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E-raamat: Excel 2013 for Physical Sciences Statistics: A Guide to Solving Practical Problems

  • Formaat: PDF+DRM
  • Sari: Excel for Statistics
  • Ilmumisaeg: 09-Mar-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319289649
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  • Formaat: PDF+DRM
  • Sari: Excel for Statistics
  • Ilmumisaeg: 09-Mar-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319289649
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This book shows the 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 2013 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 scien

ce 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.

Sample Size, Mean, Standard Deviation, and Standard Error of the Mean.- Random Number Generator.- Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing.- One-Group t-Test for the Mean.- Two-Group t-Test of the Difference of the Means for Independent Groups.- Correlation and Simple Linear Regression.- Multiple Correlation and Multiple Regression.- One-Way Analysis of Variance (ANOVA).- Appendix A: Answers to End-of-Chapter Practice Problems.- Appendix B: Practice Test.- Appendix C: Answers to Practice Test.- Appendix D: Statistical Formulas.- Appendix E: t-table.

At the beginning of his academic career, Prof. Thomas 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, and Social Science Research Methods 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.

Arvustused

This book is a well-written short book on the subject of statistical features of Excel 2013 with many examples and exercises related to the target audience. It is a great reference for those who need to use Excel for some introductory statistical concepts or as a supplement in an introductory course in statistics for nonmajors. (Morteza Marzjarani, Technometrics, Vol. 59 (2), April, 2017)

Excel 2013 for physical sciences statistics is an interesting and valuable little book. It would also be valuable to a manager who has not done any statistical work in some time and needs a review before dealing with subordinates who are using statistics to justify their work or recommend decisions. Although the title says it is for physical sciences, it is equally applicable to other areas where statistics are used. (Michael Moorman, Computing Reviews, computingreviews.com, August, 2016)

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(3)
2.2 Creating Random Numbers in an Excel Worksheet
24(2)
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(28)
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(4)
References
62(1)
4 One-Group t-Test for the Mean
63(14)
4.1 The 7 STEPS for Hypothesis-Testing Using the One-Group t-Test
63(5)
4.1.1 STEP 1: State the Null Hypothesis and the Research Hypothesis
64(1)
4.1.2 STEP 2: Select the Appropriate Statistical Test
64(1)
4.1.3 STEP 3: Decide on a Decision Rule for the One-Group t-Test
64(1)
4.1.4 STEP 4: Calculate the Formula for the One-Group t-Test
65(1)
4.1.5 STEP 5: Find the Critical Value of t in the t-Table in Appendix E
66(1)
4.1.6 STEP 6: State the Result of Your Statistical Test
67(1)
4.1.7 STEP 7: State the Conclusion of Your Statistical Test in Plain English!
67(1)
4.2 One-Group t-Test for the Mean
68(4)
4.3 Can You Use Either the 95 % Confidence Interval About the Mean OR the One-Group t-Test When Testing Hypotheses?
72(1)
4.4 End-of-Chapter Practice Problems
72(5)
References
76(1)
5 Two-Group t-Test of the Difference of the Means for Independent Groups
77(26)
5.1 The 9 STEPS for Hypothesis-Testing Using the Two-Group t-Test
78(8)
5.1.1 STEP 1: Name One Group, Group 1, and the Other Group, Group 2
78(1)
5.1.2 STEP 2: Create a Table That Summarizes the Sample Size, Mean Score, and Standard Deviation of Each Group
79(1)
5.1.3 STEP 3: State the Null Hypothesis and the Research Hypothesis for the Two-Group t-Test
80(1)
5.1.4 STEP 4: Select the Appropriate Statistical Test
80(1)
5.1.5 STEP 5: Decide on a Decision Rule for the Two-Group t-Test
80(1)
5.1.6 STEP 6: Calculate the Formula for the Two-Group t-Test
81(1)
5.1.7 STEP 7: Find the Critical Value of t in the t-Table in Appendix E
81(1)
5.1.8 STEP 8: State the Result of Your Statistical Test
82(1)
5.1.9 STEP 9: State the Conclusion of Your Statistical Test in Plain English!
82(4)
5.2 Formula #1: Both Groups Have a Sample Size Greater Than 30
86(7)
5.2.1 An Example of Formula #1 for the Two-Group t-Test
88(5)
5.3 Formula #2: One or Both Groups Have a Sample Size Less Than 30
93(7)
5.4 End-of-Chapter Practice Problems
100(3)
References
102(1)
6 Correlation and Simple Linear Regression
103(46)
6.1 What Is a "Correlation?"
103(7)
6.1.1 Understanding the Formula for Computing a Correlation
107(1)
6.1.2 Understanding the Nine Steps for Computing a Correlation, r
108(2)
6.2 Using Excel to Compute a Correlation Between Two Variables
110(5)
6.3 Creating a Chart and Drawing the Regression Line onto the Chart
115(10)
6.3.1 Using Excel to Create a Chart and the Regression Line Through the Data Points
117(8)
6.4 Printing a Spreadsheet So That the Table and Chart Fit onto One Page
125(2)
6.5 Finding the Regression Equation
127(9)
6.5.1 Installing the Data Analysis ToolPak into Excel
128(3)
6.5.2 Using Excel to Find the SUMMARY OUTPUT of Regression
131(4)
6.5.3 Finding the Equation for the Regression Line
135(1)
6.5.4 Using the Regression Line to Predict the y-Value for a Given x-Value
135(1)
6.6 Adding the Regression Equation to the Chart
136(3)
6.7 How to Recognize Negative Correlations in the SUMMARY OUTPUT Table
139(1)
6.8 Printing Only Part of a Spreadsheet Instead of the Entire Spreadsheet
139(2)
6.8.1 Printing Only the Table and the Chart on a Separate Page
140(1)
6.8.2 Printing Only the Chart on a Separate Page
140(1)
6.8.3 Printing Only the SUMMARY OUTPUT of the Regression Analysis on a Separate Page
141(1)
6.9 End-of-Chapter Practice Problems
141(8)
References
146(3)
7 Multiple Correlation and Multiple Regression
149(18)
7.1 Multiple Regression Equation
149(3)
7.2 Finding the Multiple Correlation and the Multiple Regression Equation
152(4)
7.3 Using the Regression Equation to Predict FROSH GPA
156(1)
7.4 Using Excel to Create a Correlation Matrix in Multiple Regression
157(3)
7.5 End-of-Chapter Practice Problems
160(7)
References
165(2)
8 One-Way Analysis of Variance (ANOVA)
167(18)
8.1 Using Excel to Perform a One-Way Analysis of Variance (ANOVA)
168(4)
8.2 How to Interpret the ANOVA Table Correctly
172(1)
8.3 Using the Decision Rule for the ANOVA F-test
172(1)
8.4 Testing the Difference Between Two Groups Using the ANOVA t-Test
173(5)
8.4.1 Comparing Brand A vs. Brand C in Miles Driven Using the ANOVA t-Test
174(4)
8.5 End-of-Chapter Practice Problems
178(7)
References
183(2)
Appendices
185(56)
Appendix A Answers to End-of-Chapter Practice Problems
185(33)
Appendix B Practice Test
218(10)
Appendix C Answers to Practice Test
228(10)
Appendix D Statistical Formulas
238(2)
Appendix E t-Table
240(1)
Index 241
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 The International Journal of Speleology and The 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).