By Dr. Sigbert Klinke (auth.)

ISBN-10: 3642592422

ISBN-13: 9783642592423

ISBN-10: 3790809829

ISBN-13: 9783790809824

Since the start of the seventies laptop is accessible to exploit programmable desktops for numerous projects. through the nineties the has constructed from the large major frames to non-public workstations. these days it's not in basic terms the that's even more strong, yet workstations can do even more paintings than a prime body, in comparison to the seventies. In parallel we discover a specialization within the software program. Languages like COBOL for company­ oriented programming or Fortran for clinical computing basically marked the start. The advent of non-public desktops within the eighties gave new impulses for even additional improvement, already before everything of the seven­ ties a few targeted languages like SAS or SPSS have been to be had for statisticians. Now that non-public desktops became very hot the variety of professional­ grams begin to explode. at the present time we are going to discover a wide selection of courses for nearly any statistical objective (Koch & Haag 1995).

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Missings Discreteness Mean Variance Std Dev. Minimum Maximum Range 1. quartile Median 3. 1. Some descriptive statistics of the Berlin flat data for the variables T, FA, FL, FR, FB, FM, FP, FI and FE. Missings Discreteness Mean Variance Std Dev. Minimum Maximum Range 1. quartile Median 3. 2. Some descriptive statistics of the Berlin flat data for the variables DI, DF, DU, DR, DW, DS, DN, DB and TI. 2 while the empty entries were done by hand. A statistical program would need informations about the variables to decide which descriptive statistics could be computed.

Exploratory Statistical Techniques 55 The problem with the Chernoff faces is that we do not perceive all face parts equally. For example the size of the pupil will not receive the same attention as the darkness of the hair. If we use the star diagram for all variables then they will be perceived equally. Since Chernoff faces overemphasize some variables we are able to see differences well. This is no longer the case with star diagrams. 28). In the case of Chernoff faces a tool offering such glyphs has to allow an easy reassociation with the variables of the face parts.

Orm [,11 PO . 3. Subgroup of the variable FP by the variable FE. We see that in general the prices for flats in the east part of Berlin are higher (right boxplot) than in the west part of Berlin (middle boxplot). But in the west part of Berlin we have a larger range of prices. The boxplot for the whole dataset (left boxplot) is nearly the same as for the west part of Berlin. This is due to the fact that we have only 248 observations for the east part out of 14968. Analyzing subgroups. Often it is possible to decompose a dataset into subgroups, so we are interested to know how the distribution and outside values will behave on the subgroups.

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Data Structures for Computational Statistics by Dr. Sigbert Klinke (auth.)


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