Research • Industry • Quality • Decision Analytics
edustatistic@gmail.com +91 95397 84102 +91 95397 78930
Evidence-driven analytical support

From complex data to clear, defensible decisions.

EDUSTAT Research and Analytics provides comprehensive statistical analysis, research analytics, industrial data analysis and Six Sigma support for academic, institutional, business and process-improvement projects.

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Method-first analysis Transparent reporting Confidential data handling Decision-ready outputs
Academic & Institutional Research Analytics
Manufacturing & Services Industrial Analytics
Operational Excellence Six Sigma Support
Management & Strategy Decision Analytics
What we do

End-to-end analytical services for research and real-world operations.

Engagements can begin with a research question, an existing dataset, a recurring business problem, a quality issue or a decision that needs rigorous analytical evidence.

Industrial Data Analytics

Quantitative analysis for production, operations, quality, reliability, supply chains, service systems and process performance.

  • Production and process performance analysis
  • Defect, downtime and yield analytics
  • Trend, variance and root-cause analytics
  • Reliability and maintenance data analysis
  • Operational dashboards and KPI frameworks

Six Sigma & Quality Analytics

Statistical and analytical support across DMAIC and DMADV projects, process capability, measurement systems, control, experimentation, design and improvement validation.

  • DMAIC and DMADV analytical support
  • SPC and control-chart analysis
  • Process capability and performance studies
  • Measurement System Analysis (MSA)
  • Design of Experiments (DOE)

Business & Decision Analytics

Transform structured business data into evidence for planning, segmentation, performance improvement, forecasting and management decisions.

  • Sales, customer and market analytics
  • Segmentation and profiling
  • Forecasting and scenario analysis
  • Performance scorecards
  • Executive analytical summaries

Forecasting & Time-Series Analysis

Analyse temporal patterns, seasonality, trend and uncertainty for forecasting, planning and performance monitoring.

  • Trend and seasonal decomposition
  • ARIMA / SARIMA-type workflows
  • Forecast accuracy evaluation
  • Intervention and before–after analysis
  • Time-based KPI monitoring

Dashboards, Reporting & Data Communication

Clear visual reporting for researchers, managers and decision-makers, with an emphasis on traceability and interpretation rather than decoration.

  • KPI and analytical dashboards
  • Publication-ready charts and tables
  • Executive and technical reporting
  • Data storytelling and presentation support
  • Reusable reporting structures
Analytical depth

Methods selected by the question—not by habit.

Analytical technique selection should follow the research question, design, data type, measurement structure, assumptions, sample characteristics and the intended analytical objective of the study.

Descriptive Statistics Data Screening Reliability Analysis Validity Analysis t Tests ANOVA / MANOVA ANCOVA / MANCOVA Non-parametric Tests Correlation Linear Regression Logistic Regression Multiple Regression EFA CFA SEM Path Analysis Mediation Moderation Cluster Analysis PCA Time Series Forecasting Control Charts Process Capability MSA / Gage R&R DOE Pareto Analysis Hypothesis Testing Power & Sample Size Data Visualization Qualitative Coding Thematic Analysis Content Analysis Framework Analysis Mixed-Methods Integration
Research analytics

Support across the full research analytics cycle.

EDUSTAT can support quantitative, qualitative and mixed-method analytical stages from study planning through defensible interpretation. The emphasis is on alignment among research objectives, questions or hypotheses, data sources, measurement structure, data quality and analytical method.

Research Design Alignment

Map objectives and hypotheses to variables, scales, comparison groups and appropriate analytical procedures.

Sampling & Sample Size

Support for sample-size justification, sampling plans, strata, clusters and allocation logic where appropriate.

Data Cleaning & Diagnostics

Missing data, outliers, coding errors, distribution checks, multicollinearity and model-specific assumptions.

Statistical & Qualitative Analysis

Statistical modelling and qualitative analytical approaches matched to the study design, data type and research objective.

Tables & Figures

Clear analytical tables and figures suitable for theses, reports, presentations and manuscripts.

Interpretation & Reporting

Analytically accurate interpretation that distinguishes statistical evidence, qualitative meaning, uncertainty and practical significance.

Industrial & operational analytics

Measure variation. Locate causes. Improve performance.

Industrial analytics integrates statistical reasoning with operational context. The purpose is not only to describe historical performance, but to identify controllable drivers and support sustained improvement.

Process Performance

Cycle time, throughput, yield, productivity, defect rate, scrap, rework, downtime and other operational KPIs.

Quality & Variation

Common-cause versus special-cause variation, control limits, stability, capability and defect pattern analysis.

Reliability & Maintenance

Failure patterns, downtime structure, maintenance performance, trend analysis and reliability-oriented summaries.

Root-Cause Analytics

Pareto prioritisation, stratification, correlation, regression, hypothesis testing and designed comparisons.

Planning & Forecasting

Demand, workload, production, inventory and resource patterns analysed through appropriate forecasting frameworks.

Management Reporting

Operational dashboards and concise reports that translate technical findings into actionable management information.

Six Sigma services

Analytical support across DMAIC and DMADV Six Sigma methodologies.

Projects can be supported at the full-cycle level or at specific analytical stages, depending on the organisation’s internal improvement structure and available data.

DMAIC & DMADV Analytics

Structured analytical support for improving existing processes through DMAIC and for designing or redesigning processes, products or services through DMADV.

DDefine
MMeasure
AAnalyse
IImprove
CControl
DDefine
MMeasure
AAnalyse
DDesign
VVerify

Quality Engineering Toolkit

Process capability: Cp, Cpk, Pp, Ppk
Statistical Process Control
MSA and Gage R&R
Design of Experiments
Before–after validation
Defect and Pareto analysis
Hypothesis testing
Control-plan analytics
Research domains

Cross-disciplinary analytical support.

Statistical principles generalise across disciplines, while measurement, design, terminology and interpretation remain domain-sensitive.

EducationLearning, assessment, intervention and institutional studies
Commerce & ManagementFinance, marketing, HR, banking and organisational research
Social SciencesSurvey, behavioural and community research
EconomicsEconomic indicators, relationships and time-dependent analysis
Health & Life SciencesQuantitative analysis subject to applicable ethical and professional requirements
EngineeringExperimental, process, reliability and quality data
Business OperationsCustomer, sales, service and performance analytics
Industrial SystemsManufacturing, quality, productivity and process improvement
How engagements work

A disciplined workflow from question to decision.

Every project is different, but a rigorous analytics engagement usually follows a consistent sequence: clarify the problem, evaluate the data, select defensible methods, validate results and communicate findings in a form that can be used.

01

Define the analytical objective

Clarify the research question, business problem, hypotheses, KPIs, outputs and decision context before selecting techniques.

02

Audit the data and measurement structure

Review variables, coding, scale structure, data quality, missingness, outliers, group definitions and measurement constraints.

03

Build the analysis plan

Select methods that match the design, data type and assumptions; define model sequence, diagnostics and reporting requirements.

04

Analyse, diagnose and validate

Execute the analysis, inspect model assumptions, evaluate robustness and resolve data or specification issues transparently.

05

Translate results into usable evidence

Deliver tables, figures and interpretation that connect numerical findings to the original question without overstating conclusions.

Technology-flexible workflow

Analysis can be adapted to the project environment.

Depending on the analytical requirement and the client’s preferred workflow, projects may involve commonly used statistical, spreadsheet, programming, modelling and visualisation environments.

Minitab for Six Sigma & Quality Analytics SPSS-compatible workflows R-based analysis Python analytics Excel data workflows SEM / CFA environments Dashboard tools Quality-analysis workflows Reproducible analytical scripts
Working principles

High-quality analytics requires more than software output.

Statistical software can calculate. Professional analytical work must also justify, diagnose, interpret and communicate.

Methodological Fit Methods are matched to the question, design and data-generating structure.
Assumption Awareness Diagnostics and limitations are considered rather than hidden behind p-values.
Confidentiality Client data should be handled only for the agreed analytical purpose and with appropriate care.
Interpretive Restraint Results are stated at the level supported by the design, without unjustified causal or practical claims.
Frequently asked questions

Clear expectations before analysis begins.

What types of research projects can EDUSTAT analyse?

EDUSTAT can support quantitative, qualitative and mixed-method research across many disciplines, provided the work is legitimate, the data are appropriate for the intended analysis and applicable academic, ethical and professional requirements are respected.

Can you work with data that have already been collected?

Yes. Existing quantitative datasets, qualitative material such as interview or focus-group transcripts, open-ended responses and other research data can be reviewed for structure, coding, completeness, consistency and analytical suitability before the main analysis is performed.

Can you help select the correct statistical tests?

Yes. Test selection should be based on the research objective, variable type, number and independence of groups, measurement level, assumptions and the design of the study—not simply on the most familiar software command.

Do you provide advanced analyses such as CFA, SEM, mediation or moderation?

These analyses can be included when they are methodologically justified and the sample, measurement model and data quality are adequate for the requested model.

What can be included in an industrial analytics engagement?

Industrial engagements may include data preparation, KPI analysis, variation analysis, process capability, control-chart analysis, defect patterns, downtime, reliability summaries, forecasting, regression, designed comparisons, dashboards and improvement verification.

Do you support Six Sigma projects?

EDUSTAT can provide analytical support for both DMAIC and DMADV Six Sigma projects, including measurement evaluation, statistical process control, process capability, hypothesis testing, regression, Design of Experiments and design-verification analysis where appropriate.

Can results be prepared for a thesis, research report or journal manuscript?

Analytical outputs can be structured for academic or professional reporting, including tables, figures, concise interpretation and documentation of the methods used. The researcher or author remains responsible for compliance with institutional and publication requirements.

How should I send an enquiry?

Use the contact form below, email EDUSTAT at edustatistic@gmail.com, or call +91 95397 84102 / +91 95397 78930.

Contact EDUSTAT

Tell us what you need the data to answer.

Start with the problem

Need a defensible analytical plan for your data?

Share the objective, available data and expected output. EDUSTAT can help structure the analysis before unnecessary or unsuitable statistical procedures are applied.

Email EDUSTAT