A First Course Mathematical Statistics

Front Cover
CUP Archive, Jan 2, 1949 - Mathematics - 277 pages
This book provides the mathematical foundations of statistics. Its aim is to explain the principles, to prove the formulae to give validity to the methods employed in the interpretation of statistical data. Many examples are included but, since the primary emphasis is on the underlying theory, it is of interest to students of a wide variety of subjects: biology, psychology, agriculture, economics, physics, chemistry, and (of course) mathematics.
 

Contents

Chapter I
1
Change of origin and unit
4
Variance Standard deviation
6
Moments
8
Grouped distribution
10
Continuous distributions
12
EXAMPLES I
16
PROBABILITY AND PROBABILITY DISTRIBUTIONS 7 Explanation of terms Measure of probability
19
Standard errors of the variance and the standard deviation of a large sample
136
Comparison of the standard deviations of two large samples
137
Sampling from a Bivariate Population 61 Sampling covariance of the means of the variables page
138
Variance and covariance of moments about a fixed point
140
Standard error of the covariance of a large sample
141
EXAMPLES VII
144
Chapter VIII
146
Relation between the two functions
147

Theorems of total and compound probability
21
Probability distributions Expected value
24
Expected value of a sum or a product of two variates
26
Repeated trials Binomial distribution
28
Continuous probability distributions
30
Theorems of Tchebychef and Bernoulli
32
Empirical definition of probability
34
Moment generating function and characteristic function
36
Cumulative function of a distribution
39
EXAMPLES II
42
Chapter III
46
Poissons distribution
47
Derivation from the binomial distribution page
50
Some properties of the normal distribution
51
Probabilities and relative frequencies for various intervals
55
Distribution of a sum of independent normal variates
57
EXAMPLES III
58
MATHEMATICAL NOTES
63
Chapter IV
67
Continuous distributions
68
Lines of regression
69
Coefficient of correlation Standard error of estimate
72
Estimates from the regression equation
74
Change of units
75
Numerical illustration
76
Correlation of ranks
79
Bivariate probability distributions
80
Variance of a sum of variates
82
EXAMPLES IV
83
FURTHER CORRELATION THEORY CURVED REGRESSION LINES 33 Arrays Linear regression
87
Correlation ratios
89
Calculation of correlation ratios
91
Other relations
92
Continuous distributions
93
Bivariate normal distribution
95
Intraclass correlation
97
Polynomial regression Normal equations page
99
Index of correlation
102
Some related regressions
104
EXAMPLES
105
Chapter VI
109
Large samples Test of significance
111
Comparison of large samples
112
Poissonian and Lexian sampling Samples of varying size
114
Sampling of Values of a Variable 48 Random and simple sampling
116
Sampling distributions Standard errors
117
Sampling distribution of the mean
119
Normal population Fiducial limits for unknown mean
121
Comparison of the means of two large samples
122
Standard error of a partition value
124
EXAMPLES VI
126
Chapter VII
130
Standard errors of class frequencies
131
Covariance of the frequencies in different classes
132
Standard errors in moments about a fixed value
133
Covariance of moments of different orders about a fixed value
135
Gamma distribution and Gamma variates
149
Sum of independent Gamma variates
151
Beta distribution of the first kind
153
Alternative proof of theorems
154
Product of a f₁l m variate and a yl+m variate
156
Quotient of independent Gamma variates
158
EXAMPLES VIII
160
Chapter IX
164
Linear constraints Degrees of freedom
166
Distribution of the sum of squares for a random sample from a normal population
169
Nature of the chisquare test An illustration
170
Test of goodness of fit
173
Numerical examples
175
Additive property of chisquare
177
Distribution of regression coefficients and correlation ratios
179
EXAMPLES IX
181
FURTHER TESTS OF SIGNIFICANCE SMALL SAMPLES 84 Small samples page
185
Students Distribution 85 The statistict and its distribution
186
Test for an assumed population mean
189
Comparison of the means of two samples
190
Significance of an observed correlation
192
Significance of an observed regression coefficient
194
Distribution of the range of a sample
195
Distribution of the Variance Ratio 92 Ratio of independent estimates of the population variance
196
Fishers z distribution Table of F
198
Fishers Transformation of the Correlation Coefficient 94 Distribution of r Fishers transformation
200
Comparison of correlations in independent samples
202
Combination of estimates of a correlation coefficient
203
EXAMPLES X
205
Chapter XI
209
Homogeneous population One criterion of classification
210
Calculation of the sums of squares
212
Two criteria of classification
214
The Latin square Three criteria of classification
217
Significance of an observed correlation ratio
221
Significance of a regression function
223
Test for nonlinearity of regression
224
Resolution of the sum of products One criterion of classification page
226
Calculation of the sums of products
228
Examination and elimination of the effect of regression
229
Two criteria of classification
233
EXAMPLES XI
236
Chapter XII
242
Distribution of three or more variables
244
Determination of the coefficients of regression
246
Multiple correlation
249
Partial correlation
250
Reduction formula for the order of a standard deviation
253
Reduction formula for the order of a regression coefficient
254
Normal distribution
255
Significance of an observed partial correlation
256
Significance of an observed multiple correlation
257
EXAMPLES XII
260
LITERATURE FOR REFERENCE
263
INDEX
273
Ordinates of the Normal Curve 53

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