Data and statistical analysis - HaagsehonderdNl Data and statistical analysis - HaagsehonderdNl

Data and statistical analysis

Data and statistical analysis

Enter the characters you see below Sorry, we just need to make sure you’re not a robot. Enter the characters you see below Sorry, we just need to make sure you’re not a data and statistical analysis. Statistics used in standardized testing assessment are shown. Scatter plots are used in descriptive statistics to show the observed relationships between different variables.

Statistics is a branch of mathematics dealing with the collection, organization, analysis, interpretation and presentation of data. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model.

Measurement processes that generate statistical data are also subject to error. Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. In more recent years statistics has relied more on statistical software to produce tests such as descriptive analysis. Merriam-Webster dictionary defines statistics as «a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data. Statistician Sir Arthur Lyon Bowley defines statistics as «Numerical statements of facts in any department of inquiry placed in relation to each other. Statistics is a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data, or as a branch of mathematics.

Mathematical statistics is the application of mathematics to statistics. In applying statistics to a problem, it is common practice to start with a population or process to be studied. Populations can be diverse topics such as «all persons living in a country» or «every atom composing a crystal». This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. When a census is not feasible, a chosen subset of the population called a sample is studied.

Once a sample that is representative of the population is determined, data is collected for the sample members in an observational or experimental setting. Again, descriptive statistics can be used to summarize the sample data. When full census data cannot be collected, statisticians collect sample data by developing specific experiment designs and survey samples. Statistics itself also provides tools for prediction and forecasting through statistical models. To use a sample as a guide to an entire population, it is important that it truly represents the overall population. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole.

A major problem lies in determining the extent that the sample chosen is actually representative. Sampling theory is part of the mathematical discipline of probability theory. A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables. Planning the research, including finding the number of replicates of the study, using the following information: preliminary estimates regarding the size of treatment effects, alternative hypotheses, and the estimated experimental variability. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. Further examining the data set in secondary analyses, to suggest new hypotheses for future study.

Documenting and presenting the results of the study. Experiments on human behavior have special concerns. The famous Hawthorne study examined changes to the working environment at the Hawthorne plant of the Western Electric Company. An example of an observational study is one that explores the association between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a cohort study, and then look for the number of cases of lung cancer in each group.

Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one transformation. The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions. The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations. A statistic is a random variable that is a function of the random sample, but not a function of unknown parameters. The probability distribution of the statistic, though, may have unknown parameters.

Consider now a function of the unknown parameter: an estimator is a statistic used to estimate such function. A random variable that is a function of the random sample and of the unknown parameter, but whose probability distribution does not depend on the unknown parameter is called a pivotal quantity or pivot. Between two estimators of a given parameter, the one with lower mean squared error is said to be more efficient. The best illustration for a novice is the predicament encountered by a criminal trial. The null hypothesis, H0, asserts that the defendant is innocent, whereas the alternative hypothesis, H1, asserts that the defendant is guilty. The indictment comes because of suspicion of the guilt.