According to Daimler Annual Report 2015 that we can find data about how many employees that Daimler AG have and whether or not the number of Daimler AG's employees gives impact on an increase or decrease of Daimler AG's revenue as well as earnings. Daimler AG involves five divisions such as Mercedes-Benz Cars, Daimler Trucks, Mercedes-Benz Vans, Daimler Buses and Daimler Financial Services. To facilitate a better comparison view between the number of employees, Earnings (EBIT) and Revenue the author decided to select the data with only arising the relevant data with his objectives.
From 2013 to 2015 in department of Mercedes-Benz Cars, Mercedes-Benz Vans and Daimler Financial Services there is a correlation between an increase of the number of employees with EBIT and Revenue. Conversely, in department of Daimler Trucks and Daimler Buses with the same period looks uncorrelated between an increase of the number of employees and Revenue. In Daimler Group a correlation between the high number of employees with an increase of their share price and their market capitalization is shown where in 2012 the number of their employees about 279.972 increased about 284.015. This increase was followed by an increase of share price from 68,97 to 77,58 and of Market capitalization from 73 to 83.
Because of that, in this paper we would briefly proof how strong the correlation between one factor with other factors through SPSS software which would be used to observe their correlation with aid of descriptive statistics (e.g. Histogram, Mean, Variance, Minimum, Maximum and Scatter plot) and inferential statistics (Linear Regression, Multiple Regression, Multicollinearity and Outliers) [...]
Table of Contents
1. Introduction
2. Analysis of Using Quantitative Tools
2.1 Descriptive Statistics
2.1.1 Histogram, Mean and Standard Deviation
2.1.2 Minimum and Maximum
2.1.3 Scatter Plot
2.2 Inferential Statistics
2.2.1 Linear regression
2.2.2 Multiple Regression
2.2.3 Multicollinearity
2.2.4 Outliers
3. A conclusion
Objectives and Topics
This term paper aims to analyze the statistical relationship between the number of employees and various financial indicators—specifically revenue, earnings, market capitalization, and share price—for 57 large German companies listed on the stock exchange in 2006, utilizing SPSS for both descriptive and inferential analysis.
- Application of descriptive statistics to characterize company data.
- Use of scatter plots to visualize relationships between variables.
- Execution of linear and multiple regression models for predictive analysis.
- Identification and treatment of multicollinearity and outliers to improve model accuracy.
- Evaluation of statistical significance and model reliability via R-squared and probability values.
Excerpt from the Book
2.1.1 Histogram, Mean and Standard Deviation
The histogram in the figure above shows the frequencies of the observed number of Employees. The number of Employees that is lower than 100000 representing the top 35% of the observed Frequency, and bigger than 200000, representing the minimum of the Staff members. On the horizontal line are Number of employees and on the vertical line the corresponding frequencies (how many times a certain value has been observed).
The distribution of the Employees is clearly skewed to the right, as there are relatively very few companies that employs a lot of Workers to manage their operations. We called a positively skewed distribution.
Which is located slightly to the right of the peak of the frequencies, since the distribution is skewed to the right. Actually, the mean best describes the central tendency of a variable when the variable is fully symmetrical for the distribution of employees above the mean is about 73296.88. However, it is not in our example. It is obvious that the mean is being dragged in the direct of the skew. In these situations, the median is generally considered to be the best representative of the central location of the data. The more skewed the distribution, the greater the difference between the median and mean, and the greater emphasis should be placed on using the median as opposed to the mean.
Summary of Chapters
1. Introduction: This chapter introduces the dataset of 57 German companies and outlines the research objective to investigate the correlation between company size (employees) and financial performance metrics.
2. Analysis of Using Quantitative Tools: This core chapter details the statistical methodology, providing a breakdown of descriptive statistics, regression analysis, and diagnostic procedures used for the data.
2.1 Descriptive Statistics: This section utilizes histograms, means, medians, and standard deviations to characterize the distribution of the collected economic variables.
2.1.1 Histogram, Mean and Standard Deviation: This section analyzes the frequency distribution of employee counts, illustrating right-skewed data and discussing the reliability of mean versus median.
2.1.2 Minimum and Maximum: This section examines the range of the dataset, identifying extreme values for employees, revenue, and market capitalization across the selected firms.
2.1.3 Scatter Plot: This section explains the visualization of correlations between independent variables and the number of employees, demonstrating how regression lines represent associations.
2.2 Inferential Statistics: This section introduces the use of linear and multiple regression models to generalize findings from the sample to the broader population.
2.2.1 Linear regression: This section evaluates the simple linear relationship between employee count and specific financial variables.
2.2.2 Multiple Regression: This section explores how multiple independent variables simultaneously predict the dependent variable, incorporating coefficients and regression equations.
2.2.3 Multicollinearity: This section addresses the statistical challenge where independent variables are highly correlated, using VIF and tolerance measures to ensure model validity.
2.2.4 Outliers: This section discusses the identification and exclusion of extreme data points to refine regression accuracy and improve the model's predictive power.
3. A conclusion: This final chapter synthesizes the results, confirming which variables are statistically significant and highlighting the importance of proper statistical diagnostics in economic research.
Keywords
Quantitative Tools, Descriptive Statistics, Inferential Statistics, SPSS, Linear Regression, Multiple Regression, Multicollinearity, Outliers, Revenue, Earnings, Market Capitalization, Share Price, Employee Count, Statistical Significance, R-squared.
Frequently Asked Questions
What is the primary focus of this paper?
The paper focuses on analyzing the impact of financial performance indicators on the number of employees within the 57 largest German companies listed on the stock exchange in 2006.
What are the central themes discussed in the document?
The central themes include the application of statistical methods such as descriptive analysis, regression modeling, and diagnostic tests for multicollinearity and outliers in a business economics context.
What is the main research objective?
The primary objective is to determine whether and how strongly variables like revenue, earnings, market capitalization, and share price correlate with the number of employees in a company.
Which scientific methods are employed in the study?
The study uses the SPSS software to perform descriptive statistics (histograms, mean, median) and inferential statistics (simple and multiple linear regression, correlation matrices, and VIF/Tolerance testing).
What topics are covered in the main section?
The main section covers the analysis of quantitative data, specifically transitioning from descriptive summaries to inferential models, including the detailed treatment of statistical anomalies.
Which keywords best characterize this work?
Key terms include Quantitative Tools, SPSS, Regression Analysis, Multicollinearity, and financial performance metrics like Market Capitalization and Revenue.
How is the influence of outliers addressed in the study?
Outliers are identified using residuals and Studentized Deleted Residuals; the study compares regression models before and after their removal to demonstrate improved predictive accuracy.
What conclusion does the author reach regarding share price?
The author concludes that, unlike revenue, earnings, and market capitalization, share price is not statistically significant in relation to the number of employees.
How is the reliability of the regression model assessed?
Reliability is assessed primarily through the R-squared value, adjusted R-squared, standard error of the estimate, and significance tests (p-values) to ensure the model is fit for the data.
What is the significance of the VIF values mentioned?
VIF (Variance Inflation Factor) values are used to quantify the presence of multicollinearity, helping the authors determine if independent variables are too closely correlated to provide stable regression coefficients.
- Citation du texte
- Bachelor of Arts Christoper Dewangga Pramudita (Auteur), Nadia Ouertani (Auteur), 2017, Analysis of Using Quantitative Tools in the 57 largest German Companies listed in the Stock Exchange in 2006, Munich, GRIN Verlag, https://www.grin.com/document/359241
