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

By Dr. Sigbert Klinke (auth.)

Since the start of the seventies laptop is accessible to exploit programmable pcs for numerous initiatives. in the course of the nineties the has built from the massive major frames to non-public workstations. these days it's not in simple terms the that's even more strong, yet workstations can do even more paintings than a first-rate body, in comparison to the seventies. In parallel we discover a specialization within the software program. Languages like COBOL for enterprise­ oriented programming or Fortran for medical computing in simple terms marked the start. The creation of non-public pcs within the eighties gave new impulses for even extra improvement, already initially of the seven­ ties a few particular languages like SAS or SPSS have been to be had for statisticians. Now that private desktops became highly regarded the variety of seasoned­ grams begin to explode. at the present time we'll discover a big variety of courses for nearly any statistical objective (Koch & Haag 1995).

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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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To see if two random variables have the same distribution or 2. to compare a random variable with a predefined distribution. For some statistical methods it is assumed that the distribution of the error has a special distribution. One of the most important coefficient to measure 32 Exploratory Statistical Techniques .. I o -2 2 Quen\IIea of StanclIIlI Nonn.. 4. 2). Since the datapoints deviate seriously from the line, it is clear that the assumption is not fulfilled that the variable FA is gaussian distributed.

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