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E-raamat: Excel 2016 for Physical Sciences Statistics: A Guide to Solving Practical Problems

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  • Formaat: PDF+DRM
  • Sari: Excel for Statistics
  • Ilmumisaeg: 25-Jul-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319400754
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Excel 2016 for Physical Sciences Statistics: A Guide to Solving Practical ProblemsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Excel for Statistics
  • Ilmumisaeg: 25-Jul-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319400754
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This book shows the capabilities of Microsoft Excel in teaching physical science statistics effectively. Similar to the previously published Excel 2013 for Physical Sciences Statistics, this book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical physical science problems. If understanding statistics isnt the readers strongest suit, the reader is not mathematically inclined, or if the reader is new to computers or to Excel, this is the book to start off with.





Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in physical science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Physical Sciences Statistics: A Guide to Solving Practical Problems capitalizes 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 physical 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.
1 Sample Size, Mean, Standard Deviation, and Standard Error of the Mean
1(20)
1.1 Mean
1(1)
1.2 Standard Deviation
2(1)
1.3 Standard Error of the Mean
3(1)
1.4 Sample Size, Mean, Standard Deviation, and Standard Error of the Mean
4(8)
1.4.1 Using the Fill/Series/Columns Commands
4(1)
1.4.2 Changing the Width of a Column
5(1)
1.4.3 Centering Information in a Range of Cells
6(2)
1.4.4 Naming a Range of Cells
8(1)
1.4.5 Finding the Sample Size Using the =COUNT Function
9(1)
1.4.6 Finding the Mean Score Using the =AVERAGE Function
9(1)
1.4.7 Finding the Standard Deviation Using the =STDEV Function
10(1)
1.4.8 Finding the Standard Error of the Mean
10(2)
1.5 Saving a Spreadsheet
12(1)
1.6 Printing a Spreadsheet
13(2)
1.7 Formatting Numbers in Currency Format (Two Decimal Places)
15(2)
1.8 Formatting Numbers in Number Format (Three Decimal Places)
17(1)
1.9 End-of-Chapter Practice Problems
17(4)
References
20(1)
2 Random Number Generator
21(14)
2.1 Creating Frame Numbers for Generating Random Numbers
21(4)
2.2 Creating Random Numbers in an Excel Worksheet
25(1)
2.3 Sorting Frame Numbers into a Random Sequence
26(3)
2.4 Printing an Excel File So That All of the Information Fits onto One Page
29(4)
2.5 End-of-Chapter Practice Problems
33(2)
3 Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing
35(30)
3.1 Confidence Interval About the Mean
35(11)
3.1.1 How to Estimate the Population Mean
35(1)
3.1.2 Estimating the Lower Limit and the Upper Limit of the 95% Confidence Interval About the Mean
36(1)
3.1.3 Estimating the Confidence Interval the Chevy Impala in Miles per Gallon
37(1)
3.1.4 Where Did the Number "1.96" Come From?
38(1)
3.1.5 Finding the Value for t in the Confidence Interval Formula
39(1)
3.1.6 Using Excel's TINV Function to Find the Confidence Interval About the Mean
40(1)
3.1.7 Using Excel to Find the 95% Confidence Interval for a Car's mpg Claim
40(6)
3.2 Hypothesis Testing
46(11)
3.2.1 Hypotheses Always Refer to the Population of Physical Properties That You Are Studying
47(1)
3.2.2 The Null Hypothesis and the Research (Alternative) Hypothesis
47(4)
3.2.3 The 7 Steps for Hypothesis-Testing Using the Confidence Interval About the Mean
51(6)
3.3 Alternative Ways to Summarize the Result of a Hypothesis Test
57(2)
3.3.1 Different Ways to Accept the Null Hypothesis
58(1)
3.3.2 Different Ways to Reject the Null Hypothesis
58(1)
3.4 End-of-Chapter Practice Problems
59(6)
References
63(2)
4 One-Group t-Test for the Mean
65(14)
4.1 The 7 STEPS for Hypothesis-Testing Using the One-Group t-Test
65(5)
4.1.1 STEP 1: State the Null Hypothesis and the Research Hypothesis
66(1)
4.1.2 STEP 2: Select the Appropriate Statistical Test
66(1)
4.1.3 STEP 3: Decide on a Decision Rule for the One-Group t-Test
66(1)
4.1.4 STEP 4: Calculate the Formula for the One-Group t-Test
67(1)
4.1.5 STEP 5: Find the Critical Value of t in the t-Table in Appendix E
68(1)
4.1.6 STEP 6: State the Result of Your Statistical Test
69(1)
4.1.7 STEP 7: State the Conclusion of Your Statistical Test in Plain English!
69(1)
4.2 One-Group t-Test for the Mean
70(4)
4.3 Can You Use Either the 95% Confidence Interval About the Mean OR the One-Group t-Test When Testing Hypotheses?
74(1)
4.4 End-of-Chapter Practice Problems
74(5)
References
78(1)
5 Two-Group t-Test of the Difference of the Means for Independent Groups
79(28)
5.1 The Nine STEPS for Hypothesis-Testing Using the Two-Group t-Test
80(9)
5.1.1 STEP 1: Name One Group, Group 1, and the Other Group, Group 2
80(1)
5.1.2 STEP 2: Create a Table That Summarizes the Sample Size, Mean Score, and Standard Deviation of Each Group
81(1)
5.1.3 STEP 3: State the Null Hypothesis and the Research Hypothesis for the Two-Group t-Test
82(1)
5.1.4 STEP 4: Select the Appropriate Statistical Test
82(1)
5.1.5 STEP 5: Decide on a Decision Rule for the Two-Group t-Test
82(1)
5.1.6 STEP 6: Calculate the Formula for the Two-Group t-Test
83(1)
5.1.7 STEP 7: Find the Critical Value of t in the t-Table in Appendix E
83(1)
5.1.8 STEP 8: State the Result of Your Statistical Test
84(1)
5.1.9 STEP 9: State the Conclusion of Your Statistical Test in Plain English!
84(5)
5.2 Formula #1: Both Groups Have a Sample Size Greater Than 30
89(8)
5.2.1 An Example of Formula #1 for the Two-Group t-Test
90(7)
5.3 Formula #2: One or Both Groups Have a Sample Size Less Than 30
97(6)
5.4 End-of-Chapter Practice Problems
103(4)
References
106(1)
6 Correlation and Simple Linear Regression
107(46)
6.1 What Is a "Correlation?"
107(7)
6.1.1 Understanding the Formula for Computing a Correlation
111(1)
6.1.2 Understanding the Nine Steps for Computing a Correlation, r
112(2)
6.2 Using Excel to Compute a Correlation Between Two Variables
114(5)
6.3 Creating a Chart and Drawing the Regression Line onto the Chart
119(10)
6.3.1 Using Excel to Create a Chart and the Regression Line Through the Data Points
121(8)
6.4 Printing a Spreadsheet So That the Table and Chart Fit onto One Page
129(2)
6.5 Finding the Regression Equation
131(10)
6.5.1 Installing the Data Analysis ToolPak into Excel
132(3)
6.5.2 Using Excel to Find the SUMMARY OUTPUT of Regression
135(5)
6.5.3 Finding the Equation for the Regression Line
140(1)
6.5.4 Using the Regression Line to Predict the y-Value for a Given x-Value
140(1)
6.6 Adding the Regression Equation to the Chart
141(3)
6.7 How to Recognize Negative Correlations in the SUMMARY OUTPUT Table
144(1)
6.8 Printing Only Part of a Spreadsheet Instead of the Entire Spreadsheet
144(2)
6.8.1 Printing Only the Table and the Chart on a Separate Page
145(1)
6.8.2 Printing Only the Chart on a Separate Page
145(1)
6.8.3 Printing Only the SUMMARY OUTPUT of the Regression Analysis on a Separate Page
146(1)
6.9 End-of-Chapter Practice Problems
146(7)
References
151(2)
7 Multiple Correlation and Multiple Regression
153(18)
7.1 Multiple Regression Equation
153(3)
7.2 Finding the Multiple Correlation and the Multiple Regression Equation
156(4)
7.3 Using the Regression Equation to Predict FROSH GPA
160(1)
7.4 Using Excel to Create a Correlation Matrix in Multiple Regression
160(4)
7.5 End-of-Chapter Practice Problems
164(7)
References
169(2)
8 One-Way Analysis of Variance (ANOVA)
171(18)
8.1 Using Excel to Perform a One-Way Analysis of Variance (ANOVA)
172(4)
8.2 How to Interpret the ANOVA Table Correctly
176(1)
8.3 Using the Decision Rule for the ANOVA F-Test
176(1)
8.4 Testing the Difference Between Two Groups Using the ANOVA t-Test
177(5)
8.4.1 Comparing Brand A vs. Brand C in Miles Driven Using the ANOVA t-Test
178(4)
8.5 End-of-Chapter Practice Problems
182(7)
References
187(2)
Appendices
189(56)
Appendix A Answers to End-of-Chapter Practice Problems
189(33)
Appendix B Practice Test
222(9)
Appendix C Answers to Practice Test
231(10)
Appendix D Statistical Formulas
241(2)
Appendix E t-Table
243(2)
Index 245
At the beginning of his academic career, Prof. Tom J. Quirk spent six years in educational research at The American Institutes for Research and Educational Testing Service.  He then taught Social Psychology, Educational Psychology, General Psychology, Marketing, Management, and Accounting at Principia College, and is currently a Professor of Marketing in the George Herbert Walker School of Business & Technology at Webster University based in St. Louis, Missouri (USA) where he teaches Marketing Statistics, Marketing Research, and Pricing Strategies.  He has written 60+ textbook supplements in Marketing and Management, published 20+ articles in professional journals, and presented 20+ papers at professional meetings.  He holds a B.S. in Mathematics from John Carroll University, both an M.A. in Education and a Ph.D. in Educational Psychology from Stanford University, and an M.B.A. from The University of Missouri-St. Louis.





Dr. Meghan H. Quirk holds both a Ph.D. in Biological Education and an M.A. in Biological Sciences from the University of Northern Colorado (UNC) and a B.A. in Biology and Religion at Principia College in Elsah, Illinois. She has done research on foodweb dynamics at Wind Cave National Park in South Dakota and research in agro-ecology in Southern Belize. She has co-authored an article on shortgrass steppe ecosystems in Photochemistry & Photobiology. She was a National Science Foundation Fellow GK-12, and currently teaches in Bailey, Colorado.





Howard F. Horton holds an MS in Biological Sciences from the University of Northern Colorado (UNC) and a BS in Biological Sciences from Mesa State College. He has worked on research projects in Pawnee National Grasslands, Rocky Mountain National Park, Long Term Ecological Research at Toolik Lake, Alaska, and Wind Cave, South Dakota. He has co-authored articles in Th e International Journal of Speleology and Th e Journal of Cave and Karst Studies. He was a National Science Foundation Fellow GK-12, and a District Wildlife Manager with the Colorado Division of Parks and Wildlife. He is currently the Angler Outreach Coordinator for Colorado Parks and Wildlife (USA).
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