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## Excel 2010 for Social Science Statistics

### Excel 2010 for Social Science Statistics (2012-01-01) , January 01, 2012

## Excel 2010 for Physical Sciences Statistics

### Excel 2010 for Physical Sciences Statistics (2013-01-01) , January 01, 2013

## One-Group t-Test for the Mean

### Excel 2010 for Environmental Sciences Statistics (2015-01-01): 65-79 , January 01, 2015

This chapter explains how to use one of the most popular and most helpful statistical tests in environmental science research: the one-group t-test for the mean. This test compares the mean of your data set against the hypothesized population mean for your data to determine if the difference between these two values is “large enough” to be considered a “significant difference.” The formula is presented, explained, and a practical example is given using your calculator. Seven steps are outlined for using this formula to test specific hypotheses about your data. These steps build on the hypothesis-testing steps you learned in Chap. 3 of this book. You will also learn how to find the absolute value of a number, how to find the critical value of t in the table given in Appendix E of this book, and how to state both the result and conclusion of your statistical test. In addition, the difference between the 95 % confidence interval about the mean (Chap. 3) and the one-group t-test is 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.

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

### Excel 2013 for Business Statistics (2015-01-01): 87-114 , January 01, 2015

Up until now in this book, you have been dealing with the situation in which you have had only one group of events or objects in your research study and one measurement “number” on each of these. This chapter asks you to change gears and deal with the situation in which you are measuring two groups of instead of only one group. The nine steps for hypothesis-testing using the two-group t-test are presented, including the decision rule for either accepting or rejecting the null hypothesis for your data, and writing both the result and conclusion of your statistical test. Two formulas are presented in this chapter for this test. You need to use Formula #1 whenever both of your groups have more than 30 people or objects in them, and Formula #2 whenever one or both of your groups have less than 30 people or objects in them. The Excel steps are presented for both of these formulas using a practical example. 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.

## Correlation and Simple Linear Regression

### Excel 2007 for Educational and Psychological Statistics (2012-01-01): 107-145 , January 01, 2012

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 people (or objects) in your research study and only one measurement (i.e., variable) “number” on each of these people. 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 Chap. 9 of this book. An additional practice problem is presented in the Practice Test given in Chap. 10 along with its answer in Chap. 11 of this book.

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

### Excel 2016 for Health Services Management Statistics (2016-01-01): 37-67 , January 01, 2016

This chapter explains how to find the 95 % confidence interval about the mean for a set of data, and how to test hypotheses about your data using this confidence interval. You will learn how to estimate the population mean (average) for a group of events or objects at a 95 % confidence level so that you are 95 % confident that the population mean is between a lower limit of the data and an upper limit of the data. The formula for computing this confidence interval is presented, explained, and a sample problem is given using your calculator. You will then learn how to use Excel commands to determine the 95 % confidence interval about the mean using Excel’s TINV function. The second half of this chapter explains hypothesis testing and how you can test hypotheses about your data using Excel commands to find the 95 % confidence about the mean for your data. Seven steps are presented for this test, and you will be given specific explanations of how to write both the result and the conclusion of a hypothesis test. Alternative ways to summarize the result of a hypothesis test are also presented. 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.

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

### Excel 2013 for Educational and Psychological Statistics (2015-01-01): 35-65 , January 01, 2015

This chapter explains how to find the 95 % confidence interval about the mean for a set of data, and how to test hypotheses about your data using this confidence interval. You will learn how to estimate the population mean (average) for a group of people (or objects) at a 95 % confidence level so that you are 95 % confident that the population mean is between a lower limit of the data and an upper limit of the data. The formula for computing this confidence interval is presented, explained, and a sample problem is given using your calculator. You will then learn how to use Excel commands to determine the 95 % confidence interval about the mean using Excel’s TINV function. The second half of this chapter explains hypothesis testing and how you can test hypotheses about your data using Excel commands to find the 95 % confidence about the mean for your data. Seven steps are presented for this test, and you will be given specific explanations of how to write both the result and the conclusion of a hypothesis test. Alternative ways to summarize the result of a hypothesis test are also presented. 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 for each chapter is presented in the Practice Test given in Appendix B along with answers in Appendix C of this book.

## Excel 2016 for Human Resource Management Statistics

### Excel 2016 for Human Resource Management Statistics (2016-01-01) , January 01, 2016

## Correlation and Simple Linear Regression

### Excel 2010 for Business Statistics (2011-01-01): 109-151 , January 01, 2011

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 people (or objects) in your research study and only one measurement (i.e., variable) “number” on each of these people. 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 1. An additional practice problem is presented in the Practice Test given in Appendix 2 along with its answer in Appendix 3.

## Multiple Correlation and Multiple Regression

### Excel 2010 for Social Science Statistics (2012-01-01): 157-173 , January 01, 2012

There are many times in social science 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 weighted 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 social science 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.