Applied Statistics in R 

Black and white handouts of the lectures are avaliable in this zip file.

AppliedstatisticsinR.zip

You will need these zipped data files for some of the lectures.

AppliedstatisticsinRData.zip

 1.    Descriptive Statistics     

Strip Chart, Box Plot, Histogram, Dotplot, Sample mean, median, variance and standard deviation (sd), semi-interquartile range, scatter plot, covariance, correlation   

  2. Regression for Quantitative Response   

a .  Least Squares Multiple Linear Regression    

b.    Robust  Regression, Weighted Regression, Ridge Regression 

Robust Regression, Weighted Regression, Ridge Regression A    

  3.   Regression for   Categorical Response   

        a  Logistic Regression    

Compute the confusion matrix for logistic regression   example from last lecture, Spliting data sets into training and test sets, building logistic models.   

        b.   Poisson Regression    

When to use Poisson regression, how to estimate parameters, fitting regression models in R, testing goodness of fit, adjusting for heterogeneity.     

         c.  Multinomial Logistic Regression     

          Qualitative DV is not binary but takes K nominal values.    

d.   Ordinal Logisitic Regression       

        Qualitative DV is not binary but takes K ordinal values. 

4. Classification and Regression Trees (CART)

a. CART - 1

set up in R, impurity measures, parametric models   

b.  CART - 2

pruning a tree, prediction using tree, classification trees 

5. Market Basket Analysis

Association discovery from customer transactions data, sequence discovery

6. Random Forests

Variable importance measure in Random Forestes, computing in R

7. Generalized Linear Models (Not General Linear Models)

8. Structural Equation Model

Theory, Path Diagrams, Covariance Matrix Algebra, Two Stage Least Squares

9. Arima Modeling

Tutorial through example in R

10. Cluster Analysis (under construction)  

11. Discriminant Analysis    (under construction)