Quantitative Methods in Economics & Business (II)

    Course Code: 
    8165
    Semester: 
    3rd
    Compulsory Courses
    Professor: 

    ZACHARIADIS EMMANOUIL

    Course Description

    • Descriptive Statistics
    • Sampling Distribution
    • Confidence Intervals
    • Hypothesis Testing for one Population
    • Hypothesis Testing for two Populations
    • Chi-Squared Tests
    • Simple Linear Regression
    • Multiple Linear Regression
    • Analysis of Variance
    • Time Series Analysis

    Learning Outcomes

    Upon completion of this course, students will be able:

    • Distinguish between the different types of data (numerical, ordinal, categorical)
    • Understanding of the various sampling strategies
    • Visual representation of numerical data (histograms, frequency polygons, bar charts)
    • Recognizing the shape of the distribution based on the visual representation of data
    • Determination of the measures of central tendency for numerical data
    • Understanding the connection between frequency and probability
    • Familiarity with the basic probability definitions and rules
    • Understanding the probability distribution of basic random variables
    • Understanding the concept of the sampling distribution and its relation to the population parameters
    • Definition of confidence intervals based on sample data
    • Grasping the t-student distribution
    • Determination of the sample size for defining confidence intervals of certain length
    • Understanding the idea of Hypothesis testing
    • Distinguish between the errors that may be done when performing a hypothesis test
    • Performing independence tests when frequency values are available for different levels of categorical values
    • Understanding of the simple linear regression model
    • Recognizing the meaning of the slope and intercept
    • Distinguish between explained and unexplained variance
    • Realizing the requirement for multiple independent variables
    • Ability to develop multiple linear regression models
    • Ability to develop multiple regression models using pseudo-variables for different levels of categorical variables
    • Understanding the concept of variance analysis
    • Comprehending that by analyzing variance, conclusions on the means can be reached
    • Distinguishing between the basic building blocks of time series
    • Ability to smooth time series data
    • Estimating future values of time series