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STAT311 Regression Analysis课程简介
This graduate level course offers an introduction into regression analysis. A
Credits 3 researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variable. The process of finding this mathematical model that best fits the data involves regression analysis.
STAT 501 is an applied linear regression course that emphasizes data analysis and interpretation. Generally, statistical regression is collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables).
This graduate level course covers the following topics:
- Understanding the context for simple linear regression.
- How to evaluate simple linear regression models
- How a simple linear regression model is used to estimate and predict likely values
- Understanding the assumptions that need to be met for a simple linear regression model to be valid
- How multiple predictors can be included into a regression model
- Understanding the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid
- How a multiple linear regression model is used to estimate and predict likely values
- Understanding how categorical predictors can be included into a regression model
- How to transform data in order to deal with problems identified in the regression model
- Strategies for building regression models
- Distinguishing between outliers and influential data points and how to deal with these
- Handling problems typically encountered in regression contexts
- Alternative methods for estimating a regression line besides using ordinary least squares
- Understanding regression models in time dependent contexts
- Understanding regression models in non-linear contexts
STAT311 Regression Analysis HELP（EXAM HELP， ONLINE TUTOR）
Use the cig_1st_diff data set. This is based on the changes from 1990 to 2000 , and it is extracted from the data set used in Question #3. Estimate a first-difference model, as follows: regress cigch on taxch, uratech, and beertaxch. Weight the model by pop 2000 , and use robust standard errors. Interpret the estimate on taxch.
From the example in Section 8.5 from Card and Krueger (1994) on estimating the effects of minimum-wage increases on employment, write out the regression equation for the difference-in-difference model.
Use the data set oecd_gas_demand. From Question #7 in Chapter 3, add fixed effects for the country, along with a heteroskedasticity correction.
a. How does the coefficient estimate on lrpmg change from Question #7 in Chapter 3 with the fixed effects added?
b. How does this change which observations are compared to which observations?
Return to the tv-bmi-ecls data set used for Exercise $# 5$ in Chapter 6 . From that exercise, along with other descriptions of the research issue, there is much potential omitted-factors bias.
a. Explore the data description and variable list (from the file “Exercises data set descriptions” on the book’s website). Design a model to address the omitted-factors bias.
b. Are there any shortcomings to your approach?
• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.
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