Extension of the basic package for easy transformation of data from different systems (ETL), quality management within SPC methodologies and for the creation of business processes.
View Full Version Analyst of Statistica you will use for fast data processing and evaluation, quality management within SPC methodologies and for business process development. The package includes several ETL tools for data transformation.
The Analyst version will be used primarily by data analysts, quality managers and technology staff for data management and analysis in manufacturing companies (e.g. as an enterprise-wide analysis system).
The application is available in desktop, network and server form.
No need to enter payment details
Import of data
Analyst is fully compatible with xlsx (including xls), csv files and with fixed-width data (e.g. in text files). It will allow you to:
- retrieve data from SQL, NoSQL and other databases,
- via integrated PI connector retrieve data from OSIsoft PI system (a popular solution for operational data management),
- import Spotfire SBDF data files,
- integrate two or more data sets into one graphical environment and a series of outputs.
The Analyst version offers automated data cleansing from duplicate, inconsistent and outlying values (or their recoding) using the so-called Data Health Check (DHC) function.
For advanced data transformation, the tool Rules Builderwhich allows you to process data from different sources according to complex rules (even using conditional expressions).
For easier processing, bring your data closer to a normal layout by using the built-in Box-Cox transformation.
In the Analyst version you can evaluate the measured data, among others, with the help of:
- classical methods descriptive, parametric and non-parametric statistics,
- exploratory analysis and visualization,
- multivariate statistical methods for data organization and classification,
- advanced linear and non-linear models,
- estimation of many variance components and accuracy in the data sets (Variance Estimation and Precision).
Statistica in this version also offers the possibility to program custom scripts in R, Python or C#. You can also use Analyst for e.g. for:
- understanding the key parameters affecting critical quality attributes (process analysis, quality control and multivariate statistical process control functions),
- design of experiments and their virtual execution (design of experiments function – Design of Experiments, test power analysis – Power Analysis and interval estimation – Interval Estimation).
Visualisation and outputs
In Analys version you can see the distribution of the obtained data and the results, among others, through histogram, line, box, point, scatter and quantile plots and other frequently used 2D and 3D imaging methods.
The results obtained can export e.g. in the form of:
simple and advanced reports,
entry into different types of databases,
MS Word (docx), MS Excel (xlsx) and text files (csv) or pdf.
- Calculators; Distributions, Pearson Product Moment Correlation Coefficient, Six Sigma
- Canonical Analysis
- Classification Trees
- Cluster Analysis
- Correspondence Analysis
- Cox Proportional Hazards Models
- Descriptive Statistics
- Design of Experiments (DOE)
- Discriminant Function Analysis
- Distribution Fitting
- Distributions & Simulation
- Dynamic Time Warping
- Extract, Transform, and Load(analytics are used to align time based data)
- Factor Analysis
- Fixed Nonlinear Regression
- General Discriminant Analysis (GDA)
- General Linear Models (GLM)
- General Partial Least Squares Models (PLS)
- General Regression Models (GRM)
- Generalized Linear/Nonlinear Models (GLZ)
- Log-Linear Analysis of Frequency Tables
- Multiple Regression
- Multidimensional Scaling (MDS)
- Nonlinear Estimation
- Nonparametric Statistics
- Power Analysis and Interval Estimation
- Multivariate Statistical Process Control (MSPC – PCA/PLS)
- Principal Components & Classification Analysis (PCCA)
- Process Analysis
- Quality Control Charts
- Reliability and Item Analysis
- Stepwise Model Builder (what-if)
- Structural Equation Modeling and Path Analysis (SEPATH)
- Survival & Failure Time Analysis
- Time series / forecasting
- t-tests and other tests of group differences
- Variance Components & Mixed Model ANOVA/ANCOVA
- Variance Estimation and Precision (VEPAC)