
II Reference texts (p. 685)
Answers (p. 686)
Hints on computing exercises (p. 706)
Tables (p. 713)
Index (p. 726)

Author notes provided by Syndetics

Geoffrey Clarke , formerly of University of Sussex.

Dennis Cooke , formerly of University of Sussex.

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