Samenvatting Statistics for Business and Economics

Samenvatting Statistics for Business and Economics

Samenvatting voor het vak Statistics for Business and Economics op de Rijksuniversiteit Groningen. Hoofdstuk 12, 13, 15, 16, & 17.

Last updated
18-10-2017

Multiple Regression (12)

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, multiple regression is used. The least squares principle fit the model.

Several Aspects of Regression Analysis (13)

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 goal when developing a model is to approximate the complex reality as close as possible with a relatively simple model, which can then be used to provide insight into reality. It is impossible to represent all of the influences in the real situation in a model, instead only the most influential variables are selected.

Building a statistical model has 4 stages:

Analysis of Variance (15)

 

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 ANOVA, but these are based on the assumption of either paired observations or independent random samples, and can only be used to compare two population means. ANOVA can be used to compare more than two populations, and also uses assessments of variation, which forms a large problem in other methods.

Predictions with Time-Series Data (16)

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. Thus, different forms of analysis are needed.

The main goal of analysing time-series data is to make predictions. An important assumption here is that the relations between variables remain constant.

16.1. Time-Series Components

Most time-series have the following four components:

Sampling (17)

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 the population belongs to only one strata. Stratified random sampling is the process of selecting independent simple random samples from each strata. A question that arises here Is how to allocate the sampling effort among the strata. There are various possibilities:

Bullets Statistics for Business and Economics

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.
  • Standard multiple regression assumptions include the four standard simple regression assumptions, plus a fifth one: It is not possible to find a set off nonzero numbers such that the sum of the coefficients equals zero.
  • Multiple regression models include an error term, ε, that represents variability caused by variables not included in the model.
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