Statistics Applied With Excel: Data Analysis Is (Not) an Art

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%