Summarizing Data Essay introduction.
Overview[ edit ] 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".
Ideally, statisticians compile data about the entire population an operation called census. This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. Numerical descriptors include mean and standard deviation for continuous data types like incomewhile frequency and percentage are more useful in terms of describing categorical data like race.
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. However, the drawing of the sample has been subject to an element of randomness, hence the established numerical descriptors from the sample are also due to uncertainty. To still draw meaningful conclusions about the entire population, inferential statistics is needed.
It uses patterns in the sample data to draw inferences about the population represented, accounting for randomness. These inferences may take the form of: Inference can extend to forecastingprediction and estimation of unobserved values either in or associated Statistical analysis system summarizing data the population being studied; it can include extrapolation and interpolation of time series or spatial dataand can also include data mining.
Sampling[ edit ] 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. The idea of making inferences based on sampled data began around the mids in connection with estimating populations and developing precursors of life insurance.
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.
Statistics offers methods to estimate and correct for any bias within the sample and data collection procedures. There are also methods of experimental design for experiments that can lessen these issues at the outset of a study, strengthening its capability to discern truths about the population.
Sampling theory is part of the mathematical discipline of probability theory. Probability is used in mathematical statistics to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures.
The use of any statistical method is valid when the system or population under consideration satisfies the assumptions of the method.
The difference in point of view between classic probability theory and sampling theory is, roughly, that probability theory starts from the given parameters of a total population to deduce probabilities that pertain to samples. Statistical inference, however, moves in the opposite direction— inductively inferring from samples to the parameters of a larger or total population.
Experimental and observational studies[ edit ] A common goal for a statistical research project is to investigate causalityand in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables.
There are two major types of causal statistical studies: In both types of studies, the effect of differences of an independent variable or variables on the behavior of the dependent variable are observed.
The difference between the two types lies in how the study is actually conducted. Each can be very effective. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.
In contrast, an observational study does not involve experimental manipulation. Instead, data are gathered and correlations between predictors and response are investigated. While the tools of data analysis work best on data from randomized studiesthey are also applied to other kinds of data—like natural experiments and observational studies  —for which a statistician would use a modified, more structured estimation method e.
Experiments[ edit ] The basic steps of a statistical experiment are: Planning the research, including finding the number of replicates of the study, using the following information: Consideration of the selection of experimental subjects and the ethics of research is necessary.
Statisticians recommend that experiments compare at least one new treatment with a standard treatment or control, to allow an unbiased estimate of the difference in treatment effects.Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).
Traditional methods for statistical analysis – from sampling data to interpreting results – have been used by scientists for thousands of years. But today’s data volumes make statistics ever more valuable and powerful.
Affordable storage, powerful computers and advanced algorithms have all led to an increased use of computational statistics. The RETAIN statement prevents SAS from reinitializing the values of new variables at the top of the DATA step.
General form of the RETAIN statement: RETAIN variable-name ; Previous values of retained variables are available for processing across iterations of the DATA step. SAS/STAT includes exact techniques for small data sets, high-performance statistical modeling tools for large data tasks and modern methods for analyzing data with missing values.
And because the software is updated regularly, you'll benefit from using the newest methods in the rapidly expanding field of statistics.
Statistics is a science dealing with the collection, analysis, interpretation and presentation of numerical data. Descriptive versus Inferential Statistics Population is a collection of persons, objects or items of interest. Lattice Plotting System ggplot2 Summarizing Data Hierarchical Clustering.
K-Means Clustering. Basic Statistical Analysis in R. This class focuses on inferential statistics in R. After the data munging of the data, it is ready for basic statistical analysis such as hypothesis testing.
If the class has a background in both statistics and.