Excel 2016 for Advertising Statistics: A Guide to Solving Practical Problems

This text is a step-by-step guide for students taking a first course in statistics for advertising and for advertising managers and practitioners who want to learn how to use Excel to solve practical statistics problems in in the workplace, whether or not they have taken a course in statistics.

Excel 2016 for Advertising Statistics explains statistical formulas and offers practical examples for how students can solve real-world advertising statistics problems. This book leaves detailed explanations of statistical theory to other statistics textbooks and focuses entirely on practical, real-world problem solving.

Each chapter briefly explains a topic and then demonstrates how to use Excel commands and formulas to solve specific advertising statistics problems.  This book gives practice in using Excel in two different ways:  (1) writing formulas (e.g., confidence interval about the mean, one-group t-test, two-group t-test, correlation) and (2) using Excel’s drop-down formula menus (e.g., simple linear regression, multiple correlation and multiple regression, and one-way ANOVA).  Three practice problems are provided at the end of each chapter, along with their solutions in an Appendix.  An additional Practice Test allows readers to test their understanding of each chapter by attempting to solve a specific practical advertising statistics problem using Excel; the solution to each of these problems is also given in an Appendix.

• Includes 167 illustrations in color
• Suitable for undergraduates or graduate students 

Table of Contents

Chapter 1: Sample Size, Mean, Standard Deviation, and Standard Error of the Mean
Chapter 2: Random Number Generator
Chapter 3: Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing
Chapter 4: One-Group t-Test for the Mean
Chapter 5: Two-Group t-Test of the Difference of the Means for Independent Groups
Chapter 6: Correlation and Simple Linear Regression
Chapter 7: Multiple Correlation and Multiple Regression
Chapter 8: One-Way Analysis of Variance (ANOVA)