1 edition of Basic statistics found in the catalog.
Includes bibliographical references (p. 454-459) and index.
|Statement||Brooks/Cole Pub. Co.|
|Publishers||Brooks/Cole Pub. Co.|
|The Physical Object|
|Pagination||xvi, 56 p. :|
|Number of Pages||47|
nodata File Size: 3MB.
Entwicklung Eines Verfahrens zur Wertmäßigen Bestimmung der Produktivität und Wirtschaftlichkeit von Personalentwicklungmaßnahmen in Arbeitsstrukturen ...
Statistics may be used to acquire deeper insights into how information is arranged, which can then be used to use data science approaches to gain more information.
The identification of multivariate outliers is also considered. This means that if the same measurement was taken again, the result would most likely vary. For example, you have nearly 200 instances for class 5, but just 20 for class 6. Correlating the data and building models that predict business outcomes Step 7: Optimize and Repeat The data analysis is a repeatable process and sometime leads to continuous improvements, both to the business and to the data Basic statistics chain itself.
Statistics is an effective tool for conducting data science tasks. More advanced statistical modeling can be found in the section. Suppose, through your research you are trying to find if there was a relationship between height and weight of human, it would make sense to measure the height and weight of dogs using a scale.
The data can be used in a more informative and focused manner. Probability is a mathematical language for discussing uncertain events, and it is an important part of statistics.
Uncertainty and variation are two key concepts in statistics.
In some circumstances, the uncertainty stems from the fact that the outcome has not yet been determined for example, we may not know whether it will rain tomorrow , while in others, the doubt stems from the fact that the conclusion has already been established but we are unaware of it.
visualisation, such as a bar chart, can present you with some high-level data, but only if you use statistics.
Too often Data scientists correct spelling mistakes, handle missing values and remove useless information.