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Über dieses Buch

This is the first book to show the capabilities of Microsoft Excel to teach physical sciences statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical science 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 science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2010 for Physical Sciences Statistics: A Guide to Solving Practical Problems is the first 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 science 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.

Includes 159 illustrations in color

Suitable for undergraduates or graduate students



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 used frequently and form the basis for additional statistical tests.
Thomas J. Quirk, Meghan Quirk, Howard Horton

Chapter 2. Random Number Generator

Salt marshes are coastal wetlands found on protected shorelines along the eastern seaboard of the USA where fresh water mixes with seawater. When ocean tides flood salt marshes, the plants living there must cope with the salt water. The “salinity” (i.e., the salt content of the water) depends on how close the marsh is to the ocean. Suppose that a biogeographer is studying the effects of salinity on vegetation in a salt marsh in Maine and that she has mapped the salt marsh into 32 separate geographic areas. Suppose, further, that she has asked you to take a random sample of 5 of these 32 areas within the salt marsh so that she can measure the percent of salinity level in each of these areas. Using your Excel skills to take this random sample, you will need to define a “sampling frame.”
Thomas J. Quirk, Meghan Quirk, Howard Horton

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

This chapter focuses on two ideas: (1) finding the 95 % confidence interval about the mean, and (2) hypothesis testing.
Thomas J. Quirk, Meghan Quirk, Howard Horton

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

In this chapter, you will learn how to use one of the most popular and most helpful statistical tests in science research: the one-group t-test for the mean.
Thomas J. Quirk, Meghan Quirk, Howard Horton

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

Up until now in this book, you have been dealing with the situation in which you have had only one group of people or events in your research study and only one measurement “number” on each of these people or events. We will now change gears and deal with the situation in which you are measuring two groups instead of only one group.
Thomas J. Quirk, Meghan Quirk, Howard Horton

Chapter 6. Correlation and Simple Linear Regression

Basically, a correlation is a number between ─ 1 and + 1 that summarizes the relationship between two variables, which we will call X and Y.
Thomas J. Quirk, Meghan Quirk, Howard Horton

Chapter 7. Multiple Correlation and Multiple Regression

There are many times in the physical sciences 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 in combination (e.g. X 1 , X 2 , X 3 , etc.) instead of a single predictor, X.
Thomas J. Quirk, Meghan Quirk, Howard Horton

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

So far in this 2010 Excel Guide, you have learned how to use a one-group t-test to compare the sample mean to the population mean, and a two-group t-test to test for the difference between two sample means. But what should you do when you have more than two groups and you want to determine if there is a significant difference between the means of these groups?
Thomas J. Quirk, Meghan Quirk, Howard Horton


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