Writing statistical programs for procedures and methods
Statistical procedures are the methods of analyzing, manipulating, evaluating, or presenting data to make it easy to understand. These procedures are often incorporated into statistical programs for the purpose of fast and efficient data analysis. Different types of data may require different types of statistical programs as well as different procedures and methods. Here, we discuss some of the most essential software applications used in statistics today and the procedures that these programs come with.
Statistical programs are software applications used to collect, organize, analyze, interpret, and present statistical data. They can either read the data directly from a specified spreadsheet or the user can input the data directly into the software. Other times, the user can utilize a special data entry tool to capture data. There are numerous statistical programs available today. Below are the most common:
Stata: This is a unified software that provides users with a complete statistical data analysis package. It is authentic, quick, and easy to use, and provides you with countless features for data management and graphics. With Stata, you can perform procedures and methods like basic tabulations and construct advanced multilevel statistical models. You can also regulate variables, amend data sets, and compile statistics across different groups of data.
SPSS: Issued by IBM, SPSS is an analytical software that provides an abundance of statistical analytics like automated modeling, text and entity analysis, decision making, and development. Its interface is easy to use for both beginner and expert data analysts. SPSS provides advanced features that help users discover trends and patterns in both structured and unstructured data.
SAS: The SAS program is perfect for traditional linear regression and analysis of variance and meets a wide range of statistical needs. It utilizes the latest procedures and methods for statistical analysis, and the entire software is organized in such a manner that you can navigate through, access, and manage data, and build statistical models easily.
Matlab: Developed by Mathworks, Matlab is a programming language and an analytical platform used by scientists and engineers to manipulate data. It offers a large library of tools and procedures to help you perform efficient data analysis and create your own code where necessary. Even though Matlab can be a little tough to learn for beginners, it provides immense flexibility in terms of the data analysis you can perform.
R: This is a free statistical program that is widely used in research especially to analyze human behavior. It offers users great tools to simplify various concepts of data analysis and processing. Like Matlab, R can be a little difficult to learn and one needs a certain degree of proficiency in coding to be able to use it effectively.
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Statistical procedures and methods
Statistical procedures and methods allow you to perform data analysis and present results in an effective and efficient manner. Below is a breakdown of the most common procedures and methods available in statistical software applications today:
Descriptive statistics: Includes descriptive statistics, frequencies, p-p plots, ratio, and q-q plots procedures
Compare means: Includes t-test procedure, mean procedure, and one-way analysis of variance
General linear model: Includes multivariate procedures, univariate ANOVA procedures, variance components analysis, and repeated measures ANOVA
Generalized linear models: Includes generalized estimation equations
Mixed models: Includes linear mixed models
Correlate: Includes distance measures as well as bivariate and partial correlation
Regression: Includes curve estimation, linear regression, logistic regression, partial least squares, two-stage least squares, nonlinear models, and optimal scaling
Loglinear models: Includes logit, model selection, and general log-linear models
Classification: Includes k-mean clustering, two-step clustering, discriminant analysis, and hierarchical clustering
Data reduction: Includes factor analysis, optimal scaling, and correspondence analysis
Nonparametric tests: Include 1 sample K-S, K related samples, K independent samples
Time-series: Includes seasonal decomposition, model creation and application, autocorrelation, spectral analysis, and cross-correlation
Survival models: Include Kaplan Meier modes, life table, and Cox regression models
Quality control: Includes Pareto charts, control charts, and capability analysis
ROC curve: Includes model comparison and categorical response tools
Probability: Includes factorials, sample size combinations, and normal, binomial, and Poisson distributions.
Writing statistical programs for procedures and methods can be difficult if you are not familiar with the above features. If you would like professional assistance for grasping these, contact our assignment help experts.