Канал: Quantitative Methods for Economics and Management

Literature Review References

Literature Review References

Week12: Lecture22 (Remedial Measure for Heteroscedasticity: Part 2 (Using Eviews) )

Week12: Lecture22 (Remedial Measure for Heteroscedasticity: Part 2 (Using Eviews) )

Week1: Lecture 1 (Introduction)

Week1: Lecture 1 (Introduction)

16 Correlation Covariance in Joint Probability Distribution

16 Correlation Covariance in Joint Probability Distribution

15 Covariance and correlation of Joint Probability Distribution

15 Covariance and correlation of Joint Probability Distribution

14 Proof of Laws of Mean and variance in Joint Probability Distribution

14 Proof of Laws of Mean and variance in Joint Probability Distribution

13 How to Find Mean and Variance of Joint Probability Distribution

13 How to Find Mean and Variance of Joint Probability Distribution

12 Theory of Joint Probability Distribution

12 Theory of Joint Probability Distribution

11 How to find area under a normal curve using Z Table

11 How to find area under a normal curve using Z Table

10 Uniform and Normal Distributions

10 Uniform and Normal Distributions

09 Mean, Variance and Mode of Continuous probability distribution

09 Mean, Variance and Mode of Continuous probability distribution

08 Continuous Probability Distribution_Part-2

08 Continuous Probability Distribution_Part-2

07 Continuous Probability Distribution_Part 1

07 Continuous Probability Distribution_Part 1

Learning Statistical Analysis using SPSS: A Beginner's Approach (Second Session)

Learning Statistical Analysis using SPSS: A Beginner's Approach (Second Session)

06 Poisson Distribution and Multinomial Distribution

06 Poisson Distribution and Multinomial Distribution

Learning Statistical Analysis using SPSS: A Beginner's Approach (First Session)

Learning Statistical Analysis using SPSS: A Beginner's Approach (First Session)

05 Negative Binomial Distribution

05 Negative Binomial Distribution

04 A Hyper-geometric Distribution Vs Binomial Distribution

04 A Hyper-geometric Distribution Vs Binomial Distribution

03 Binomial Probability Distribution

03 Binomial Probability Distribution

Introduction to Fundamental of Econometrics

Introduction to Fundamental of Econometrics

Introduction of Channel

Introduction of Channel

How to Check HEC recognized Journals in 2 minutes

How to Check HEC recognized Journals in 2 minutes

02 Laws of Mean Variance for discrete random Variable

02 Laws of Mean Variance for discrete random Variable

01 Basics of Random Variables

01 Basics of Random Variables

Conditional Probability Multiplicative Rules Part A

Conditional Probability Multiplicative Rules Part A

The Form of Hypothesis Testing

The Form of Hypothesis Testing

Type I and Type II error and Hypothesis Testing

Type I and Type II error and Hypothesis Testing

Hypothesis testing an Introduction

Hypothesis testing an Introduction

Week16: Lecture 30 (Overview of the Econometric Models for Time Series Data)

Week16: Lecture 30 (Overview of the Econometric Models for Time Series Data)

Week16: Lecture 29 (Overview of the Econometric Models for Cross-Sectional Data)

Week16: Lecture 29 (Overview of the Econometric Models for Cross-Sectional Data)

Week15: Lecture 28 (Theory of Model Misspecificaiton Errors II)

Week15: Lecture 28 (Theory of Model Misspecificaiton Errors II)

Week15: Lecture 27 (Theory of Model Specification Errors I)

Week15: Lecture 27 (Theory of Model Specification Errors I)

Week14: Lecture 26 (Remedial Measures of Autocorrelation using Eviews)

Week14: Lecture 26 (Remedial Measures of Autocorrelation using Eviews)

Week14: Lecture 25 (Autocorrelation Remedial Measure: Theory)

Week14: Lecture 25 (Autocorrelation Remedial Measure: Theory)

Week13: Lecture 24 (Autocorrelation Testing Theory, Using Eviews to Check Autocorrelation)

Week13: Lecture 24 (Autocorrelation Testing Theory, Using Eviews to Check Autocorrelation)

Week13: Lecture 23 (Auto-correlation Theory, Causes and Consequences)

Week13: Lecture 23 (Auto-correlation Theory, Causes and Consequences)

How to Select Journal for Publishing Research Work ? (Free Journals)

How to Select Journal for Publishing Research Work ? (Free Journals)

Week12: Lecture21 (Remedial Measure for Heteroscedasticity: USING Eviews )

Week12: Lecture21 (Remedial Measure for Heteroscedasticity: USING Eviews )

Week12: Lecture21 (Remedial Measure for Heteroscedasticity: Theory )

Week12: Lecture21 (Remedial Measure for Heteroscedasticity: Theory )

Probability Theory-5 (Multiplicative Rule of Probability and Numerical Examples)

Probability Theory-5 (Multiplicative Rule of Probability and Numerical Examples)

Probability Theory-6 (Bayes Theory of Probability and Numerical Examples)

Probability Theory-6 (Bayes Theory of Probability and Numerical Examples)

Probability Theory-4 (Conditional Probability with numerical Examples )

Probability Theory-4 (Conditional Probability with numerical Examples )

Week11: Lecture20 (Consequences of Heterocedasticity, Test for Heterocedasticity)

Week11: Lecture20 (Consequences of Heterocedasticity, Test for Heterocedasticity)

Week11: Lecture19 (PCA Method, Theory of Heterocedasticity)

Week11: Lecture19 (PCA Method, Theory of Heterocedasticity)

Supply Chain Models using Lindo (Transshipment Models)

Supply Chain Models using Lindo (Transshipment Models)

Supply Chain Models (Assignment Models)

Supply Chain Models (Assignment Models)

Supply Chain Models using Lindo (Transportation Models)

Supply Chain Models using Lindo (Transportation Models)

Probability Theory-2 (Sample Space, &, OR, NOT Rules, Combining Probabilities Rules)

Probability Theory-2 (Sample Space, &, OR, NOT Rules, Combining Probabilities Rules)

Probability Theory-3 (Theory of Combination and Permutation, Numerical Examples)

Probability Theory-3 (Theory of Combination and Permutation, Numerical Examples)

Probability Theory-1 ( Introduction to Probability, And and OR rule of Probabilities)

Probability Theory-1 ( Introduction to Probability, And and OR rule of Probabilities)

Introduction to Minitab, Installation and Simple Linear Regression

Introduction to Minitab, Installation and Simple Linear Regression

Multiple Regression and Variance Inflation Factor (VIF) Using Minitab

Multiple Regression and Variance Inflation Factor (VIF) Using Minitab

Production Managements (Supply Chain Models through Lindo Software)

Production Managements (Supply Chain Models through Lindo Software)

Week10: Lecture18 (Multicollinearity detection, theory and Eviews Usage)

Week10: Lecture18 (Multicollinearity detection, theory and Eviews Usage)

Week10: Lecture 17 (Multicollinearity Theory and Consequences)

Week10: Lecture 17 (Multicollinearity Theory and Consequences)

Week8: Lecture 16 (Dummy Variables for Seasonal Variations)

Week8: Lecture 16 (Dummy Variables for Seasonal Variations)

Week8: Lecture 15(Regression Models with Dummy Variables-III)

Week8: Lecture 15(Regression Models with Dummy Variables-III)

Week7: Lecture 14 (Regression Models with Dummy Variables-II)

Week7: Lecture 14 (Regression Models with Dummy Variables-II)

Week7: Lecture 13: Regression Models with Dummy Variables-I

Week7: Lecture 13: Regression Models with Dummy Variables-I

Week6: Lecture 12 (Regression on Standardized Variables & Measure of goodness of Fit)

Week6: Lecture 12 (Regression on Standardized Variables & Measure of goodness of Fit)

Regression on Standardized Variables (Using Eviews)

Regression on Standardized Variables (Using Eviews)

Week6: Lecture 11 (Polynomial Regression Models)

Week6: Lecture 11 (Polynomial Regression Models)

Polynomial Regression Model

Polynomial Regression Model

Estimating Lin Log Model Using Eviews

Estimating Lin Log Model Using Eviews

Week5: Lecture 10 (Theory and Estimation of Lin-Log Model & Reciprocal Model )

Week5: Lecture 10 (Theory and Estimation of Lin-Log Model & Reciprocal Model )

Linear Trend Model

Linear Trend Model

Log-Lin Model

Log-Lin Model

Week5: Lecture 9 (Theory of Log-Lin & Linear Trend Models, estimation in Eviews & Output Discussion)

Week5: Lecture 9 (Theory of Log-Lin & Linear Trend Models, estimation in Eviews & Output Discussion)

Week4: Lecture 8 (Testing Linear Restrictions in Regression using Eviews)

Week4: Lecture 8 (Testing Linear Restrictions in Regression using Eviews)

Week4: Lecture7 (Estimating dobule-log function: Theory, Estimation in Eviews & Discussing Results)

Week4: Lecture7 (Estimating dobule-log function: Theory, Estimation in Eviews & Discussing Results)

Double-Log Models using Eviews (Log-Linear)

Double-Log Models using Eviews (Log-Linear)

Week3: Lecture 6 (Testing OLS Estimators, R-Square, Calculating LRM using Views and interpretation)

Week3: Lecture 6 (Testing OLS Estimators, R-Square, Calculating LRM using Views and interpretation)

Week3: Lecture 5 (OLS Estimators, OLS Assumptions, Gauss Markov theorem, S.E of the OLS Estimators)

Week3: Lecture 5 (OLS Estimators, OLS Assumptions, Gauss Markov theorem, S.E of the OLS Estimators)

Week2: Lecture 4 (Nature and sources of economic data, Estimating Linear Regression Model)

Week2: Lecture 4 (Nature and sources of economic data, Estimating Linear Regression Model)

Week2: Lecture 3 (Introduction to Econometrics , LRM, Measuring scales)

Week2: Lecture 3 (Introduction to Econometrics , LRM, Measuring scales)

Week1: Lecture 2 (Quick overview of Statistics Concepts)

Week1: Lecture 2 (Quick overview of Statistics Concepts)