Math for Business and Economics: Compendium of Essential Formulas 2nd Edition by Franz W. Peren 9783662658468 3662658461

Math for Business and Economics: Compendium of Essential Formulas 2nd Edition Franz W. Peren – Ebook Instant Download/Delivery ISBN(s): 9783662658468, 3662658461

Product details:

• ISBN 10: 3662658461
• ISBN 13:9783662658468
• Author:  Franz W. Peren

Statistics for Business and Economics
Compendium of Essential Formulas

Table contents:

Chapter 1 Statistical Signs and Symbols
General
Set Theory
Chapter 2 Descriptive Statistics
2.1 Empirical Distributions
2.1.1 Frequencies
2.1.2 Cumulative Frequencies
2.2 Mean Values and Measures of Dispersion
2.2.1 Mean Values
2.2.2 Measures of Dispersion
2.3 Ratios and Index Figures
2.3.1 Ratios
2.3.2 Index Figures
2.3.3 Peren-Clement Index (PCI)
2.4 Correlation Analysis
2.5 Regression Analysis
2.5.1 Simple Linear Regression
2.5.1.1 Confidence Intervals for the Regression Coefficients of a Simple Linear Regression Function
2.5.1.2 Student’s t-Tests for the Regression Coefficients of a Simple Linear Regression Function
2.5.2 Multiple Linear Regression
2.5.2.1 Confidence Intervals for the Regression Coefficients of a Multiple Linear Regression Functio
2.5.2.2 Student’s t-Tests for the Regression Coefficients of a Multiple Linear Regression Function
2.5.3 Double Linear Regression
2.5.3.1 Confidence Intervals for the Regression Coefficients of a Double Linear Regression Function
2.5.3.2 Student’s t-Tests for the Regression Coefficients of a Double Linear Regression Function
Chapter 3 Inferential Statistics
3.1 Probability Calculation
3.1.1 Fundamental Terms/Definitions
3.1.2 Theorems of Probability Theory
3.2 Probability Distributions
3.2.1 Concept of Random Variables
3.2.2 Probability, Distribution and Density Function
3.2.2.1 Discrete Random Variables Probability Function
3.2.2.2 Continuous Random Variables
3.2.3 Parameters for Probability Distributions
3.3 Theoretical Distributions
3.3.1 Discrete Distributions
3.3.2 Continuous Distributions
3.4 Statistical Estimation Methods (Confidence Intervals)
3.4.1 Confidence Interval for the Arithmetic Mean of the Population μ
3.4.2 Confidence Interval for the Variance of the Population σ2
3.4.3 Confidence Interval for the Share Value in the Population θ
3.4.4 Confidence Interval for the Difference of the Mean Values of Two Populations μ1 and μ2
3.4.5 Conficence Interval for the Difference of the Share Values of Two Populations θ1 and θ2
3.5 Determination of the Required Sample Size
3.5.1 Determination of the Required Sample Size for an Estimation of the Arithmetic Mean μ
3.5.2 Determination of the Required Sample Size for an Estimation of the Share Value θ
3.6 Statistical Testing Methods
3.6.1 Parameter Tests
3.6.1.1 Arithmetic Mean with Known Variance of the Population | One Sample Test
3.6.1.2 Arithmetic Mean with Unknown Variance of the Population | One Sample Test
3.6.1.3 Share Value | One Sample Test
3.6.1.4 Variance | One Sample Test
3.6.1.5 Difference of Two Arithmetic Means with Known Variances of the Population | Two Samples Test
3.6.1.6 Difference of Two Arithmetic Means with Unknown Variances of the Populations under the Assum
3.6.1.7 Difference of Two Arithmetic Means with Unknown Variances of the Populations under the Assum
3.6.1.8 Difference of Two Share Values | Two Samples Test
3.6.1.9 Quotients of Two Variances | Two Samples Test
3.6.2 Distribution Tests (Chi-Squared Tests)
3.6.2.1 Chi-Squared Goodness of Fit Test
3.6.2.2 Chi-Squared Independence Test
3.6.2.3 Chi-Squared Homogeneity Test
3.6.3 Yates’s Correction
Chapter 4 Probability Calculation
4.1 Terms and Definitions
4.2 Definitions of Probability
4.2.1 The Classical Definition of Probabilty
4.2.2 The Statistical Definition of Probability
4.2.3 The Subjective Definition of Probability
4.2.4 Axioms of Probability Calculation
4.3 Theorems of Probability Calculation
4.3.1 Theorem of Complementary Events
4.3.2 The Multiplication Theorem with Independence of Events
4.3.3 The Addition Theorem
4.3.4 Conditional Probability
4.3.5 Stochastic Independence
4.3.6 The Multiplication Theorem in General Form
4.3.7 The Theorem of Total Probability
4.3.8 Bayes’ Theorem (Bayes’ Rule)
4.3.9 Overview of the Probability Calculation of Mutually Exclusive and Non-Exclusive Events
4.4 Random Variable
4.4.1 The Concept of Random Variables
4.4.2 The Probability Function of Discrete Random Variables
4.4.3 The Distribution Function of Discrete Random Variables
4.4.4 Probability Density and Distribution Function of Continuous Random Variables
4.4.5 Expected Value and Variance of Random Variables

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