Aim and objectives: 

Statistics is the mathematical analysis and the interpretation of the data, and drawing of interferences about a set of data when only a  part (subset) of the data is observed.

Statistics for business aims at introducing the students to some core concepts such as random experiments, random events, probability of the random event, random variables, distributions of the random variables, probability models, and statistical inference.

After the course, the students will be able to analyze and solve problems of statistical nature in various fields of business: management, marketing, finance, and also in psychology, political sciences, economics, etc. We underline that, especially for Finance students, a solid understanding of Statistics for Business is a strong formal and essential prerequisite.  

Prerequisites: 

Students are expected to be familiar with College Algebra. Even though Differential and Integral Calculus is an important tool in Statistics for Business, it will be revised as simply as possible. During the course, approximately 4 hours ill be devoted to the derivative and integral in their simplest forms (see the Syllabus section below). 

Teaching Methods: 

There are 4 hours per week in the form of lectures (2+2). The teaching experience at NYU shows that Statistics for Business should not be considered as an easy course. Therefore, students are expected to devote about four hours of their time, for every 2 hours of lecture, to study theory, exercises and problems. 

Assesment Criteria: 

Active participation                                               10%

Midterm exams                                           2 x 30 % = 60%

Final Exam                                                           30% 

Main Textbook: 

G. Attwood, G. Dyer, and G. Skipworth. Statistics 1. Heinemann  Modular Mathematics, 2000.

G. Attwood, G. Dyer, and G. Skipworth. Statistics 2. Heinemann  Modular Mathematics, 2000. 

SYLLABUS: 

• What is Statistics?
• Descriptive Statistics 1

Tabular and graphical approaches. Relative frequency distribution, Bar graph and Pie chart, Histogram, Frequency polygon. 

• Descriptive Statistics 2

Numerical Methods. Mean, range, variance, standard deviation, coefficient of variation, median, mode, percentiles.

• Elements of Probability

Random experiment, sample space, sample points, random events, algebra of random events. Basic rules for the probabilities of random events. Venn diagrams. Conditional probability and Multiplication rule. Tree diagrams. Independent and mutually exclusive random events. Permutations. Combinations.

• Discrete Random Variables

The law of distribution, expectation, variance. The uniform distribution. The binomial distribution and Poisson distribution.

• Basic Mathematical techniques

Differentiation, rules of differentiation, differentiation of polynomials, exponential and trigonometric functions. Integration by parts. Definite Integral: Newton-Leibniz Formula.

• Continuous Random Variables

Probability distribution function, expected value, variance, standard deviation, median, mode. The uniform distribution. The normal distribution.

• Statistical Inference

Elementary estimation theory. Hypothesis testing for the mean and variance, testing for parameter “p” of the Binomial Model and the mean of the Poisson Model. 

Grading Scale and Quality Points: