Advanced Applied Statistics
It is applied course in statistics that is designed to provide you with the concepts and methods of statistical analysis for decision making under uncertainties. This course is a combination of lectures and computerbased practice, joining theory firmly with practice. It introduces techniques for summarizing and presenting data, estimation, confidence intervals, hypothesis testing, modeling relationships and some multivariate techniques. The lectures focuses more on understanding of key concepts and statistical thinking, and less on formulas and calculations, which can now be done on statistical software.
Summary Statistics for Measurement Variables Exploratory Data Analysis Normality Tests and Transformations Frequency Tables and TwoWay CrossTabulations Multiple Tables and MultiWay CrossTabulations Tables of Means, Medians and Other Summary Statistics Histograms Box Plots Scatterplots and Overlays Line Plots and ConnectedLine Plots Other Twoway Plot Types Bar Charts and Pie Charts Symmetry and Quantile Plots
OneSample Tests
TwoSample Tests
OneWay Analysis of Variance (ANOVA)
Two and NWay Analysis of Variance
Factor Variables and Analysis of Covariance (ANCOVA)
Predicted Values and ErrorBar Charts
Rate and Ratios Stratified Rate and Ratios Contingency Table Analysis of 2 X 2 Tables Contingency Table Analysis of 2 X 2 X K Tables Contingency Table Analysis of R X C Tables Contingency Tables Analysis of ordinal variables Contingency Tables Analysis of Matched Pair samples
###Linear Regression Analysis Do File crime Data elemapi2 Data Do File
Simple Regression
Correlation
Multiple Regression
Hypothesis Tests
Dummy Variables
Interaction Effects
Robust Estimates of Variance
Predicted Values and Residuals
Diagnosing Multicollinearity and Heteroskedasticity
Confidence Bands in Simple Regression
Diagnostic Graphs
###Advanced Regression Methods Do File
Lowess Smoothing
Robust Regression
Further rreg and qreg Applications
Nonlinear Regression  1
Nonlinear Regression  2
Box�Cox Regression
Multiple Imputation of Missing Values
Structural Equation Modeling
Do File
###Logistic Regression
Using Logistic Regression
Marginal or Conditional Effects Plots
Diagnostic Statistics and Plots
Logistic Regression with OrderedCategory y
Multinomial Logistic Regression
Ordinal Logistic Regression
###Survival and EventCount Models
SurvivalTime Data
CountTime Data
Kaplan�Meier Survivor Functions
Cox Proportional Hazard Models
Exponential and Weibull Regression
Poisson Regression
###Principal Component, Factor and Cluster Analysis
Principal Component Analysis and Principal Component Factoring
Rotation
Factor Scores
Principal Factoring
MaximumLikelihood Factoring
Cluster Analysis � 1
Cluster Analysis � 2
Using Factor Scores in Regression
Measurement and Structural Equation Models
###Time Series Analysis
Smoothing Further Time Plot Examples Recent Climate Change Lags, Lead and Differences Correlograms ARIMA Models ARMAX Models
###Multilevel and MixedEffects Modeling
Regression with Random Intercepts Random Intercepts and Slopes Multiple Random Slopes Nested Levels Repeated Measurements CrossSectional Time Series MixedEffects Logit Regression
###Survey Data
Declare Survey Data Design Weights Poststratification Weights SurveyWeighted Tables and Graphs
Basic Concepts and Tools Matrix Programming
#Assignments
[Assignment 1]
[Assignment 3]
[Assignment 4]
Case Studies
Project
[Project]
Books

Sophia RabeHesketh & Brian Everitt (2004), Handbook of Statistical Analysis using Stata, Chapman & Hall/CRC Press LLC

Landau, Sabine. (2004) "A handbook of statistical analyses using SPSS", Chapman & Hall/CRC Press LLC

Yosef Cohen and Jeremiah Y. Cohen (2008), "Statistics and Data With R, An applied approach through examples" Wiley

About
Nadeem Shafique Butt, Associate Professor of Biostatistics at King Abdulaziz University Also Visiting/Adjunct faculty member at University of the Punjab,
Phone:
Fax:

AAS

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