Manova research is
no mere number cruncher. Our entire focus revolves around
understanding the uniqueness of your data and choosing
the correct statistical technique to derive actionable
analysis from its study. Some of the commonly used statistical
techniques we use in the course of our analysis and
interpretation include:
Summarizing
data
Measures of Location,
Variation and Shape
Frequency Tables
Cross Tabulation
Histogram and Box Whisker plot
Tests of
Significance (Parametric)
One sample t test
T test for independent samples
Paired t test
Analysis of Variance (one way
and two way)
Tests of
Significance (Non Parametric)
Mann Whitney Wilcoxon’s
test
Wilcoxon’s signed rank
test
Kruskal Wallis Anova
Median Test
Mcnemar Test
Design of
Experiments
2 * 2 cross over
design
Higher Order cross over design
Balanced Incomplete Block Designs
Multivariate
Techniques
Principal Component
Analysis
Factor Analysis
Cluster Analysis
Canonical Correlation Analysis
Discriminant Analysis
Multivariate Analysis of Variance
(MANOVA)
Statistical
Modeling
Multiple Linear
Regression
Binary Logistic Regression
Multinomial Logistic Regression
Forecasting
techniques
ARIMA Modeling
Exponential Smoothing
Operational
Research (OR) techniques
Optimization Techniques
Linear programming
Integer programming
Stochastic programming
Nonlinear programming
Transportation Problems
Markov Chains
Queuing Theory
This
list is merely indicative of some of the techniques
we are familiar with and do not represent the
sum of our capabilities. Tell us about your project
and we will revert with a proposal/technique best
suited to your unique data characteristics.
