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In statistics what is summation?
in statistics, summation denoted by upper case sigma, is used to find the sum of a series of observation in a particular variable.
What is the distribution of area under a curve?
In terms of probability theory, the cumulative distribution function (cdf) is the result of the summation or integration of the probability density function (pdf). The cdf F(a) is the area under the pdf from its lower limit to a. I hope I am responding to your question. If not, perhaps you can clarify it and resubmit it.
What is the differences between spartial data and attribute data?
Spartial data shows specific location of geographical phenomena in terms of coordinates whilst attribute data is non-spatial in that it does not use coordinates but only show what is on a point, line and polygon. by Wilkins Nyamangunda at Midlands State University(2:1)
Which of the following measures of central tendency would be appropriate to describe the average pulse rates group of surgical patients?
Mean is the answer. Because if you want to get for the AVERAGE no. of pulse or an AVERAGE of any data, the most appropriate way is to get the summation of the data then divided it into the number of sample. Therefore you will get the average. In some case, like if you want to get the age of people who is mostly has a problem in their circulation of blood including the high-blooded and low-blooded it is best to use MODE since you were looking for the most age of occurrence. It is best to analyse your data first, know what you needed you solve or what you needed to answer. God bless.
Is it a simple transformation of technology developed from databases statistics and machine learning?
No. Data mining is more than a simple transformation of technology developed from databases, statistics, and machine learning. Instead, data mining involves an integration, rather than a simple transformation, of techniques from multiple disciplines such as database technology, statistics, machine learning, high-performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis.
