Statistical Analysis Guidance

Statistical Tests/Procedures

Researchers from a wide variety of fields come and seek expert statistical guidance. M.R.I. provides expert guidance for the following list of the statistical data analysis procedures, tests, graphics and calculations.

Descriptive Statistics

  •  Descriptive Statistics - Summary Tables/Lists

  •  Cluster Randomization - Create Cluster Means Dataset

  •  Contingency Tables (Crosstabs / Chi-Square Test)

  •  Frequency Tables

  •  Box-Cox Transformation

  •  Data Screening

  •  Data Simulation

  •  Grubbs' Outlier Test

  •  Normality Tests

  •  Area Under Curve

  •  Circular Data Analysis

  •  Tolerance Intervals

 Probability Plots

  •  Normal Probability Plots

  •  Weibull Probability Plots

  •  Chi-Square Probability Plots

  •  Exponential Probability Plots

  •  Gamma Probability Plots

  •  Log-Normal Probability Plots

  •  Probability Plot Comparison

 Types of Regression

  •  Simple Linear Regression

  •  Multiple Regression

  •  Multiple Regression - Basic

  •  Principal Components Regression

  •  Robust Regression

  •  Multiple Regression with Serial Correlation

  •  Mediation Analysis

  •  Analysis of Covariance (ANCOVA) with Two Groups

  •  One-Way Analysis of Covariance (ANCOVA)

  •  Logistic Regression

  •  Discriminant Analysis

  •  Probit Analysis

  •  Cox Regression

  •  Count Data

  •  Poisson Regression

  •  Binomial Regression

  •  Geometric Regression

 Analysis of Variance (ANOVA)

  •  Balanced Design Analysis of Variance

  •  Box-Cox Transformation for Two or More Groups (T-Test and One-Way ANOVA)

  •  One-Way Analysis of Covariance (ANCOVA)

  •  Analysis of Covariance (ANCOVA) with Two Groups

  •  General Linear Models (GLM)

  •  General Linear Models (GLM) for Fixed Factors

  •  Repeated Measures Analysis of Variance

  •  Multivariate Analysis of Variance (MANOVA)

  •  Multivariate Analysis of Covariance (MANCOVA)

Cluster Analysis

  •  Fuzzy Clustering

  •  Hierarchical Clustering

  •  K-Means Clustering

  •  Regression Clustering

  •  Clustered Heat Maps


  •  ARIMA (Box-Jenkins)

  •  Automatic ARMA

  •  Cross-Correlations

  •  Spectral Analysis

  •  Decomposition Forecasting

  •  Harmonic Regression

 Multivariate Analysis

  •  Factor Analysis

  •  Principal Components Analysis

  •  Equality of Covariance

  •  Discriminant Analysis

  •  Hotelling's One-Sample T2

  •  Hotelling's Two-Sample T2

  •  Correspondence Analysis

  •  Loglinear Models

  •  Multidimensional Scaling

  •  Time Series Analysis

Nonparametric Tests

  •  Cochran's Q Test

  •  Conover Equal Variance Test

  •  Cumulative Incidence Curves

  •  Dunn's Test

  •  Dwass-Steel-Critchlow-Fligner Test

  •  Friedman's Rank Test

  •  Kaplan-Meier Curves

  •  Kendall's Tau Correlation

  •  Kolmogorov-Smirnov Test

  •  Two-Sample T-Test

  •  Descriptive Statistics

  •  Normality Tests

  •  Tolerance Intervals

  •  Kruskal-Wallis Tes

  •  Logrank Test

  •  Mann-Whitney U or Wilcoxon Rank-Sum Test

  •  McNemar Test

  •  ROC Curves

  •  Runs Tests (Wald-Wolfowitz Runs Test)

  •  Sign or Quantile Test

  •  Spearman-Rank Correlation

  •  Wilcoxon Signed-Rank Test

 Econometric Analysis

The resource allocation from the perspective of Economics is concerned with developing and validating theories, designing, predicting and social engineering of the behaviour of the individuals/organizations. The major role is played by the methods for analysing and understanding economic data concepts from probability and statistics. Apart from knowledge of econometric analysis, researchers are supposed to have an understanding of the relationship between economic reasoning, probability, statistics, and economic data.

The researcher must know-

  •  Variables selection techniques

  •  Detection, Consequences and Corrective measures of Heteroscedasticity.

  •  Detection, Consequences and Corrective measures of Multicollinearity.

  •  Corrective measures of Autocorrelation in Regression.

  •  Estimation for Errors-in-variables models.

  •  Checking non-normality