The expansion of human information has created new tools for collecting, describing, analyzing, transferring and presenting information by researchers in different fields. Therefore, it is necessary for all those who deal with data to be aware of advanced statistical methods. Managers, researchers, programmers, and those who need to evaluate statistical data, with the help of these advanced methods, will be able to analyze and evaluate more accurately the results of their projects.

Goal:
This workshop will be held for 20 hours in the form of 10 two-hour sessions, with the aim of familiarizing with the advanced statistical methods needed to carry out various projects. This course is designed in such a way that after successfully completing it, the participants, in addition to gaining the ability to understand the statistical basics of the discussed topics, will be able to analyze their data more accurately with the help of R software. Although this course is designed independently of the introductory course, participation in the introductory course is recommended to better understand the concepts designed in the advanced course. A certificate will be awarded after the completion of the course,.

Requirements:
Basic Maths and Programming, Introductory Statistics

About the Instructor:
Dr. Atieh Sarabi-Jamab, obtained a bachelor's degree in electrical-electronic engineering. She continued electrical-control engineering for her master degree due to her interest in modeling, and she focused on the analytical and reasoning aspect of modeling complex systems during PhD. She completed her thesis in the field of "Analytical and theoretical decision-making under conditions of uncertainty" which was jointly conducted by the Faculty of Engineering, University of Tehran and the Department of Mathematics and Statistics, University of Munich, Germany. After her doctorate, she joined the cognitive science research institute for the postdoctorate and started her activity in the field of behavioral and neurocognitive modeling of decision-making in individual and social conditions. After several years of experience in this field as well as cooperation with various centers in the field of statistics, she is currently completing her experience in the field of behavioral economics at the Institute for Research in Fundamental Sciences (IPM).

Time:
For more information about classes please contact us.

Foundations for inference
Hypothesis testing
Statistical significance
Formal testing using p-values
Decision errors
Confidence intervals
Introduction to Linear Regression
Relationship between two numerical variables
Linear regression with a single predictor
Outliers in linear regression
Inference for linear regression
Multiple Linear Regression
Regression with multiple predictors
Inference for multiple linear regression
Model selection
Model diagnostics
Advanced inferential statistics 1
Chi-square
Wilcoxon
Permutation
Advanced inferential statistics 2
Mann-Whitney
Shapiro-Wilk
Kolmogorov-Smirnov
Signal detection theory
Likelihood-ratio test
ROC/AUC
Correlation analysis
Covariance
Autocorrelation
Cross correlation
Partial correlation
Spearman, Kendall
Advanced statistical techniques
PCA
ICA
Mutual Information
Clustering algorithms
K-means
Hierarchical
Classification algorithms
LDA
SVM
ANN
Decision tree