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Produktbild: Applied Regression Analysis for Business

Applied Regression Analysis for Business Tools, Traps and Applications

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

06.06.2019

Abbildungen

XI, 58 illus. in color., farbige Illustrationen

Verlag

Springer

Seitenzahl

286

Maße (L/B/H)

23,5/15,5/1,7 cm

Gewicht

458 g

Auflage

Softcover reprint of the original 1st edition 2018

Sprache

Englisch

ISBN

978-3-319-89041-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

06.06.2019

Abbildungen

XI, 58 illus. in color., farbige Illustrationen

Verlag

Springer

Seitenzahl

286

Maße (L/B/H)

23,5/15,5/1,7 cm

Gewicht

458 g

Auflage

Softcover reprint of the original 1st edition 2018

Sprache

Englisch

ISBN

978-3-319-89041-8

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Applied Regression Analysis for Business
  • Preface ........................................................................................................................... 1

    Chapter 1 – Basics of regression models .................................................................. 21.1. Types and applications of regression models. .............................................................................. 21.2. Basic elements of a single-equation linear regression model. ..................................................... 4Chapter 2 – Relevance of outlying and influential observations for regression analysis ..................................................................................................... 72.1. Nature and dangers of univariate and multivariate outlying observations. ................................ 72.2. Tools for detection of outlying observations. ............................................................................. 192.3. Recommended procedure for detection of outlying and influential observations. .................... 322.4. Dealing with detected outlying and influential observations. .................................................... 33Chapter 3 – Basic procedure for multiple regression model building ............. 353.1. Introduction. ............................................................................................................................... 353.2. Preliminary specification of the model. ...................................................................................... 353.3. Detection of potential outliers in the dataset. ........................................................................... 403.4. Selection of explanatory variables (from the set of candidates). ............................................... 483.5. Interpretation of the obtained regression’ structural parameters. ............................................ 57Chapter 4 – Verification of multiple regression model ...................................... 604.1. Introduction. ............................................................................................................................... 604.2. Testing general statistical significance of the whole model: F test. ........................................... 614.3. Testing the normality of regression residuals’ distribution. ....................................................... 634.4. Testing the autocorrelation of regression residuals. .................................................................. 724.5. Testing the heteroscedasticity of regression residuals. .............................................................. 874.6. Testing the symmetry of regression residuals. ........................................................................... 974.7. Testing the randomness of regression residuals. ..................................................................... 1064.8. Testing the specification of the model: Ramsey’s RESET test. ................................................. 1154.9. Testing the multicollinearity of explanatory variables. ............................................................ 1214.10. What to do if the model is not correct? .................................................................................. 1254.11. Summary of verification of our model .................................................................................... 130Chapter 5 – Common adjustments to multiple regressions .............................. 1325.1. Dealing with qualitative factors by means of dummy variables. ............................................. 1325.2. Modeling seasonality by means of dummy variables. ............................................................. 1365.3. Using dummy variables for outlying observations. .................................................................. 1482815.4. Dealing with structural changes in modeled relationships. ..................................................... 1555.5. Dealing with in-sample non-linearities. .................................................................................... 164Chapter 6 – Common pitfalls in regression analysis .......................................... 1716.1. Introduction. ............................................................................................................................. 1716.2. Distorting impact of multicollinearity on regression parameters. ........................................... 1716.3. Analyzing incomplete regressions. ........................................................................................... 1766.4. Spurious regressions and long-term trends. ............................................................................. 1806.5. Extrapolating in-sample relationships too far into out-of-sample ranges. .............................. 1866.6. Estimating regressions on too narrow ranges of data. ............................................................ 1936.7. Ignoring structural changes within modeled relationships and within individual variables. ... 197Chapter 7 – Regression analysis of discrete dependent variable .................... 2097.1. The nature and examples of discrete dependent variables. ..................................................... 2097.2. The discriminant analysis. ........................................................................................................ 2097.3. The logit function. ..................................................................................................................... 218Chapter 8 – Real-life case-study: The quarterly sales revenues of Nokia Corporation............................................................................................................... 2238.1. Introduction. ............................................................................................................................. 2238.2. Preliminary specification of the model. .................................................................................... 2238.3. Detection of potential outliers in the dataset .......................................................................... 2258.4. Selection of explanatory variables (from the set of candidates). ............................................. 2318.5. Verification of the obtained model. .......................................................................................... 2348.6. Evaluation of the predictive power of the estimated model. ................................................... 246Chapter 9 – Real-life case-study: Identifying overvalued and undervalued airlines ........................................................................................................................ 2529.1. Introduction. ............................................................................................................................. 2529.2. Preliminary specification of the model. .................................................................................... 2529.3. Detection of potential outliers in the dataset .......................................................................... 2549.4. Selection of explanatory variables (from the set of candidates). ............................................. 2589.5. Verification of the obtained model. .......................................................................................... 2599.6. Evaluation of model usefulness in identifying overvalued and undervalued stocks. ............... 268Appendix – Statistical Tables ................................................................................... 271A1. Critical values for F-statistic for k = 0,05................................................................................. 271A2. Critical values for t-statistic. ...................................................................................................... 273A3. Critical values for Chi-squared statistic. .................................................................................... 274282A4. Critical values for Hellwig test. .................................................................................................. 275A5. Critical values for symmetry test for k = 0,10. ........................................................................ 276A6. Critical values for maximum series length test for k = 0,05. ................................................... 276A7. Critical values for number of series test for k = 0,05. ............................................................. 277