Business Statistics and Analysis-3
课件来源于Rice University的Business Statistics and Analysis
四，Linear Regression for Business Statistics
Week 1 – Regression Analysis
1. Introducing Linear Regression- Building the Model
What is Linear Regression?
Linear regression attempts to fit a linear relation between a variable of interest and a set of variables that may be related to the variable of interest.
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Overview of Regression:
A regression with only one explanatory or X variable
A regression with two or more explanatory or X variables
- Modeling Developing a regression model
- Estimation Using software to estimate the model
- Inference Interpreting the estimated regression model
- Prediction Making predictions about the variable of interest
2. Errors, Residuals and R-square
Regression is a process that has errors
- Residuals and Errors
- R-square: A “goodness of fit” measure.
Why do we have errors in the regression model ?
- Omitted variables.
- Functional relationship between the Y and X variables.
- The theory of regression analysis is based on certain assumptions about these errors.
- We have differentiated between the β’s and the b’s.
- In common usage people tend to mix these notations.
- However, as long as you understand the conceptual difference, you should be ok.
Week 2 – Hypothesis Testing and Goodness of Fit
1. Hypothesis Testing in a Linear Regression
2. Lesson Hypothesis Testing in a Linear Regression- ‘p-values’
- The t-cutoff approach
- The p-value approach
- The confidence interval approach
3. ‘Goodness of Fit’ measures- R-square and Adjusted R-square
4. Categorical Variables in a Regression- Dummy Variables
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Week 3 – Dummy Variables, Multicollinearity
1. Dummy Variable Regression – Extension to Multiple Categories
We need one dummy variable less than the number of categories. Incorrect to have three dummies REGA, REGB and REGC.