Today, through media, we are faced with various statistical graphs and results, many of which use statistical tricks. In this workshop, we will learn the main concepts of statistics, data processing and converting them into the information and graphs needed to show the results, and we will learn how to avoid being deceived by numbers and statistics by knowing these materials. In other words, we learn to raise the level of our statistical analysis skills so not to be fooled easily.

Goal:
This workshop will be held for 20 hours in the form of 10 two-hour sessions, with the aim of familiarizing with preliminary and applied statistical methods for carrying out various projects. This course is designed in such a way that after successfully completing it, the participants will be able to analyze their data and evaluate statistical reports with the help of R software, in addition to gaining the ability to understand the statistical basics of the discussed topics. Certificate will be awarded after the completion of the course.

Requirements:
Basic Maths and Programming

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.

Introduction to Data
Data basics
Data collection principles
Sampling Strategies
Designing experiments
Examining numerical data
Analyzing categorical data
Descriptive statistics
Histogram, PDF, CDF
Measures of central tendency and dispersion moments
Probability and distributions
Defining probability
Random variables
Probability distributions: Uniform, Gaussian, t-student, Binomial
Foundations for inference 1
Variability in estimates, Centeral Limit Theorem
Confidence interval
Foundations for inference 2
Formal testing using p-value
Decision errors and statistical significance
Inference for numerical variables 1
Null hypothesis testing
Inference on a sample mean (One sample t-test)
Difference of two means (Two sample t-test)
Inference for numerical variables 2
Statistical power calculation
Comparing three or more means (ANOVA)
Inference for categorical variable 1
Inference for a single proportion
Comparing two proportions
Comparing three or more proportions
Inference for categorical variables 2
Testing for goodness of fit using chi-square
Testing for independence
Small sample hypothesis testing
Advanced plotting
Essential considerations in visualizing data
Multiplot