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5. Statistics Education from a Data-Centric Perspective

Author

Gehrke, Matthias, Kistler, Tanja, Lübke, Karsten, Markgraf, Norman, Krol, Bianca, and Sauer, Sebastian

Abstract

The ubiquitous acquisition and generation of data require a reworking of curricula in introductory statistics in tertiary education. We present a renewed curriculum that focuses on scientific thinking, modeling, and simulation-based inference, utilizing R and various R tools such as shiny and learnr apps. We teach statistics from a data-centric perspective, enabling the students to become data literate. Initial feedback of students and educators at our university of applied sciences shows that students' conceptual understanding improved and they understood the practical applicability of statistics better.

Published
2021
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6. Why We Should Teach Causal Inference: Examples in Linear Regression with Simulated Data

Author

Lübke, Karsten, Gehrke, Matthias, Horst, Jörg, and Szepannek, Gero

Abstract

Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also help to overcome the mantra "Correlation does not imply Causation." To motivate and introduce causal inference in introductory statistics or data science courses, we use simulated data and simple linear regression to show the effects of confounding and when one should or should not adjust for covariables.

Published
2020
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7. Statistical Computing and Data Science in Introductory Statistics

Author

Lübke, Karsten, Gehrke, Matthias, Markgraf, Norman, Gaul, Wolfgang, Managing Editor, Vichi, Maurizio, Managing Editor, Weihs, Claus, Managing Editor, Baier, Daniel, Editorial Board Member, Critchley, Frank, Editorial Board Member, Decker, Reinhold, Editorial Board Member, Diday, Edwin, Editorial Board Member, Greenacre, Michael, Editorial Board Member, Lauro, Carlo Natale, Editorial Board Member, Meulman, Jacqueline, Editorial Board Member, Monari, Paola, Editorial Board Member, Nishisato, Shizuhiko, Editorial Board Member, Ohsumi, Noboru, Editorial Board Member, Opitz, Otto, Editorial Board Member, Ritter, Gunter, Editorial Board Member, Schader, Martin, Editorial Board Member, Bauer, Nadja, editor, Ickstadt, Katja, editor, Lübke, Karsten, editor, Szepannek, Gero, editor, and Trautmann, Heike, editor

Published
2019
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12. Daten

Author

Lübke, Karsten, Vogt, Martin, FOM Hochschule für Oekonomie & Mana, Lübke, Karsten, and Vogt, Martin

Published
2014
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14. Daten und Zufall

Author

Lübke, Karsten, Vogt, Martin, FOM Hochschule für Oekonomie & Mana, Lübke, Karsten, and Vogt, Martin

Published
2014
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15. Firm-Specific Determinants on Dividend Changes: Insights from Data Mining

Author

Luebke, Karsten, Rojahn, Joachim, Bock, Hans-Hermann, Editor-in-chief, Baier, Daniel, Series editor, Gaul, Wolfgang, Editor-in-chief, Vichi, Maurizio, Editor-in-chief, Critchley, Frank, Series editor, Decker, Reinhold, Series editor, Diday, Edwin, Series editor, Greenacre, Michael, Series editor, Lauro, Carlo Natale, Series editor, Meulman, Jacqueline, Series editor, Monari, Paola, Series editor, Nishisato, Shizuhiko, Series editor, Ohsumi, Noboru, Series editor, Opitz, Otto, Series editor, Ritter, Gunter, Series editor, Schader, Martin, Series editor, Weihs, Claus, Editor-in-chief, Wilhelm, Adalbert F.X., editor, and Kestler, Hans A., editor

Published
2016
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21. Local Models in Register Classification by Timbre

Author

Weihs, Claus, Szepannek, Gero, Ligges, Uwe, Luebke, Karsten, Raabe, Nils, Bock, H. -H., editor, Gaul, W., editor, Vichi, M., editor, Arabie, Ph., editor, Baier, D., editor, Critchley, F., editor, Decker, R., editor, Diday, E., editor, Greenacre, M., editor, Lauro, C., editor, Meulman, J., editor, Monari, P., editor, Nishisato, S., editor, Ohsumi, N., editor, Opitz, O., editor, Ritter, G., editor, Schader, M., editor, Weihs, C., editor, Batagelj, Vladimir, editor, Bock, Hans-Hermann, editor, Ferligoj, Anuška, editor, and Žiberna, Aleš, editor

Published
2006
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22. klaR Analyzing German Business Cycles

Author

Weihs, Claus, Ligges, Uwe, Luebke, Karsten, Raabe, Nils, Bock, H.-H., editor, Gaul, W., editor, Vichi, M., editor, Arabie, Ph., editor, Baier, D., editor, Critchley, F., editor, Decker, R., editor, Diday, E., editor, Greenacre, M., editor, Lauro, C., editor, Meulman, J., editor, Monari, P., editor, Nishisato, S., editor, Ohsumi, N., editor, Opitz, O., editor, Ritter, G., editor, Schader, M., editor, Weihs, C., editor, Baier, Daniel, editor, Decker, Reinhold, editor, and Schmidt-Thieme, Lars, editor

Published
2005
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23. KMC/EDAM: A New Approach for the Visualization of K-Means Clustering Results

Author

Raabe, Nils, Luebke, Karsten, Weihs, Claus, Bock, H.-H., editor, Gaul, W., editor, Vichi, M., editor, Arabie, Ph., editor, Baier, D., editor, Critchley, F., editor, Decker, R., editor, Diday, E., editor, Greenacre, M., editor, Lauro, C., editor, Meulman, J., editor, Monari, P., editor, Nishisato, S., editor, Ohsumi, N., editor, Opitz, O., editor, Ritter, G., editor, Schader, M., editor, Weihs, C., editor, Weihs, Claus, editor, and Gaul, Wolfgang, editor

Published
2005
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24. Different Subspace Classification

Author

Szepannek, Gero, Luebke, Karsten, Bock, H.-H., editor, Gaul, W., editor, Vichi, M., editor, Arabie, Ph., editor, Baier, D., editor, Critchley, F., editor, Decker, R., editor, Diday, E., editor, Greenacre, M., editor, Lauro, C., editor, Meulman, J., editor, Monari, P., editor, Nishisato, S., editor, Ohsumi, N., editor, Opitz, O., editor, Ritter, G., editor, Schader, M., editor, Weihs, C., editor, Weihs, Claus, editor, and Gaul, Wolfgang, editor

Published
2005
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25. Understanding Patterns with Different Subspace Classification

Author

Szepannek, Gero, Luebke, Karsten, Weihs, Claus, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Perner, Petra, editor, and Imiya, Atsushi, editor

Published
2005
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28. How much do we see? On the explainability of partial dependence plots for credit risk scoring.

Author

Szepannek, Gero and Lübke, Karsten

Subjects
CREDIT scoring systems, MACHINE learning, ARTIFICIAL intelligence, CREDIT analysis
Abstract

Risk prediction models in credit scoring have to fulfil regulatory requirements, one of which consists in the interpretability of the model. Unfortunately, many popular modern machine learning algorithms result in models that do not satisfy this business need, whereas the research activities in the field of explainable machine learning have strongly increased in recent years. Partial dependence plots denote one of the most popular methods for model-agnostic interpretation of a feature's effect on the model outcome, but in practice they are usually applied without answering the question of how much can actually be seen in such plots. For this purpose, in this paper a methodology is presented in order to analyse to what extent arbitrary machine learning models are explainable by partial dependence plots. The proposed framework provides both a visualisation, as well as a measure to quantify the explainability of a model on an understandable scale. A corrected version of the German credit data, one of the most popular data sets of this application domain, is used to demonstrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Digital oder Real: Analyse präferierter Lehrformate von Studierenden.

Author

SYDOW, STEVE, ASCHENBRUCK, RABEA, SZEPANNEK, GERO, and LÜBKE, KARSTEN

Subjects
PSYCHOLOGICAL typologies, PERSONALITY, STUDENT surveys, DIGITAL learning, CLUSTER analysis (Statistics), BLENDED learning, ONLINE education
Abstract

Copyright of Hochschullehre is the property of wbv Media GmbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2022
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39. Subjective Financial Well-Being An Explorative Analysis by Algorithmic Modeling Techniques

Author

Christiaans, Thomas, Lübke, Karsten, and Richenhagen, Julia

Subjects
Economics, ddc:330
Abstract

Taking non-rational behavior and incomplete information into account, actual income distributions and financial well-being probably deviate from their perceptions. Based on a questionnaire sample of more than 45,000 Germans we investigate which socio-economic variables and combinations of these may help to model subjective financial well-being. Additionally to age, gender, and regional information, (perceived) household income and local and country-wide rankings of income distribution are used as possible modeling variables. The link is investigated by means of ordinary least squares regression as well as tree based methods. It turns out that on our sample, additionally to subjective income class and reported actual income, age, region and, though to a lower extend, gender help to model subjective financial well-being.

Published
2018

41. Einführung von Elektromobilität in Deutschland: Eine Bestandsaufnahme von Barrieren und Lösungsansätzen

Author

Hose, Christian, Lübke, Karsten, Nolte, Thomas, and Obermeier, Thomas

Abstract

[Vorwort] Elektromobilität gilt national und international seit Jahren als Schlüsseltechnologie der Zukunft, die dazu beitragen soll, lokale Emissionen zu senken, weniger Primärenergien zu verbrauchen und dadurch klimaschonend zu wirken. Die Bundesregierung hat sich demzufolge innerhalb ihres Regierungsprogrammes Elektromobilität u. a. zum Ziel gesetzt, bis zum Jahr 2020 die Zulassungszahlen von Elektroautos auf eine Million auszubauen. Die aktuelle Bilanz fällt allerdings ernüchternd aus, da die Entwicklung der Zulassungszahlen bisher schleppend verläuft und die anvisierten Zahlen mithin deutlich verfehlt werden. Trotz aller politischen Bemühungen scheinen die geschaffenen Rahmenbedingungen nicht überzeugend genug zu sein, um das Ziel zu erreichen. Da die Verantwortlichen sich laut NPE (Plattform Elektromobilität) in der sogenannten Marktvorbereitungsphase vornehmlich auf technische Aspekte, wie Antriebssysteme, Ladeinfrastruktur und Batterietechnologien, konzentriert haben, um Vorreiter elektronischer Mobilität zu werden, wurde bislang der eigentlichen Kundenperspektive wenig Beachtung geschenkt. Letztere wird aber in der Markthochlaufphase zunehmend an Bedeutung gewinnen, da sich die Elektrofahrzeuge im Wettbewerb mit anderen Antriebskonzepten insbesondere in Hinblick auf den Preis und die Reichweite behaupten müssen. Zahlreiche Automobilhersteller bieten zwar in ihren Portfolios bereits Elektroautos an, jedoch zu vergleichsweise sehr hohen Anschaffungskosten. Um dem ambitionierten Ziel näher zu kommen, wird es von entscheidender Bedeutung sein, Prioritäten der Nutzer zu beobachten sowie Erfahrungen und Daten über Nutzerverhalten auszuwerten, um die notwendigen Schlüsse daraus zu ziehen und die Rahmenbedingungen entsprechend anpassen zu können. An diesem Punkt setzt die Forschungsinitiative FOM-FIRST in dem vorliegenden Arbeitspapier an. Mittels einer empirischen Erhebung wurden FOM-Studierende verschiedener Hochschulzentren sowohl zu Ihrem Informationsstand als auch zur Akzeptanz in Bezug auf das Thema Elektromobilität befragt. Auf Basis der ermittelten Ergebnisse analysieren die Autoren Ursachen, die allgemein für die mangelnde Kaufbereitschaft verantwortlich sein können. Anschließend werden daraus mögliche Treiber identifiziert, die die Akzeptanz von Elektrofahrzeugen erhöhen könnten.

Published
2015

42. Nachhaltigkeit als betriebswirtschaftlicher Wettbewerbsfaktor: Eine Propensity Score Analyse deutscher Aktiengesellschaften

Author

Hose, Christian, Lübke, Karsten, Nolte, Thomas, and Obermeier, Thomas

Abstract

Mit der vorliegenden empirischen Studie untersucht die FOM-FIRSTForschungsinitiative die Motivationsfaktoren von Unternehmen zur Erstellung von Nachhaltigkeitsberichten, indem sie den Zusammenhang zwischen den veröffentlichten Nachhaltigkeitsbemühungen deutscher Aktiengesellschaften und dem betriebswirtschaftlichen Erfolg anhand des PSM (Propensity Score Matching)-Ansatzes, einer statistischen Auswertungsmethodik, ermittelt. Die Studie wurde mit Hilfe von FOM-Studierenden mehrerer Standorte durchgeführt und basiert auf den Merkmalen und Dimensionen verschiedener Modelle zur Nachhaltigkeitsermittlung. Die Ergebnisse stützen sich zwar auf die Analyse eines nur begrenzten Datenmaterials, lassen aber dennoch neue Erkenntnisse zu und geben daher Anlass zu weiteren Forschungen.

Published
2014

43. Adaptive lineare Dimensionsreduktion in der Klassifikation

Author

Lübke, Karsten, Weihs, Claus, and Krämer, Walter

Subjects
adaptive method, Datenraumbeschreibung, classification, dimension reduction, Klassifikation, adaptive Verfahren, Dimensionsreduktion, data space description
Published
2006

44. A Computer Intensive Method for Choosing the Ridge Parameter

Author

Czogiel, Irina, Lübke, Karsten, and Weihs, Claus

Subjects
prediction model, experimental design, generalized cross validation, ridge regression
Abstract

In this paper we describe a computer intensive method to find the ridge parameter in a prediction oriented linear model. With the help of a factorial experimental design the method is tested and compared to a classical one.

Published
2004

45. A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification

Author

Lübke, Karsten and Weihs, Claus

Subjects
Statistics::Machine Learning, ComputingMethodologies_PATTERNRECOGNITION, classification, latent factor models, regression, projection matrix
Abstract

In this paper it is shown that the number of latent factors in a multiple multivariate regression model need not be larger than the number of the response variables in order to achieve an optimal prediction. The practical importance of this lemma is outlined and an application of such a projection on latent factors in a classification example is given.

Published
2004
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46. A computer intensive method for choosing the ridge parameter

Author

Lübke, Karsten, Czogiel, Irina, and Weihs, Claus

Subjects
Ridge Regression,Experimental Design,Prediction model,Generalized Cross Validation
Abstract

In this paper we describe a computer intensive method to find the ridge parameter in a prediction oriented linear model. With the help of a factorial experimental design the method is tested and compared to a classical one.

Published
2004

48. BackMatter.

Author

Lübke, Karsten and Vogt, Martin

Published
2014

49. FrontMatter.

Author

Lübke, Karsten and Vogt, Martin

Published
2014

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