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statistics class 9

Statistics of Class 9

Mathematics and statistics play a part in almost all daily activities. They are at the heart of advances in science and technology, as well as being indispensable problem-solving and decision-making tools in many areas of life. Forecasting the weather or movements in the stock market, planning travel timetables, or calculating how much water is needed to fill a swimming pool, all rely on mathematics or statistics. Mathematical and statistical knowledge is much sought after by employers for a wide variety of jobs.

You don’t need to have studied mathematics to a high level at school to study one of our qualifications or courses in maths. There are modules you can study as part of your degree that will build up your mathematical and statistical knowledge from a starting place that’s right for you.

Great Statisticians

Sir Ronald Aylmer Fisher, FRS (1890 - 1962) was an English statistician, evolutionary biologist, and geneticist. Richard Dawkins described him as "The greatest of Darwin's successors", and the historian of statistics Anders Hald said "Fisher was a genius who almost single-handedly created the foundations for modern statistical science".His contributions to experimental design, analysis of variance, and likelihood based methods have led some to call him "The Father of Statistics". Some think that it was first Fisher who referred to the growth rate r (used in equations such as the logistic function) as the Malthusian parameter, as a criticism of the writings of Thomas Robert Malthus, who Fisher referred to "...a relic of creationist philosophy..." in observing the fecundity of nature and deducing (as Darwin did) that this therefore drove natural selection. However, it is much more likely that Fisher called r the Malthusian parameter because, in 1798, Malthus published An Essay on the Principal of Population, which contained a mathematical model of population growth that became commonly known as the Malthusian Growth Model and which contained said parameter in the following formula:

P(t)= where P0 = initial population, r = growth rate, t = time.

Statistical science is concerned with the planning of studies, especially with the design of randomized experiments and with the planning of surveys using random sampling. The initial analysis of the data from properly randomized studies often follows the study protocol.

Of course, the data from a randomized study can be analyzed to consider secondary hypotheses or to suggest new ideas. A secondary analysis of the data from a planned study uses tools from data analysis.Data analysis is divided into:

  • Descriptive statistics - The part of statistics that describes data, i.e. summarises the data and their typical properties.
  • Inferential statistics - The part of statistics that draws conclusions from data (using some model for the data): For example, inferential statistics involves selecting a model for the data, checking whether the data fulfill the conditions of a particular model, and with quantifying the involved uncertainty (e.g. using confidence intervals).

While the tools of data analysis work best on data from randomized studies, they are also applied to other kinds of data --- for example, from natural experiments and observational studies, in which case the inference is dependent on the model chosen by the statistician, and so subjective.

Mathematical statistics has been inspired by and has extended many procedures in applied statistics.

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