Introduction to Statistics


The main aims of this unit are: To introduce the principles of descriptive and inferential statistics in particular regression techniques and analysis of variance (ANOVA) that will be expanded in subsequent graduate statistical classes/courses. To enhance the basic data analysis skills and most importantly aid your presentations, interpretations and conclusions from all basic statistical methods up to regression and ANOVA. To develop a better understanding of Visual Exploratory steps and Graphical Data Display so that you become very comfortable and proficient with using commonly used statistical packages to compute necessary statistics and graph important statistical summaries of your data.        


 An undergraduate course in Statistics is helpful, (and 95% of the students in the past with BS degree equivalent have "a STATS undergraduate course" prior to arriving at MAICh, but there is no prior knowledge requirement that is expected of the students. Also there are no expectations that the students have prior knowledge with data analysis or experience with any statistical software.

Learning Outcomes

The students are would be shown step by step how data is collected and learn the various kinds of displays of (all kinds of) data. exposed to visualizing key characteristics of the distribution of variables and explore their associations expected to be able to calculate basic statistics and display the distribution of a variable. expected to understand randomness and probability, their roles and rules and their necessity in statistics. expected to understand and visualize via simulation the idea of the sampling distribution of a statistic. expected to understand how to test if two variables are  associated. expected to understand the basis of statistical inferences (confidence intervals and hypothesis testing) for a single population parameter(s) (mean or proportion). expected to understand the inference in Simple Linear Regression. expected to expand the comparisons between two population means or proportions and how to extend to several population means via the ANOVA model.  


A. PART I :  Introduction to Statistics Statistics and Data.   Variables distributions and graphs. Data description and visualizations.The Normal distribution.   B. PART II:  Randomness and Probability Gathering data (Surveys, Experiments and Observational Data)  Probability and counting rules. Discrete probability distributions. Random variables   C. PART III:  Inferential Statistics (Part I) Sampling distribution AND CLT Confidence Interval for one population parameter (mean or proportion) Hypothesis testing for one population parameter   D. PART IV: Inferential Statistics (Part II) – Confidence intervals and Hypothesis testing for testing the difference between two means, two proportions and two variances.   Inference in regression  E. PART V: ANOVA Analysis of variance Non-parametric tests.  Other Chi-square tests.        

Content Delivery

The delivery of the unit is based on: Lectures (3 hours in class daily for 5 days). During these lectures, the students become familiar with the theory of Statistics. Also, the students are able to see how we face real life situations and scenarios, as well problems in their fields of study (relevant to Statistics). They also learn how to use computer software programs (e.g.  JMP and others) in order to solve statistical problems. HANDS ON ONE HOUR EVENING SESSIONS "Problem solving sessions" that will end with a quiz during the last part of each day. There are "daily quiz assignments", with with short answer questions (some multiple choice) based on comprehending the lectures, (often these will involve real life scenarios). 

Coursework And Assignment Details

The final grade for the course will be based on 60% Final Exam and 40% from the daily homework (quiz): Assignments/ quiz questions which will be given to the students at the end of each day (starting the second day).  Each assignment/quiz consists (mainly) of a number of five questions based on comprehending the previous day material. The problems/questions will be similar to old problem questions. The students have to try to solve these problems individually.  Questions about the problems will be answered during the problem-solving sessions. These assignments are given to the students in order to: Familiarise them with the material covered during the lectures. Be able to see and overcome the difficulties when they try to solve problems in Statistics. decide and apply the methods suitable for each one of the problems (situations). Explain to the rest of the students the techniques used (in each one of the problems) and the reasons for choosing these techniques. The written quizzes count 40% towards the final grade. A written final examination. The material for this examination will be covered during the lectures as well as the questions/problems assigned and worked and tested daily (an old exam is provided for practice). This is a three hour, in class, exam.  A review for the final exam will be given during the quizzes sessions. The final examination counts 60% towards the final grade.