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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.
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)
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 EconometricsWeek 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 universityMaster 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 EconometricsTeaching 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
100% Satisfaction Guarantee
Good-fit Instructor Guarantee
If you are not satisfied after your first lesson, Apprentus will find you another instructor or will refund your first lesson.
Online reputation
- Instructor since June 2023
- Phone number verified
- Google connected
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Availability of a typical week
(GMT -04:00) New York
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from C$36.35Online via webcam
Good-fit Instructor Guarantee
If you are not satisfied after your first lesson, Apprentus will find you another instructor or will refund your first lesson.
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