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Ashwag
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Ashwag
posted 6 Years Ago
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Hi,
I have question regarding to residual vs error term, are they different or can be use it interchangeably
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Ashwag
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6 Years Ago
They are not the same. ●The residual is the observed value minus the fitted value; it depends on the sample values. ●The error term is the random variable ε in the true regression equation.
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VEE Exam Course: Corporate Finance
Updates
Registration
Benefits of online course
Course scheduling
Introduction
Materials
Homework assignments
Final exam
Questions
Module 1: Present value and the opportunity cost of capital
Module 2: Calculating present values
Module 3: Value of common stocks
Module 4: Net present value vs other valuation models
Module 5: Investment decisions and net present value
Module 6: Risk, return, and the opportunity cost of capital
Module 7: Risk and return
Module 8: Capital budgeting and risk
Module 9: Analyzing corporate projects
Module 10: Positive net present value
Module 11: Maximizing net present value
Module 12: Corporate financing and market efficiency
Module 13: Corporate financing
Module 14: The capital markets
Module 15: Common stock dividends
Module 16: Debt policy
Module 17: Optimal corporate borrowing
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FA Mod 4: The balance sheet (statement of financial position)
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MS Mod 12: Expected values and β’s for ANOVA + unequal sample sizes
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Updates
Registration
Benefits of online course
Course scheduling
Introduction
Materials
Homework assignments
Final exam
Questions
Module 1: Consumer's Demand, Producer's Supply, and Equilibrium
Module 2: Prices, costs, and the gains from trade
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Module 12: Price discrimination
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Module 17: Externalities: Pigou and Coase
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Textbook
Syllabus
Homework assignments
Course questions
Final exam
Macro practice exam questions
Macro Module 1: National Income Accounting
Macro Module 2: Economic Growth
Macro Module 3: Growth Models
Macro Module 4: Working with the Solow Growth Model
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VEE Exam Course: Regression Analysis John Fox text
Registration
Course scheduling
Introduction
Materials
Homework assignments
Questions
Final exam
Fox Module 1 Statistical models
Fox Module 2 Basics of regression analysis
Fox Module 3 Univariate displays
Fox Module 4 Bivariate displays
Fox Module 5 Multivariate displays
Fox Module 6 Transforming data
Fox Module 7 Advanced transformations
Fox Module 8 Linear least squares regression
Fox Module 9 Multiple regression
Fox Module 10 Advanced multiple regression
Fox Module 11 Statistical inference for simple linear regression
Fox Module 12 Statistical inference for multiple regression
Fox Module 13 Dummy variable regression
Fox Module 14 Modeling interactions
Fox module 15 advanced interactions
Fox Module 16 Analysis of variance
Fox Module 17 Unusual and influential data
Fox Module 18 Outliers and Influence advanced
Fox Module 19 Heteroscedasticity
Fox Module 20 Collinearity
Fox Module 21 Generalized linear models concepts
Fox Module 22 Generalized linear models discrete and continuous data
Fox Module 23 Generalized linear models, probabilities
Fox Module 24 Student project requirements and templates
VEE Regression Analysis Student Project
Student Projects: Read First (Basic Information)
Other Ideas: Parameter Stability and Heteroscedasticity
Project Template: Sports Won-Loss Statistics
Regression Analysis Student Project Samples
RA student project questions
VEE Exam Course: Time Series Cryer Chan text
Registration
Materials
Introduction
Homework assignments
Course scheduling
Final exam
Questions
TS Module 1 Time series overview
TS Module 2 Time series concepts
TS Module 3 Trends
TS Module 4 Regression methods
TS Module 5 Stationary moving average processes
TS Module 6 Stationary autoregressive processes
TS Module 7 Stationary mixed processes
TS Module 8 Non-stationary time series basics
TS Module 9 Non-stationary ARIMA time series
TS Module 10 Autocorrelation functions
TS Module 11 Simulated and actual time series
TS Module 12 Parameter estimation method of moments
TS Module 13 Parameter estimation least squares
TS Module 14 Model diagnostics
TS Module 15 Forecasting basics
TS Module 16 ARIMA Forecasting
TS Module 17 Forecasting bounds
TS Module 18 Forecast updates and weights
TS Module 19 Seasonal models basics
TS Module 20 Seasonal models advanced
TS Module 21 Building an ARIMA process
TS Module 22 Student projects interest rates
TS Module 23 Student projects sport statistics
TS Module 24 Student projects weather and demographics
VEE Time Series Student Project
Time Series Student Project Samples
Interest Rates
Time Series Project Templates
Web Sites and Data Questions
ARIMA Modeling
Weather: Daily Temperature Data
Time Series Techniques
Indices for TS Student Projects
Student Project General: Introduction, Write-up, Grading (Read First)
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