1/8
Trusted teacher
This teacher has a fast response time and rate, demonstrating a high quality of service to their students.
Since July 2026
Instructor since July 2026
1 repeat student
Trusted choice for 1 returning student
I explain and simplify macroeconomics and microeconomics (micro/macro) materials for undergraduate and graduate students in Arab and foreign universities.
I will help you understand and overcome the difficulties of university economics subjects in a smooth and direct manner that ensures your academic achievement and excellence.
The service includes:
Explanation of topics in microeconomics and macroeconomics.
Solving and analyzing mathematical applications, problems, and exam exercises.
Clarifying and simplifying economic curves and data.
The curriculum is explained in either Arabic or English, depending on the university requirements.
Service features:
Interactive sessions with students via Zoom.
Summaries and worksheets to review key concepts before tests.
Support, interaction, and a space to answer any questions during the study period.
I will help you understand and overcome the difficulties of university economics subjects in a smooth and direct manner that ensures your academic achievement and excellence.
The service includes:
Explanation of topics in microeconomics and macroeconomics.
Solving and analyzing mathematical applications, problems, and exam exercises.
Clarifying and simplifying economic curves and data.
The curriculum is explained in either Arabic or English, depending on the university requirements.
Service features:
Interactive sessions with students via Zoom.
Summaries and worksheets to review key concepts before tests.
Support, interaction, and a space to answer any questions during the study period.
Our services include:
Descriptive and Inferential Statistics.
Probabilities, statistical distributions, and hypothesis testing (t-test, ANOVA, Chi-Square).
Training in statistical analysis using popular software such as SPSS, Excel, or R.
Review and test preparation:
Intensive reviews before midterm and end-of-term exams.
Solve past exam papers and focus on the most frequently asked questions.
Providing concise summaries and rules for each chapter.
Descriptive and Inferential Statistics.
Probabilities, statistical distributions, and hypothesis testing (t-test, ANOVA, Chi-Square).
Training in statistical analysis using popular software such as SPSS, Excel, or R.
Review and test preparation:
Intensive reviews before midterm and end-of-term exams.
Solve past exam papers and focus on the most frequently asked questions.
Providing concise summaries and rules for each chapter.
Linear Algebra: Vector Spaces, Eigenvalues & Eigenvectors, and Linear Applications.
Differential Equations: Ordinary Equations (ODEs), Partial Equations (PDEs), and Laplace and Fourier Transforms.
Real and Complex Analysis: Measurement Theory, Continuity, and the Study of Complex Functions.
Differential Equations: Ordinary Equations (ODEs), Partial Equations (PDEs), and Laplace and Fourier Transforms.
Real and Complex Analysis: Measurement Theory, Continuity, and the Study of Complex Functions.
List of lessons
1. Vector Spaces & Subspaces
Key concepts: Understanding the definition of vector space and the ten conditions (Vector Space Axioms) to prove that a set with addition and multiplication operations in its standard form a vector space.
Linear Independence & Dependence: Testing whether vectors are linearly independent or dependent on each other.
Span, Basis & Dimension:
Determine the generative span of the space.
Finding the basis and dimension of different spaces (such as vector spaces \mathbb{R}^n, matrices M_{m \times n}, and polynomials P_n).
Subspaces: Subspace conditions, finding the null space, column space, and row space of matrices.
2. Linear Transformations and Applications
Definition and properties: Proving the linearity of applications and determining the Standard Matrix Representation.
Kernel and Range:
Calculate the kernel (Kernel/Null Space) and image (Image/Range) for conversion.
Applying the Rank-Nullity Theorem and calculating rank and nihilism.
Change of Basis: Transition Matrices and how to represent the same linear transformation under different bases and families.
3. Eigenvalues & Eigenvectors
Characteristic Equation: Finding the eigenvalues of \lambda by solving the equation \det(A - \lambda I) = 0.
Eigenvectors and Eigenspaces: Finding the eigenspace associated with each eigenvalue by solving the homogeneous system (A - \lambda I)\vec{v} = \mathbf{0}.
Diagonalization:
Diagonalizability Condition Test.
Formulating the decomposition of diagonal A = PDP^{-1} to facilitate the calculation of matrix powers A^k and the solving of systems of differential equations.
Orthogonal Diagonalization: For symmetric matrices and the application of Spectral Theorem.
1. Vector Spaces & Subspaces
Key concepts: Understanding the definition of vector space and the ten conditions (Vector Space Axioms) to prove that a set with addition and multiplication operations in its standard form a vector space.
Linear Independence & Dependence: Testing whether vectors are linearly independent or dependent on each other.
Span, Basis & Dimension:
Determine the generative span of the space.
Finding the basis and dimension of different spaces (such as vector spaces \mathbb{R}^n, matrices M_{m \times n}, and polynomials P_n).
Subspaces: Subspace conditions, finding the null space, column space, and row space of matrices.
2. Linear Transformations and Applications
Definition and properties: Proving the linearity of applications and determining the Standard Matrix Representation.
Kernel and Range:
Calculate the kernel (Kernel/Null Space) and image (Image/Range) for conversion.
Applying the Rank-Nullity Theorem and calculating rank and nihilism.
Change of Basis: Transition Matrices and how to represent the same linear transformation under different bases and families.
3. Eigenvalues & Eigenvectors
Characteristic Equation: Finding the eigenvalues of \lambda by solving the equation \det(A - \lambda I) = 0.
Eigenvectors and Eigenspaces: Finding the eigenspace associated with each eigenvalue by solving the homogeneous system (A - \lambda I)\vec{v} = \mathbf{0}.
Diagonalization:
Diagonalizability Condition Test.
Formulating the decomposition of diagonal A = PDP^{-1} to facilitate the calculation of matrix powers A^k and the solving of systems of differential equations.
Orthogonal Diagonalization: For symmetric matrices and the application of Spectral Theorem.
The lecture includes
- Explanation of all econometric topics from the basic to the advanced level.
- Simplifying concepts and theories in a clear and easy-to-understand style.
- Explanation of econometric models from basic to advanced, including simple and multiple linear regression, ECM, VECM, ARDL, VAR, and time series models, with interpretation of statistical results and their practical application.
- Training on solving assignments and tests with a detailed explanation of the solution steps.
- Assistance in using statistical analysis software such as EViews, Stata, and SPSS.
- Explaining how to interpret the outputs of statistical programs and write the results in an academic manner.
- Providing practical examples and applications to reinforce understanding.
- Answering all student inquiries during the session.
Suitable for
Undergraduate students.
Graduate students.
Researchers who wish to apply econometrics in their research.
- Explanation of all econometric topics from the basic to the advanced level.
- Simplifying concepts and theories in a clear and easy-to-understand style.
- Explanation of econometric models from basic to advanced, including simple and multiple linear regression, ECM, VECM, ARDL, VAR, and time series models, with interpretation of statistical results and their practical application.
- Training on solving assignments and tests with a detailed explanation of the solution steps.
- Assistance in using statistical analysis software such as EViews, Stata, and SPSS.
- Explaining how to interpret the outputs of statistical programs and write the results in an academic manner.
- Providing practical examples and applications to reinforce understanding.
- Answering all student inquiries during the session.
Suitable for
Undergraduate students.
Graduate students.
Researchers who wish to apply econometrics in their research.
1st lesson is backed
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by our Good-fit Instructor Guarantee