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- 12. Multiple Regression
- 13. Additional Topics in Regression Analysis
- 15. Analysis of Variance
- 16. Time-Series Analysis and Forecasting
- 17. Additional Topics in Sampling
12. Multiple Regression 1. A study was conducted to assess the i...
12. Multiple Regression
- Regression objectives are either to predict the value of the dependent variable, or to estimate the marginal effect of each independent variable.
- A population multiple regression model is a model that includes multiple independent variables.
- Standar...
17. Sampling There are various ways of sampling a population, according to research and analysis goals. 17.1. Stratified Sampling Stratified sampling involves breaking the population into strata (a.k.a. subgroups) according to a specific identifiable characteristic in such a way that each member of ...
16. Predictions with Time-Series Data Time series data involves measurements that are ordered over time, in which the sequence of observations is important. Most procedures for data analysis cannot be used for this data, as these procedures are based on the assumption that the errors are independent...
15. Analysis of Variance There are situations and experiments that require processes to be compared at more than two levels. Data from such experiments can be analysed using analysis of variance or ANOVA. 15.1. Comparing Population Means There are other ways to compare population means than A...
13. Several Aspects of Regression Analysis This chapter focusses on topics that add to the understanding of regression analysis. This includes alternative specifications for these models, and what happens in the situations where basic regression assumptions are violated. 13.1. Developing models The ...
12. Multiple Regression Simple regression (see chapter 11) can predict a dependent variable as a function of a single independent variable. But often there are multiple variables at play. In order to determine the simultaneous effect of multiple independent variables on a dependent variable, multipl...