Introduction to Statistical Reasoning & Data Analysis

COURSE DESCRIPTION

In our news-saturated era, brimming with information and unfiltered data, understanding what is relevant and what is not, and more importantly, discerning what has a scientific basis and is evidence-based, becomes one of the most important skills that any professional must acquire during their higher education. This course serves as an introduction to fundamental concepts and the logical foundation of statistical reasoning. It equips students with practical skills at an introductory level, enabling them to select, generate, and accurately interpret various descriptive and inferential methods. Furthermore, this course fosters an understanding of the wide-ranging applications of statistics and its significance. No prior statistical knowledge is required. This course aims to teach students how to develop the proper mental stepwise process to collect, inspect, characterize and examine data from a scientific point of view, based predominantly on statistics. You will learn to unpack and analyze information by presenting and discussing your own results in round tables and presentations.

Prerequisites: Basic math and Algebra

CONTENT
Unit I. Data basic concepts.
o Data Analysis and Statistics
o Variance.
o Standard deviations.
o Mean.
o Quartile.
o Variables.
o Random variables.
o Relationships between variables.
o Populations.
o Data acquisition techniques.

Unit II. Data interpretation techniques.
o Types of plots (histograms, bar plots, whiskers and boxes, pie chart, mosaic plots, 
mapped data, tree diagrams).
o Contingency tables.
o Row and column proportions.
o Venn diagrams.

Unit III. Probability.

o Mutually exclusive events.
o Probability distribution.
o Complements.
o Independence.
o Conditional probability.
o Marginal and joint probabilities.
o Linear combination of random variables.
o Variability.
o Continuous distributions

Unit IV. Types of distributions.
o Normal distributions.
o Geometric distribution.
o Binomial distribution.
o Negative binomial distribution.
o Poisson distribution.
Unit V. Interference.
o Categorical data.
o Numerical data.
o Chi-square.
o Two-way tables.
o Single and double proportions.
o Paired data.
o One-sample means with the t-distribution.
o One way ANOVA

Unit VI. Regressions.
o Linear regression.
o Multiple regression

LEARNING EVALUATION

Oral presentations to present the results of the case studies (3 in total) 30 %
Round tables (3 in total) 20 %
Written reports (3 in total) 30 %
Final assignment (1 in total) 20 %
Total 100%