This book shows you how to analyze data sets systematically and to use Excel 2019 to extract information from data almost effortlessly. Both are (not) an art!
The statistical methods are presented and discussed using a single data set. This makes it clear how the methods build on each other and gradually more and more information can be extracted from the data. The Excel functions used are explained in detail - the procedure can be easily transferred to other data sets.
Various didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform. This book is a translation of the original German 2nd edition Statistik angewandt mit Excel by Franz Kronthaler, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2021. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.
Author(s): Franz Kronthaler
Publisher: Springer
Year: 2022
Language: English
Pages: 342
City: Berlin
A Note to the Reader
New Features and Additions
Acknowledgements
Contents
List of Figures
List of Tables
Part I Basic Knowledge and Tools to Apply Statistics
1 Statistics Is Fun
1.1 Why Statistics?
1.2 Checkpoints
1.3 Data
1.4 Checkpoints
1.5 Scales: Lifelong Important in Data Analysis
1.6 Checkpoints
1.7 Software: Excel, SPSS, or ``R''
1.8 Case Studies: The Best Way to Learn
1.9 Case Study: Growth of Young Enterprises
1.10 Applications
2 Excel: A Brief Introduction to the Statistical Tools
Part II Describe, Nothing but Describe
Describing People or Objects, or Simply Descriptive Statistics
3 Average Values: How People and Objects Behave in General
3.1 Average Values: For What Do We Need Them
3.2 The (Arithmetic) Mean
3.3 The Median
3.4 The Mode
3.5 The Geometric Mean and Growth Rates
3.6 What Average Value Should We Use and What else Do We Need to Know?
3.7 Calculating Averages with Excel
3.7.1 Calculating the Arithmetic Mean with Excel
3.7.2 Calculating the Median with Excel
3.7.3 Calculating the Mode with Excel
3.7.4 Calculating the Geometric Mean with Excel
3.8 Checkpoints
3.9 Applications
4 Variation: The Deviation from Average Behavior
4.1 Variation: The Other Side of Average Behavior
4.2 The Range
4.3 The Standard Deviation
4.4 The Variance
4.5 The Coefficient of Variation
4.6 The Interquartile Range
4.7 The Boxplot
4.8 Calculating Variation Measures with Excel
4.8.1 Calculating the Range (MIN, MAX) with Excel
4.8.2 Calculating the Standard Deviation with Excel
4.8.3 Calculating the Variance with Excel
4.8.4 Calculating of the Interquartile Range (First Quartile and Third Quartile) with Excel
4.9 Creating the Boxplot with Excel
4.10 Checkpoints
4.11 Applications
5 Charts: The Possibility to Display Data Visually
5.1 Charts: Why Do We Need Them?
5.2 The Frequency Table
5.3 The Frequency Charts
5.4 Absolute Frequency Chart, Relative Frequency Chart, or Histogram?
5.5 More Ways to Display Data
5.6 Creating the Frequency Table, Frequency Charts and Other Graphs with Excel
5.7 Checkpoints
5.8 Applications
6 Correlation: From Relationships
6.1 Correlation: The Joint Movement of Two Variables
6.2 The Correlation Coefficient of Bravais–Pearson for Metric Variables
6.3 The Scatterplot
6.4 The Correlation Coefficient of Spearman for Ordinal Variables
6.5 The Phi Coefficient for Nominal Variables with Two Characteristics
6.6 The Contingency Coefficient for Nominal Variables
6.7 Correlation, Spurious Correlation, Causality, and More Correlation Coefficients
6.8 Calculating Correlation Coefficients with Excel
6.8.1 Calculating the Correlation Coefficient Bravais–Pearson with Excel Using the Command Insert Function
6.8.2 Calculating the Correlation Coefficient Bravais–Pearson with Excel Using the Data Analysis Command
6.8.3 Determine the Ranks for an Ordinal Variable with Excel
6.8.4 Creating a Pivot Table with Excel
6.9 Checkpoints
6.10 Applications
7 Ratios and Indices: The Opportunity to Generate New Knowledge from Old Ones
7.1 Different Ratio Numbers
7.2 The Price and Quantity Index of Laspeyres and Paasche
7.3 Checkpoints
7.4 Applications
Part III From Few to All
From Few to All or from the Sample to the Population
8 Of Data and Truth
8.1 How do We Get our Data: Primary or Secondary Data?
8.2 The Random Sample: The Best Estimator for Our Population
8.3 Of Truth: Validity and Reliability
8.4 Checkpoints
8.5 Applications
9 Hypotheses: Only a Specification of the Question?
9.1 The Little, Big Thing of the (Research) Hypothesis
9.2 The Null Hypothesis H0 and the Alternative Hypothesis HA
9.3 Hypotheses, Directional or Non-directional?
9.4 How to Formulate a Good Hypothesis?
9.5 Checkpoints
9.6 Applications
10 Normal Distribution and Other Test Distributions
10.1 The Normal Distribution
10.2 The z-Value and the Standard Normal Distribution
10.3 Normal Distribution, t-Distribution, χ2-Distribution and (or) F-Distribution
10.4 Creating Distributions with Excel
10.4.1 Drawing the Normal Distribution N(100, 20) with Excel
10.4.2 Drawing the t-Distribution Curve with 15 Degrees of Freedom Using Excel
10.4.3 Drawing the χ2-Distribution Curve with 10 Degrees of Freedom Using Excel
10.4.4 Drawing the F-Distribution Curve with Two Times 15 Degrees of Freedom with Excel
10.5 Checkpoints
10.6 Applications
11 Hypothesis Test: What Holds?
11.1 What Does Statistically Significant Mean?
11.2 The Significance Level α
11.3 Statistically Significant, But also Practically Relevant?
11.4 Steps When Performing a Hypothesis Test
11.5 How do I Choose My Test?
11.6 Checkpoints
11.7 Applications
Part IV Hypothesis Tests
Time to Apply the Hypothesis Test
12 The Test for a Group Mean or One-Sample t-Test
12.1 Introduction to the Test
12.2 The Research Question and Hypothesis: Are Company Founders on Average 40 Years Old?
12.3 The Test Distribution and Test Statistic
12.4 The Critical Value
12.5 The z-Value
12.6 The Decision
12.7 The Test When the Standard Deviation in the Population Is Unknown or the Sample Is Small n ≤ 30
12.8 The Effect Size
12.9 Calculating the One Sample t-Test with Excel
12.10 Checkpoints
12.11 Applications
13 The Test for a Difference Between Group Means or Independent Samples t-Test
13.1 Introduction to the Test for Difference Between Group Means with Independent Samples
13.2 The Research Question and Hypothesis: Are Women and Men of the Same Age When Starting an Enterprise?
13.3 The Test Distribution and the Test Statistic
13.4 The Critical t-Value
13.5 The t-Value and the Decision
13.6 The Effect Size
13.7 Equal or Unequal Variances
13.8 Calculating the Independent Samples t-Test with Excel
13.9 Checkpoints
13.10 Applications
14 The Test for a Difference Between Means with Dependent Samples or Dependent Samples t-Test
14.1 Introduction to the Test for a Difference Between Means with Dependent Samples
14.2 The Example: Training for Enterprise Founders in the Pre-founding Phase
14.3 The Research Question and the Hypothesis in the Test: Does the Training have an Influence on the Market Potential Estimation?
14.4 The Test Statistic
14.5 The Critical t-Value
14.6 The t-Value and the Decision
14.7 The Effect Size
14.8 Calculating the Dependent Samples t-Test with Excel
14.9 Checkpoints
14.10 Applications
15 The Analysis of Variance to Test for Group Differences When There Are More Than Two Groups
15.1 Introduction to the Analysis of Variance
15.2 The Example: Do Enterprise Founders with Different Founding Motives Differ in the Amount of Time They Work?
15.3 The Research Question and the Hypothesis of the Analysis of Variance
15.4 The Basic Idea of the Analysis of Variance
15.5 The Test Statistic
15.6 The Critical F-Value
15.7 The F-Value and the Decision
15.8 The Analysis of Variance an Omnibus Test and the Bonferroni Correction
15.9 The Effect Size
15.10 The Calculation of the Analysis of Variance with Excel
15.11 Checkpoints
15.12 Applications
16 The Test for Correlation with Metric, Ordinal, and Nominal Data
16.1 The Test for a Correlation with Metric Data
16.1.1 The Test Situations for a Correlation with Metric Data
16.1.2 The Test Statistic and the Test Distribution
16.1.3 Example: Is There a Relationship Between Expenditure on Marketing and Expenditure on Innovation in Young Enterprises?
16.2 The Test for a Correlation with Ordinal Data
16.2.1 The Test Situations for a Correlation with Ordinal Data
16.2.2 The Test Statistic and the Test Distribution
16.2.3 Example: Is There a Relationship Between Self-assessment and Expectation Regarding the Economic Development of an Enterprise?
16.3 The Test for Correlation with Nominal Data
16.3.1 The Test Situations When Testing for a Correlation with Nominal Data
16.3.2 The Test of Independence for Nominal Variables with Two Characteristics
16.3.3 The Test of Independence for Nominal Variables with More Than Two Characteristics
16.4 Calculating Correlation Tests with Excel
16.5 Checkpoints
16.6 Applications
17 More Tests for Nominal Variables
17.1 The χ2-Test with One Sample: Does the Share of Female Founders Correspond to the Gender Share in Society?
17.2 The χ2-Test with Two Independent Samples: Are Start-Up Motives the Same for Service and Industrial Firms?
17.3 The χ2-Test with Two Dependent Samples: Is My Advertising Campaign Effective?
17.4 Calculating the Tests with Excel
17.5 Checkpoints
17.6 Applications
18 Summary Part IV: Overview of Test Procedures
Part V Regression Analysis
Regression Analysis: The Possibility to Predict What Will Happen
19 The Simple Linear Regression
19.1 Objectives of Regression Analysis
19.2 The Linear Regression Line and the Ordinary Least Squares Method
19.3 How Much do We Explain, the R2?
19.4 Calculating Simple Linear Regression with Excel
19.5 Is One Independent Variable Enough, Out-of-Sample Predictions, and Even More Warnings
19.6 Checkpoints
19.7 Applications
20 Multiple Regression Analysis
20.1 Multiple Regression Analysis: More than One Independent Variable
20.2 F-Test, t-Test and Adjusted-R2
20.3 Calculating the Multiple Regression with Excel
20.4 When Is the Ordinary Least Squares Estimate BLUE?
20.5 Checkpoints
20.6 Applications
Part VI What Happens Next?
21 How to Present Results
21.1 Contents of an Empirical Paper
21.2 Example I for a Report: Is There a Difference in Founding Age Between Male and Female Founders (Fictitious)
21.3 Example II for a Report: Professional Experience and Enterprise Performance (Fictitious)
21.4 Applications
22 Advanced Statistical Methods
23 Interesting and Advanced Statistical Textbooks
24 Another Data Set to Practice
A Solutions to the Applications
Chapter 1
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 19
Chapter 20
B The Standard Normal Distribution N (0,1)
C The t-Distribution
D The χ2-Distribution
E The F-Distribution
α=10%
α=5%
α=1%
