Biostatistical AnalysisPresents a broad collection of data analysis techniques suitable for biological investigations, either as an introductory textbook assuming no prior knowledge of statistics, or as a reference on concepts and procedures of statistical analysis for professional use in the biological disciplines. Each |
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Page 18
... parameter being estimated . That is , for some samples a statistic may underestimate the parameter of interest , and for others it may overestimate that parameter ; but in the long run the estimates that are too low and those that are ...
... parameter being estimated . That is , for some samples a statistic may underestimate the parameter of interest , and for others it may overestimate that parameter ; but in the long run the estimates that are too low and those that are ...
Page 435
... parameter estimates to arrive at a set of somewhat better parameter estimates , using the new estimates to derive better estimates , etc. Thus , many of these programs require the user to submit initial estimates of ( i.e. , to guess ...
... parameter estimates to arrive at a set of somewhat better parameter estimates , using the new estimates to derive better estimates , etc. Thus , many of these programs require the user to submit initial estimates of ( i.e. , to guess ...
Page 572
... PARAMETER Confidence limits for the parameter ( which is both the population mean and the population variance ) of a population following the Poisson distribution may be obtained as follows . The lower 1 - a confidence limit is X1 - a ...
... PARAMETER Confidence limits for the parameter ( which is both the population mean and the population variance ) of a population following the Poisson distribution may be obtained as follows . The lower 1 - a confidence limit is X1 - a ...
Contents
POPULATIONS AND SAMPLES | 15 |
MEASURES OF DISPERSION AND VARIABILITY | 31 |
Exercises | 62 |
Copyright | |
25 other sections not shown
Common terms and phrases
analysis of variance ANOVA Appendix Table b₁ b₂ binomial distribution binomial test calculated cell Chapter chi-square confidence interval confidence limits cont contingency table correlation coefficient Critical Values data of Example degrees of freedom demonstrated in Example determine diets difference Distribution Numerator employed equal Equation estimate exact test experimental design Fisher groups hypothesis test interaction levels of factor linear males Mann-Whitney test mean square measurements median mg/m³ Mmmmm multiple comparison multiple regression n₁ nonparametric normal approximation normal distribution null hypothesis number of data observed obtained one-tailed test parameter population mean probability procedure proportion R₁ random ranks ratio regression coefficients reject reject Ho residual sample sizes sampled population shown in Example significance species standard error sum of squares TABLE B.4 test statistic total number transformation two-sample Type I error v₁ weight X₁ zero ΣΧ