Understanding regression analysis /
Proceeding on the assumption that it is possible to develop a sufficient understanding of this technique without resorting to mathematical proofs and statistical theory, Understanding Regression Analysis explores Descriptive statistics using vector notation and the components of a simple regression...
| Κύριος συγγραφέας: | |
|---|---|
| Μορφή: | Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
New York :
Plenum Press,
�1997.
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| Θέματα: | |
| Διαθέσιμο Online: | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=34594 |
Πίνακας περιεχομένων:
- Origins and uses of regression analysis
- Basic matrix algebra: manipulating vectors
- Mean and variance of a variable
- Regression models and linear functions
- Errors of prediction and least-squares estimation
- Covariance and linear independence
- Separating explained and error variance
- Transforming variables to standard form
- Regression analysis with standardized variables
- Populations, samples, and sampling distributions
- Sampling distributions and test statistics
- Testing hypotheses using the t test
- t test for the simple regression coefficient
- More matrix algebra: manipulating matrices
- Multiple regression model
- Normal equations and partial regression coefficients
- Partial regression and residualized variables
- Coefficient of determination in multiple regression
- Standard errors of partial regression coefficents
- Incremental contributions of variables
- Testing simple hypotheses using the f test
- Testing for interaction in multiple regression
- Nonlinear relationships and variable transformations
- Regression analysis with dummy variables
- One-way analysis of variance using the regression model
- Two-way analysis of variance using the regression model
- Testing for interaction in analysis of variance
- Analysis of covariance using the regression model
- Interpreting interaction in analysis of covariance
- Structural equation models and path analysis
- Computing direct and total effects of variables
- Model specification in regression analysis
- Influential cases in regression analysis
- Problem of multicollinearity
- Assumptions of ordinary least-squares estimation
- Beyond ordinary regression analysis.
