Quantitative Methods in Economics (ECON 2040) is a course that provides an introduction to statistical methods relevant to economics, including descriptive statistics, probability and probability distributions, hypothesis testing, and ordinary least squares regression. The course aims to equip students with the necessary statistical tools and techniques to analyze economic data and make informed decisions.
Main topics to be covered:
 Describing data: Delving into descriptive statistics, this topic focuses on methods to summarize and describe data in a meaningful manner. It encompasses measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and graphical representations to facilitate understanding of data distribution and characteristics.
 Probability distributions: This topic explores statistical functions that outline all possible values and likelihoods a random variable can assume within a given range. Probability distributions can be either discrete or continuous and are employed to model uncertainty and randomness in various situations.
 Binomial Distributions: The binomial distribution, a discrete probability distribution, models the probability of obtaining one of two outcomes (success or failure) in a fixed number of trials, each having the same probability of success. It is frequently used in scenarios with only two possible outcomes, such as coin tosses or pass/fail tests.
 Poisson Distributions: Focusing on the Poisson distribution, this topic examines a discrete probability distribution that models the probability of a given number of events occurring within a fixed interval of time or space. It is characterized by a single parameter, λ (lambda), which represents the mean number of events.
 Sampling Distribution: This topic addresses the sampling distribution, which is the probability distribution of a given statistic based on a random sample from a population. It aids in understanding the variability and uncertainty associated with sample estimates and is utilized in hypothesis testing and confidence interval estimation.
 Normal Distributions: The normal distribution, also known as the Gaussian distribution, is a continuous probability distribution characterized by its bellshaped curve. It is defined by two parameters: the mean (µ) and the standard deviation (σ). The normal distribution is extensively used in statistics due to its desirable properties, such as the central limit theorem, which states that the sum of a large number of independent random variables will tend to be normally distributed.
 Hypothesis testing: This topic delves into hypothesis testing, a statistical method employed to make inferences about populations based on sample data. It involves formulating null and alternative hypotheses, calculating test statistics, and interpreting results to determine whether there is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis.
 Regression Model: The regression model, a statistical method for estimating relationships between variables, is the focus of this topic. In the context of Ordinary Least Squares (OLS) regression, it is widely used in econometrics to model the relationship between a dependent variable and one or more independent variables. The topic covers how to interpret regression results and assess the validity of the models.
