Single equation linear models

single equation linear models

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Über die Autoren This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The authors discuss commonly applied econometric procedures, which are useful in building regression models for economic forecasting and supporting business decisions.

single equation linear models

A significant part of the book is devoted to traps and pitfalls in implementing regression analysis in real-world scenarios. The book consists of nine chapters, the final two of which are fully devoted to case studies. Today's business environment is characterised by a huge amount of economic data.

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Making successful business geheim flirten under such data-abundant conditions requires objective analytical tools, which can help to identify and quantify multiple relationships between dozens of economic variables.

Single-equation regression analysis, which is discussed in this book, is one such tool.

The MicrosoftMicrosoft Linear Regression algorithm is a variation of the MicrosoftMicrosoft Decision Trees algorithm that helps you calculate a linear relationship between a dependent and independent variable, and then use that relationship for prediction. The relationship takes the form of an equation for a line that best represents a series of data. Die Linie des folgenden Diagramms ist z.

The book offers a valuable guide and is relevant in single equation linear models areas of economic and business analysis, including marketing, financial and operational management.

Inhaltsverzeichnis Preface Types and applications of regression models.

single equation linear models

Basic elements of a single-equation linear regression model. Nature and dangers of univariate and multivariate outlying observations.


Tools for detection of outlying observations. Recommended procedure for detection of outlying and influential observations.

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Dealing with detected outlying and influential observations. Preliminary specification of the model. Detection of potential outliers in the dataset.

Selection of explanatory variables from the set of candidates. Interpretation of the obtained regression' structural para meters. Testing general statistical significance of the whole model: F test.

Communication and aims for the analyst. Getting a feel for the data. Massaging the data.

Testing the normality of regression residuals' distribution. Testing the autocorrelation of regression residuals. Testing the heteroscedasticity of regression residuals.

Kompetenzen Die Studierenden sollen in den beiden zu besuchenden Vorlesungen statistische Denkweisen verinnerlichen und mit realen Datenproblemen umgehen können. Im Modul lernen die Studierenden grundlegende Methoden der multivariate Datenanalyse kennen.

Testing single equation linear models symmetry of regression residuals. Testing the randomness of regression residuals. Testing the multicollinearity of explanatory variables. What to do if the model is not correct?

Summary of verification of our model Dealing with qualitative factors by means of dummy variables.

single equation linear models

Modeling seasonality by means of dummy variables. Using dummy variables for outlying observations. Dealing with structural changes in modeled relationships.

  • Testing Linear Restrictions of Parameters in Regression Analysis |
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  • Januar - gebunden - XI Beschreibung This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression.

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