Essays about: "Application of statistics in business"

Showing result 1 - 5 of 19 essays containing the words Application of statistics in business.

1. 1. The Extended Maximum Likelihood Estimation for Monotone Probability Mass Function with Application using Forensic Data

   University essay from Lunds universitet/Matematisk statistik

   Author : Tiancheng Ma; [2022]
   Keywords : Statistical Inference Statistics Maximum Likelihood Estimation Non-parametric Estimation; Mathematics and Statistics;

   Abstract : This paper presents solutions to the modelling of frequency data of species labels, but the data is incomplete in the sense that some rarely-occurring species labels give zero observed frequency. The data can be modelled by a monotone probability function with parameters to be estimated, and yet, due to the order constraints and the incomplete data, using conventional parameter estimation methods will cause trouble. READ MORE

3. 2. Modelling Customer Lifetime Value in the Retail Banking Industry

   University essay from KTH/Matematisk statistik

   Author : Max Völcker; Carl Stenfelt; [2021]
   Keywords : CLV; Statistics; applied mathematics; marketing; machine learning; markov chains; CART; random forest; retail banking; CLV; Statistik; tillämpad matematik; marknadsföring; maskininlärning; markovkedjor; CART; random forest; banksektorn;

   Abstract : This thesis was conducted in cooperation with the Swedish bank SEB, who expressed interest in getting an increased understanding of how the marketing measure Customer Lifetime Value could be implemented and used in the retail banking industry. Accordingly, the purpose of this thesis was to provide insight into how Customer Lifetime Value could be modelled in an appropriate way in the retail banking industry and provide an increased understanding of necessary considerations for the modelling process. READ MORE

4. 3. Detecting changes in web applications

   University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

   Author : Phillip Lunyov; [2020]
   Keywords : web application security analysis; change identification and classification; diff-algorithms;

   Abstract : As the availability and popularity of the Internet continues to grow, the trend ofproviding global access to business resources and services online is an efficient andprofitable way for organizations to acquire a new share of the market. Due to the flexibilityand scalability of modern web technologies, web-based applications processand store personal or critical information in enormous amounts. READ MORE

5. 4. Design Development of an Energy Supplier Application Focusing on the User Experience

   University essay from Lunds universitet/Ergonomi och aerosolteknologi

   Author : Ayla Borglund; Julia Giver; [2019]
   Keywords : UX; UI; Interaction design; User test; Prototyping; Environmental awareness; Technology and Engineering;

   Abstract : What do customers want from an energy supplier application? To meet the increasing digitalization of the society, the energy supplier Kraftringen strives to create a new application that will match the customer’s needs and Kraftringen’s vision. This thesis was performed in order to present a user interface proposal of Kraftringen’s new application, to improve the user experience. READ MORE

6. 5. High-risk Consumer Credit Scoring using Machine Learning Classification

   University essay from Lunds universitet/Matematisk statistik

   Author : Max Mjörnell; Ludvig Levay; [2019]
   Keywords : Machine learning; Scorecard modelling; Logistic regression; Support Vector Machine; Decision Tree; Random Forest; k-Nearest Neighbors; Artificial Neural Network; Voting ensemble; SHAP; LIME; Average Precision score; Feature engineering; Mathematics and Statistics;

   Abstract : The use of statistical models in credit rating and application scorecard modelling is a thoroughly explored field within the financial sector and a central component in a credit institution’s underlying business model. The aim of this report was to apply and compare six different machine learning models in predicting credit defaults for high-risk consumer credits, using a data set provided by a Swedish consumer credit institute. READ MORE