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    Mahmoud
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    Introduction to Modern Econometrics with applications

    This course provides a comprehensive introduction to econometrics—the application of statistical methods to economic data. It equips students with the tools to model, estimate, interpret, and test economic relationships using real-world data. The course blends theory with hands-on empirical practice, emphasizing understanding the assumptions behind models, diagnosing issues like multicollinearity and heteroscedasticity, and making sound inferences.
    By the end of the course, students will be able to formulate econometric models, estimate parameters using regression techniques, interpret empirical results, and evaluate the reliability and limitations of statistical inferences in an economic context.

    Extra information

    Week 1: Introduction to Econometrics

    Week 2: Simple Linear Regression Model

    Week 3: Inference in the Simple Regression Model

    Week 4–5: Multiple Linear Regression Model

    Week 6: Violations of Classical Assumptions I – Heteroscedasticity

    Week 7: Violations II – Autocorrelation

    Week 8: Model Specification and Selection

    Week 9: Dummy Variables and Qualitative Information

    Week 10: Introduction to Time Series Econometrics

    Week 11: Simultaneous Equation Models (Optional for Intro Level)

    Location

    Online via webcam

    General info

    Age:
    Adults (18-64 years old)
    Student level:
    Beginner
    Intermediate
    Duration:
    60 minutes
    90 minutes
    The class is taught in:English, Arabic

    About Me

    PHD in Statistics , Cairo university, Egypt.
    More than 10 years of experience in teaching statistics and relevant subjects, such as Statistics and Probability, Data Analytics, Mathematics, Calculus, Regression Analysis, Linear Algebra, Non parametric Statistics, operation research, and Econometrics
    Teaching and analyzing data using Excel, SPSS, Stata, Minitab and R-programming

    Education

    PHD in Statistics, Cairo university
    Master degree in Statistics, Cairo University, Egypt
    Bachelor degree in Statistics, minor computer science Cairo University, Egypt

    Experience / Qualifications

    More than 10 years of experience in teaching statistics and relevant subjects, such as Statistics and Probability, Data Analytics, Mathematics, Calculus, Regression Analysis, Linear Algebra, Non parametric Statistics, operation research, and Econometrics
    Teaching and analyzing data using Excel, SPSS, Stata, Minitab and R-programming.
    module 1: Introduction to Data Analysis Understanding Data Analysis: Purpose and Scope Overview of Excel as a Data Analysis Tool module 2:Introduction to Excel Interface and Tools Data Entry and Formatting Basic Formulas and Functions SUM, AVERAGE, COUNT, etc. Introduction to Cell Referencing (Relative, Absolute, Mixed) Module 3:Data Cleaning and Preparation Importing Data from Various Sources (CSV, Text, Databases) Data Cleaning Techniques Removing Duplicates Handling Missing Data Data Validation Working with Text Functions LEFT, RIGHT, MID, TRIM, CONCATENATE Module 4: Data Analysis Tools in Excel Sorting and Filtering Data Conditional Formatting Using PivotTables and Pivot Charts Subtotals and Summarizing Data Data Tables and Scenario Analysis Practical Exercise: Analyzing Sales Data Module 5: Advanced Functions and Formulas Logical Functions IF, AND, OR, NOT Lookup Functions VLOOKUP, HLOOKUP, INDEX, MATCH, XLOOKUP Mathematical and Statistical Functions ROUND, RANK, STDEV, MEDIAN Array Formulas Practical Exercise: Creating a Dynamic Dashboard Module 6: Data Visualization Principles of Data Visualization Creating Charts and Graphs Line, Bar, Pie, Scatter, Combo Charts Formatting and Customizing Charts Using Sparklines for Quick Insights
    Course Description Running the Program Browsing the SPSS menus creating new file opening existing file importing data file variable view creating new variable Types of variables entering data descriptive statistics frequency tables graphical presentation confidence intervals for one population parameter and two population parameter testing hypothesis for one population parameter and two population parameter Analysis of Variance ANOVA Regression Analysis
    Descriptive statistics Types of variables Frequency tables Center dimensions Diffusion measures Torsion scales Display data in one variable The data display is variable Confidence intervals for one and two population parameters Test hypothesis for one population parameter and two population parameters Analysis of variance (ANOVA) Correlation analysis Regression analysis

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    from C$36.35Online via webcam

    Good-fit Instructor Guarantee


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    Online reputation

    • Instructor since June 2023
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    Availability of a typical week

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    from C$36.35Online via webcam

    Good-fit Instructor Guarantee


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