This document outlines the statistics consultation services provided, including quantitative and qualitative data analysis, popular statistical tools and techniques like regression and hypothesis testing, and the process of identifying appropriate tests and interpreting results. Key steps include submitting requirements, receiving a quote, payment, and interpretation reports. Statistical analysis is offered for dissertations and theses work, focusing on research design, sample size determination, and justifying methods.

1. Known for Precise and Impartial
approach

2. Statistics Consultation methodology:
 Submit your requirements
 Receive a quote
 Process payment
 Interpretation reports
 Consulting

3. We perform following type of data analysis:
 Quantitative data analysis
 Qualitative data analysis
 Statistical data analysis

4. Most popular tools and techniques:
 Regression as well as correlation analysis
 Time series as well as index number
 Hypothesis testing
 Chi Square Test
 Z test
 T test
 ANOVA or even at times MANOVA
 Mann Whitney Test

6.  Qualitative data analysis
 Interpretation of data analysis
 Significance of the findings
 Parametric statistical tests
 Non parametric statistical tests
 Descriptive Statistics
 Inferential Statistics

7. We follow these steps:
 Identification of the type of data
 Assessment of the objective of the
study
 Zeroing on the exact tests that need
to be formed
 Analyses of the results of the tests as
well as interpreting the same

8. Statistician for Dissertation work on
following areas :
 Regression as well as correlation analysis
 Time series as well as index number
 Hypothesis testing
 Chi Square Test
 Z test
 T test
 ANOVA or even at times MANOVA
 Mann Whitney Test

9. Features of thesis statistics:
 In depth research design
 Sample size determination
 Justification of Research methods undertaken in research
design or even sample size chosen
 Various statistical tests – both descriptive as well as
inferential statistics
 Both parametric as well as non-parametric statistical tests
under Data Analysis
 Formulation of methods of data collection
 Justification of data collection methods
 Interpretation of the tests as well as of the answers they
yield

11. Some of the leading benefits are:
 Inclination towards quantitative techniques
 Data Analysis using E-views
 Better way of justification of any aspect of
the study
 Established frameworks to abide by
 Interpretation of data becomes much easier
and channelized
 Accepted by academic fraternity all over the
world