It examines the use of computers in statistical data analysis.
Without the loss of generality, and conserving space, the following presentation is in the context of small sample size, allowing us to see statistics in 1, or 2-dimensional space.
This course will bring out the joy of statistics in you.
These statistical techniques give inferences about the arithmetic mean (which is intimately connected with the least-squares error measure); however, the arithmetic mean of log-transformed data is the log of the geometric mean of the data.
The aim is a better understanding by visualization in 2-or-3 dimensional space, and to generalize the ideas for higher dimensions by analytic thinking.
The birth of statistics occurred in mid-17 century.
The probability distribution of the statistic upon which the the analysis is based is not dependent upon specific information or assumptions about the population(s) which the sample(s) are drawn, but only on general assumptions, such as a continuous and/or symmetric population distribution.
There are few different schools of thoughts in statistics.
As a general guideline, statisticians have used the prescription that if the parent distributionis symmetric and relatively short-tailed, then the sample mean reaches approximate normality forsmaller samples than if the parent population is skewed or long-tailed.
Computers play a very important role in statistical data analysis.
Other modeling approaches include structural and classical modeling such as Harvey, and Box-Jenkins approaches, co-integration analysis and general micro econometrics in probabilistic models, e.g., Logit, Probit and Tobit, panel data and cross sections.
The first activity in statistics is to measure or count.
The statistical bootstrap, which uses resampling from a given set of data to mimic the variability that produced the data in the first place, has a rather more dependable theoretical basis and can be a highly effective procedure for estimation of error quantities in statisticalproblems.
in the meta analysis literature ...
The main idea of statistical inference is to take a random sample from a population and then to usethe information from the sample to make inferences about particular population characteristics such as the mean (measure of central tendency), the standard deviation (measure of spread) or the proportion of units in the population that have a certain characteristic.
The standard test for normality is the statistic.
What About the Zipf's Law? Benford's Law states that if we randomly select a number from a table of physical constants or statistical data, the probability that the first digit will be a "1" is about 0.301, rather than 0.1 as we might expect if all digits were equally likely.