By Hans-Michael Kaltenbach
The textual content provides a concise advent into basic thoughts in facts. bankruptcy 1: brief exposition of likelihood idea, utilizing typical examples. bankruptcy 2: Estimation in thought and perform, utilizing biologically influenced examples. Maximum-likelihood estimation in lined, together with Fisher details and gear computations. tools for calculating self assurance durations and powerful choices to plain estimators are given. bankruptcy three: speculation checking out with emphasis on thoughts, quite type-I , type-II error, and examining try out effects. a number of examples are supplied. T-tests are used all through, very important different checks and robust/nonparametric choices. a number of checking out is mentioned in additional intensity, and mixture of self sustaining assessments is defined. bankruptcy four: Linear regression, with computations exclusively in accordance with R. a number of team comparisons with ANOVA are lined including linear contrasts, back utilizing R for computations.
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Additional info for A Concise Guide to Statistics
1 Maximum-Likelihood To apply maximum-likelihood estimation in the general case, we need to specify a family of distributions that is parametrized by θ such that each value of θ selects one particular distribution from this family. In the previous example, this family was the set of all binomial distributions with fixed n, where each value for p selects one particular member of this family. Here, we consider the density function f (x; θ ), describing the family of distributions, and aim at estimating the parameter θ.
5. 1 Maximum-Likelihood To apply maximum-likelihood estimation in the general case, we need to specify a family of distributions that is parametrized by θ such that each value of θ selects one particular distribution from this family. In the previous example, this family was the set of all binomial distributions with fixed n, where each value for p selects one particular member of this family. Here, we consider the density function f (x; θ ), describing the family of distributions, and aim at estimating the parameter θ.
I=k+1 For k = 0, we recover the ordinary arithmetic mean again, for k = n/2 (taken to the next suitable integer), we recover the median. The k-trimmed mean is thus a generalization of both estimators. 25 × n. 876, 8 so the smallest and largest value are ignored and the ordinary arithmetic mean is computed from the remaining data. 635 does not change the estimate, as this point is ignored in the computation. In R, the k-trimmed mean can be accessed by mean(. . , trim=. . ). 2 Scale: MAD and IQR Similar considerations lead to two robust alternatives for measuring the scale of a distribution: the median absolute deviation (MAD) and the inter-quartile-range (IQR).
A Concise Guide to Statistics by Hans-Michael Kaltenbach