Population Census (2008): 8.26 millionGDP Growth: -2.30% (2021)Inflation Rate: 58.21% (May, 2025)
Population Census (2008): 8.26 millionGDP Growth: -2.30% (2021)Inflation Rate: 58.21% (May, 2025)

Statistical Methods

Comprehensive statistical methodologies for data collection, analysis, and interpretation to support evidence-based decision making and research.

Statistical Method Categories

We employ a wide range of statistical methods tailored to different data types, research questions, and analytical needs.

Descriptive Statistics

Methods for summarizing and describing the main features of datasets through measures of central tendency, dispersion, and distribution.

Measures of Central Tendency Dispersion Measures Frequency Distributions
Complexity: Basic

Inferential Statistics

Techniques for making predictions or inferences about populations based on sample data, including hypothesis testing and confidence intervals.

Hypothesis Testing Confidence Intervals Statistical Significance
Complexity: Intermediate

Regression Analysis

Statistical process for estimating relationships among variables, widely used for prediction and forecasting.

Linear Regression Logistic Regression Multiple Regression
Complexity: Advanced

Time Series Analysis

Methods for analyzing time-ordered data points to extract meaningful statistics and identify trends, seasonal patterns, and cycles.

Trend Analysis Seasonal Decomposition Forecasting
Complexity: Advanced

Multivariate Analysis

Techniques for analyzing data that contain more than one variable, focusing on understanding the relationships between them.

Factor Analysis Cluster Analysis Principal Components
Complexity: Advanced

Sampling Methods

Techniques for selecting a subset of individuals from a statistical population to estimate characteristics of the whole population.

Probability Sampling Stratified Sampling Cluster Sampling
Complexity: Intermediate

Method Selection Guide

Use this guide to select appropriate statistical methods based on your research questions and data characteristics.

By Research Objective

Describe Characteristics

When you need to summarize and describe the main features of your data.

Descriptive Statistics Frequency Tables

Compare Groups

When you need to compare means or proportions between different groups.

T-tests ANOVA Chi-square tests

Predict Outcomes

When you need to predict an outcome based on one or more predictor variables.

Regression Analysis Time Series

By Data Type

Continuous Data

Numerical data that can take any value within a range.

T-tests ANOVA Correlation

Categorical Data

Data that can be divided into groups or categories.

Chi-square tests Logistic Regression Contingency Tables

Time Series Data

Data collected sequentially over time at regular intervals.

Trend Analysis Seasonal Decomposition ARIMA Models

Practical Applications

Our statistical methods are applied across various sectors to address real-world challenges and inform decision-making.

Public Health

Epidemiological studies, clinical trial analysis, health outcome prediction, and disease surveillance.

  • Disease prevalence estimation
  • Treatment effectiveness analysis
  • Health risk factor identification

Economic Analysis

Economic forecasting, market research, price analysis, and economic impact assessment.

  • GDP growth forecasting
  • Inflation trend analysis
  • Labor market statistics

Policy Evaluation

Program impact assessment, policy effectiveness analysis, and social program evaluation.

  • Policy impact measurement
  • Cost-benefit analysis
  • Program effectiveness evaluation

Environmental Studies

Climate change analysis, environmental monitoring, resource management, and ecological studies.

  • Climate trend analysis
  • Environmental impact assessment
  • Resource utilization patterns

Education Research

Educational assessment, learning outcome analysis, and educational program evaluation.

  • Student performance analysis
  • Educational intervention impact
  • Literacy rate assessment

Social Research

Demographic analysis, social trend monitoring, and quality of life assessment.

  • Population demographics
  • Social indicator analysis
  • Quality of life measurement

Statistical Tools & Software

We utilize industry-standard statistical software to implement advanced methodologies and ensure accurate results.

SPSS

Statistical analysis and data management

Stata

Data analysis and statistical software

R

Programming for statistical computing

Python

Data analysis and machine learning

Our statisticians are proficient in multiple statistical software packages and can recommend the most appropriate tool for your specific analysis needs.

Consult Our Experts

Our Methodology Process

We follow a rigorous process to ensure statistical validity and reliability in all our analyses.

Problem Definition & Research Design

We begin by clearly defining the research question, objectives, and determining the appropriate study design and methodology.

Data Collection & Preparation

We design data collection instruments, implement sampling strategies, and prepare data for analysis through cleaning and validation.

Method Selection & Application

We select appropriate statistical methods based on data characteristics and research objectives, then apply these methods rigorously.

Interpretation & Reporting

We interpret results in context, assess statistical significance and practical importance, and prepare comprehensive reports with actionable insights.