Cumulative table

Part 1 of ebook "Statistics (Eleventh edition)" provides readers with contents including: descriptive statistics organizing and summarizing data; describing data with tables and graphs; normal distributions and standard (z) scores; inferential statistics: generalizing beyond data; sampling distribution of the mean;...

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Lecture "Probability & statistics - Chapter 2: Tables & charts" has contents: Frequency, relative frequency, cumulative table; pie chart; bar chart, column chart; histogram; line chart; radar chart; rcatter plot, bubble chart.

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Chapter 2 - Describing data: Frequency tables, frequency distributions, and graphic presentation. After completing this unit, you should be able to: Organize qualitative data into a frequency table, present a frequency table as a bar chart or a pie chart, organize quantitative data into a frequency distribution, present a frequency distribution for quantitative data using histograms, frequency polygons, and cumulative frequency polygons.

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Chapter 6 - Continuous random variables. After mastering the material in this chapter, you will be able to: Define a continuous probability distribution and explain how it is used, use the uniform distribution to compute probabilities, describe the properties of the normal distribution and use a cumulative normal table, use the normal distribution to compute probabilities,...

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If a node calculates that it does not have enough storage capacity for the table, it initiates the group formation algo- rithm. To minimize the number of times an original tuple must be transmitted to make it available to every member of a group, we require that all nodes in the group are within broadcast range of each other. A second required property of a group is that it must have enough cumulative storage capacity to accommodate the table of predicates. If these requirements can not be met, the join classification (see Section 3.2) is not intermediate but rather...

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When the predicates table does not fit on one node, joins can no longer be performed strictly locally. Instead, the table must be horizontally partitioned. A tuple can only immediately join with the local partition at the node and must be shipped to other nodes to complete the join. Once the original tuple has reached every node that contains a partition of the table, it can be dropped and results can be forwarded to the root.

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