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## Über dieses Buch

This book shows how Microsoft Excel is able to teach human resource management statistics effectively. Similar to the previously published Excel 2010 for Human Resource Management Statistics, it is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical human resource management problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.

Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in human resource management courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2013 for Human Resource Management Statistics: A Guide to Solving Practical Problems is the next book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work.

Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand human resource management problems. Practice problems are provided at the end of each chapter with their solutions in an Appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.

## Inhaltsverzeichnis

### Chapter 1. Sample Size, Mean, Standard Deviation, and Standard Error of the Mean

This chapter deals with how you can use Excel to find the average (i.e., “mean”) of a set of scores, the standard deviation of these scores (STDEV), and the standard error of the mean (s.e.) of these scores. All three of these statistics are basic to the study of statistics and are used frequently within many additional statistical tests. The formulas are presented, explained, and a practical example is given for each formula that shows how the formula can be applied using a calculator. Then, the steps needed to compute these formulas using Excel commands are explained so that you can practice using Excel to use these formulas correctly. You will also learn how to use Fill/Series commands, how to change the width of a column, how to center information in a group of cells, how to give a “name” to a group of cells, how to find the sample size for a data set, how to format numbers with a specific number of decimal places, how to save an Excel worksheet into your computer, and how to print out an Excel worksheet. Three practice problems are given at the end of the chapter to test your Excel skills, and the answers to these problems appear in Appendix A of this book. An additional practice problem is presented in the Practice Test given in Appendix B along with its answer in Appendix C of this book.
Thomas J. Quirk, Julie Palmer-Schuyler

### Chapter 2. Random Number Generator

This chapter explains how to use Excel to take a random sample of events or objects from a sampling frame that contains the list of events or objects from which you want to take a random sample. You will learn how to use Excel to create frame numbers for generating random numbers, why a set of duplicate frame numbers is important, and the Excel commands needed to sort the frame numbers into a random sequence. In addition, you will learn how to print out your result so that all of the information fits entirely onto a single page of paper. Three practice problems are given at the end of the chapter to test your Excel skills, and the answers to these problems appear in Appendix A of this book. An additional practice problem is presented in the Practice Test given in Appendix B along with its answer in Appendix C of this book.
Thomas J. Quirk, Julie Palmer-Schuyler

### Chapter 3. Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing

Thomas J. Quirk, Julie Palmer-Schuyler

### Chapter 4. One-Group t-Test for the Mean

Thomas J. Quirk, Julie Palmer-Schuyler

### Chapter 5. Two-Group t-Test of the Difference of the Means for Independent Groups

Thomas J. Quirk, Julie Palmer-Schuyler

### Chapter 6. Correlation and Simple Linear Regression

Up until now in this book, you have been dealing with the situation in which you have had only one group or two groups of events or objects in your research study and only one measurement (i.e., variable) “number” on each of these. This chapter asks you to change gears again and to deal with the situation in which you are measuring two variables instead of only one variable, and you are trying to discover the “relationship” between these variables. For example, if one variable increases in value, does the other variable increase in value (i.e., a “positive” relationship) or decrease in value (i.e., a negative relationship), and is this relationship “weak” or “strong?” The formula for the correlation r is presented, explained, and the nine steps for computing a correlation are explained using a calculator example. Then, the Excel commands for computing a correlation are presented along with the Excel steps needed to create a chart summarizing the relationship between the two variables. You will learn how to use Excel to draw the “best-fit line” through the data points on a scatterplot and how to determine the equation for this line so that you can use this equation to predict one variable from the other variable. You will learn both how to print a chart by itself, and how to print both the table and the chart so that they fit onto a single page. Three practice problems are given at the end of the chapter to test your Excel skills, and the answers to these problems appear in Appendix A of this book. An additional practice problem is presented in the Practice Test given in Appendix B along with its answer in Appendix C of this book.
Thomas J. Quirk, Julie Palmer-Schuyler

### Chapter 7. Multiple Correlation and Multiple Regression

There are many times in human resource management research when you want to predict a criterion, Y, but you want to find out if you can develop a better prediction model by using several predictors to predict Y instead of a single predictor as we discussed in Chap. 6 of this book. The resulting statistical procedure is called “multiple correlation” because it uses two or more predictors, each weighed differently in an equation, to predict Y. The job of multiple correlation is to determine if using several predictors can do a better job of predicting Y than any single predictor by itself. The equation for multiple correlation is presented, explained, and a practical human resource management problem is used to present the Excel commands needed to find the multiple correlation and the multiple regression equation generated from the data set. Excel commands are also used to create a SUMMARY OUTPUT which gives the coefficients needed to write the multiple regression equation for the data. Finally, the Excel commands needed to find the correlation between all of the variables is explained so that you can create a “correlation matrix” for your data set. You will learn how to read this correlation matrix to determine the correlation between any two variables in your study. Three practice problems are given at the end of the chapter to test your Excel skills, and the answers to these problems appear in Appendix A of this book. An additional practice problem is presented in the Practice Test given in Appendix B along with its answer in Appendix C of this book.
Thomas J. Quirk, Julie Palmer-Schuyler

### Chapter 8. One-Way Analysis of Variance (ANOVA)

So far in this book, you have learned how to test for the difference within one group of data between the mean of the group and the hypothesized population mean for the data using either the 95 % confidence interval about the mean (Chap. 3 of this book) or the one-group t-test of the mean (Chap. 4 of this book). You have also learned how to test for the difference between the means for two groups of to determine if this difference was a “significant” difference (Chap. 5 of this book). In this chapter, you will learn how to test for the difference between groups on a single variable when you have three or more groups of data. A practical human resource management problem is presented that walks you through the Excel steps needed to generate the output for a one-way ANOVA test. You will learn how to interpret the Summary Output table correctly, and how to test the hypotheses comparing the population means of the three or more groups to see if they are “significantly different from each other.” If this overall ANOVA test is produces a significant result, you will learn how to test the hypotheses comparing any two groups using an ANOVA t-test formula. This formula is presented, explained, and a practical human resource management example is used delineating the five steps needed to perform this test using a calculator. Then, the Excel steps for using this formula are presented and explained. Three practice problems are given at the end of the chapter to test your Excel skills, and the answers to these problems appear in Appendix A of this book. An additional practice problem is presented in the Practice Test given in Appendix B along with its answer in Appendix C of this book.
Thomas J. Quirk, Julie Palmer-Schuyler

### Backmatter

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