"Comparison of Broad Statistical tools Across Key Attributes"

 

"Comparison of Broad Statistical tools Across Key Attributes"
Sujai balasubramaniam

Sr. No.	Statistical Tool	Purpose	Data Type	Complexity Level	Typical Applications	Ease of Use	Strengths	Limitations
1	Descriptive Statistics	Summarize data	Quantitative	Low	Data reporting, exploratory analysis	High	Easy to interpret, provides quick insights	No analysis of relationships
2	Correlation Analysis	Assess variable relationships	Quantitative	Low to Medium	Quality control, customer behaviour analysis	High	Indicates strength and direction of association	No causal implication
3	Hypothesis Testing	Test significance of findings	Quantitative	Low to Medium	Quality assurance, experimental validation	High	Provides scientific validation	Prone to Type I/II errors
4	T-Test	Compare means of two groups	Quantitative	Low	A/B testing, experiments	High	Common, simple, and interpretable	Only for two-group comparison
5	Chi-Square Test	Test associations in categories	Categorical	Low to Medium	Demographics, behavior analysis	High	Ideal for nominal or categorical data	Needs large sample sizes
6	ANOVA (Analysis of Variance)	Compare multiple groups means	Quantitative	Medium	Clinical trials, product testing	Medium	Tests group differences effectively	Sensitive to normality assumption
7	Regression Analysis	Predict relationships	Quantitative	Medium	Risk modeling, trend analysis	Medium	Shows predictive relationships	Limited by non-linear patterns
8	Factor Analysis	Data reduction, uncover patterns	Quantitative	High	Survey data, psychological research	Medium	Reveals hidden structure among variables	Complex, subjective interpretation
9	Principal Component Analysis (PCA)	Reduce dimensionality	Quantitative	High	Image compression, exploratory data analysis	Medium	Reduces redundancy, highlights key features	Difficult to interpret
Sr. No.	Statistical Tool	Purpose	Data Type	Complexity Level	Typical Applications	Ease of Use	Strengths	Limitations
10	Control Charts	Monitor process stability	Quantitative	Low	Manufacturing, quality control	High	Visualizes process changes over time	Assumes stable process
11	Kaplan-Meier Estimator	Estimate survival probability	Quantitative	Low	Medical research, customer retention	High	Visualizes survival function	Limited for complex time-dependent variables
12	Log Rank Test	Compare survival curves	Quantitative	Medium	Medical research, reliability testing	Medium	Effective for time-to-event data	Assumes proportional hazards
13	Time Series Analysis	Analyse data over time	Quantitative	High	Economic forecasting, sales trends	Medium	Captures seasonal and trend patterns	Requires continuous data
14	Canonical Correlation Analysis (CCA)	Assess multivariable relationships	Quantitative	High	Environmental studies, genomics	Low to Medium	Analyses complex, multiple-variable relationships	Computationally intensive
15	Conjoint Analysis	Assess consumer preferences	Categorical	Medium	Marketing, product design	Medium	Reveals preferences, useful for market segmentation	Complex survey design
16	Multidimensional Scaling (MDS)	Visualize similarity of data points	Quantitative	Medium	Market research, perceptual mapping	Medium	Useful for visualizing high-dimensional data	Limited interpretability
17	Cluster Analysis	Group data points	Quantitative	Medium to High	Market segmentation, genetics	Medium	Identifies natural clusters or segments	Sensitive to outliers and scaling
18	Kruskal-Wallis Test	Non-parametric comparison of groups	Quantitative	Medium	Non-parametric studies, ordinal data	High	Useful when normality assumptions aren’t met	Sensitive to tied ranks
Sr. No.	Statistical Tool	Purpose	Data Type	Complexity Level	Typical Applications	Ease of Use	Strengths	Limitations
19	Canonical Correspondence Analysis (CCA)	Ecological multivariate analysis	Quantitative	High	Ecology, environmental studies	Medium	Handles multiple response variables	Computationally intensive
20	Partial Least Squares Regression (PLS)	Reduce predictors	Quantitative	High	Chemometrics, genomics	Medium	Handles multicollinearity, suitable for small samples	Interpretation can be complex
21	Multivariate Analysis of Variance (MANOVA)	Compare multiple dependent variables	Quantitative	High	Experimental design, psychology	Low	Analyses multiple outcome variables	Requires balanced data
22	Gage R&R	Assess measurement consistency	Quantitative	Low	Manufacturing, quality control	High	Measures repeatability and reproducibility	Limited to measurement system evaluation
23	Survival Analysis	Time-to-event modeling	Quantitative	Medium to High	Medical survival studies, customer retention	Medium	Models timing effectively for life data	Sensitive to censored data
24	Decision Trees	Visualize decision paths	Mixed	Medium	Business intelligence, customer decisions	High	Easy to interpret, shows paths to decisions	Prone to overfitting
25	Structural Equation Modeling (SEM)	Complex relationships analysis	Quantitative	High	Behavioural science, market research	Low	Allows complex, multivariate relationships	Requires large sample sizes
26	Logistic Regression	Predict binary outcomes	Categorical	Medium	Medical diagnosis, fraud detection	High	Great for binary outcomes	Limited to binary and linear outcomes
Sr. No.	Statistical Tool	Purpose	Data Type	Complexity Level	Typical Applications	Ease of Use	Strengths	Limitations
27	Bootstrap Resampling	Estimate statistics with resampling	Mixed	Medium	Small sample analysis, machine learning	Medium	No assumption of normality	Computationally intensive
28	Latent Class Analysis (LCA)	Identify hidden subgroups	Categorical	High	Psychometrics, marketing research	Medium	Useful for segmentation	Requires large datasets
29	Ridge Regression	Handle multicollinearity in data	Quantitative	Medium	Finance, predictive modeling	Medium	Addresses multicollinearity, prevents overfitting	Less interpretable than linear regression
30	Cox Proportional Hazards Model	Assess hazard ratios over time	Quantitative	Medium to High	Clinical trials, survival analysis	Medium	Handles censored data	Assumes proportional hazards
31	Entropy Analysis	Measure randomness	Quantitative	Medium	Information theory, machine learning	Medium	Quantifies uncertainty and information content	Limited to categorical/ordinal data
32	Lasso Regression	Variable selection, regularization	Quantitative	Medium	Feature selection, predictive analytics	Medium	Shrinks irrelevant coefficients to zero	Can ignore useful variables
33	Monte Carlo Simulation	Model uncertainty	Quantitative	High	Engineering, risk analysis	Medium	Model’s complex random processes	Requires high computation
34	K-Means Clustering	Simple clustering	Quantitative	Medium	Customer segmentation, basic clustering	Medium	Fast, effective for simple clustering	Sensitive to initial clusters
35	Generalized Additive Models (GAM)	Non-linear modeling	Quantitative	High	Predictive analytics, medicine	Medium	Captures non-linear patterns	Difficult to interpret
Sr. No.	Statistical Tool	Purpose	Data Type	Complexity Level	Typical Applications	Ease of Use	Strengths	Limitations
36	Mixed-Effects Models	Account for fixed and random effects	Quantitative	High	Longitudinal studies, multi-level data	Low	Handles nested data effectively	Complex interpretation
37	Hierarchical Bayes Estimation	Multi-level parameter estimation	Quantitative	High	Marketing, predictive modeling	Low	Provides parameter estimates at multiple levels	Complex computation
38	Support Vector Machines (SVM)	Classification, regression	Quantitative	High	Image recognition, text classification	Low to Medium	Effective for high-dimensional data	Requires careful tuning
39	Neural Networks	Complex pattern recognition	Mixed	Very High	Image recognition, deep learning	Low	Excels at capturing complex patterns	Needs large datasets, complex interpretation
40	Conditional Random Fields (CRFs)	Model structured predictions	Mixed	Very High	NLP, sequence prediction	Low	Great for sequential, contextual data	Requires large datasets, high computation