There are so many possibilities for analysis!

Bayesian Analysis, Categorical Data Analysis, Categorical Trees, Census Data Analysis, Classification Trees, Cook’s Distance, Cross-Validation, Data Mining, Density Estimation, Discrete Multivariate Analysis, Discriminant Function Analysis, Distributions, Duncan’s Test, Dunnett’s Test, Experimental Design, Exploratory Data Analysis, Factor Analysis, Forecasting, General Linear Models (GLIM), Gravity Modeling Theory, Hazard Function, Inductive versus Deductive Reasoning, Local Minima/Maxima, Logit Regression, Model Criticism, Multidimensional Scaling, Multinomial Logit and Probit Regression, Multivariate Time Series Analysis, Network Analysis, Newman-Kuels Test, Nonlinear Regression Analysis, Panel Data, Parallel Coordinate Analysis, Predictive Modeling, Principal component analysis, Probability Theory, Probit Regression, Quality and Productivity, Random Variables, Regression Trees, Reliability, Response surface, Sample survey, Segmentation Theory and Analysis, Spectral plot, Spatial Interaction Models, Spline fitting, Statistical Consulting Skills (technical jargon, asking good questions, listening skills, negotiating fair exchanges, talking about statistics, and resolving difficult situations), Statistical Software (S-Plus, SAS, Excel, R, etc.), Survival Data Analysis, Variance components (In Mixed Model ANOVA), Visual Display of Quantitative Information, Wald Statistic, Weighted Regression Analysis and Yates Corrected Chi-Square, Analysis of Variance, Bayesian Inference, Chi-square Test, Cluster Analysis, Confidence Interval for the mean, Confidence Interval Theory, Confidence Level, Continuous Distributions, Contouring, Mallows Cp statistic, Data Smoothing, Exploratory Data Analysis (Data Mining), External Model Validation, Extrapolation, Frequentist Statistics, Gravity Model Application, Hypothesis Testing, Intermediate Probability Theory, Internal Model Validation, Kendall Tau, K-Means Algorithm, K-Nearest Neighbor algorithm, Kruskall-Wallis Test, Kurtosis, Lack of Fit Tests, Level of Significance (alpha), Matrix Algebra, Maximum Likelihood Method, Metric Spaces (Generalized Distances), Minimax, Multiple or Multivariate Regression, Multi-way tables, Normality Tests, Outliers, Overfitting, Parallel Coordinate Plots, p-level (statistical significance), PRESS Statistic, Quality Control, Residual Analysis, Robust Analysis, Scheffe’s test, Shapiro-Wilks test, Shewart Control Charts, Short run control charts, Signal to Noise ratio, Site Selection, Skewness, Standard error of a proportion, Standardized residuals, Statistical Graphics, Statistical Inference, Statistical Power, Statistical Significance (p-level), Stepwise regression, Studentized residuals, Univariate Time Series Analysis, Time Series Analysis - Seasonal Factors, Type I Error, Type II Error, Weighted Least Squares, Weighted Variance and Wilcoxin Test, analysis of variance, Bayesian inference, chi-square test, confidence interval theory, continuous distributions, exploratory data analysis (data mining), frequentist statistics, gravity model application, hypothesis testing, intermediate probability theory, lack of fit tests, matrix algebra, maximum likelihood method, multi-way tables, normality tests, residual analysis, robust analysis, site selection, statistical graphics, statistical inference, stepwise regression, univariate time series analysis, time series analysis – seasonal factors, Type I error, Type II error and weighted least squares, Assignable Causes and Actions, Avoiding Lying with Statistics and Maps, Chernoff Faces, Coded/Dummy Variables, Coefficient of Determination, Combinatorial Analysis, Computation of Expected, Confidence Interval for a datum, Confidence Intervals, Data Basics, Discrete Distributions, Elementary Probability Theory, Elementary Statistical Sampling, Gaussian/Normal Distribution, Geometric Mean, Graphical Display/Analysis, Harmonic Mean, Histograms, Interpolation, Introduction to trend cycle forecasting, Multivariate Star Plots, Lack of Fit in Regression Analysis, Method of Least Squares, Market Share Analysis, Odds Ratio, One-sample t-test, One-way tables, Original Data Plots, Over parameterized model, Paired t-test, Percentiles, Pie Chart Replacements, Quartiles, R – Pearson correlation coefficient, Randomness, Ranks, Raw Data Plots, Residuals, Run tests, Sign Test, Spearman R, Student’s t-test, Travel Distance or Travel Time, Trimmed Means, Types of Data, Uniform distribution, Univariate Regression Analysis and Weighted Mean, descriptive statistics (correlation, r-squared, chi-squared, geometric mean, harmonic mean, t-statistic, F-statistic, sample standard deviation and those included in the introductory concepts above), computation of expected, confidence intervals, data basics, discrete distributions, elementary probability theory, elementary statistical sampling, graphical display/analysis, introduction to trend-cycle forecasting, market share analysis, one sample t-test, one-way tables, paired t-test and sign test, univariate regression analysis, basic descriptive statistics (mean, median, mode, range, covariance, population standard deviation) and terms, such as: Euclidean Distance, Mahalanobis Distance, Missing Values versus Zeros, Product Popularity Computations, Quantiles and Rank, Analysis of Variance, Bayesian Inference, Chi-square Test, Cluster Analysis, Confidence Interval for the mean, Confidence Interval Theory, Confidence Level, Continuous Distributions, Contouring, Mallows Cp statistic, Data Smoothing, Exploratory Data Analysis (Data Mining), External Model Validation, Extrapolation, Frequentist Statistics, Gravity Model Application, Hypothesis Testing, Intermediate Probability Theory, Internal Model Validation, Kendall Tau, K-Means Algorithm, K-Nearest Neighbor algorithm, Kruskall-Wallis Test, Kurtosis, Lack of Fit Tests, etc.