Atjaunināt sīkdatņu piekrišanu

E-grāmata: Excel 2013 for Engineering Statistics: A Guide to Solving Practical Problems

  • Formāts: PDF+DRM
  • Sērija : Excel for Statistics
  • Izdošanas datums: 15-Oct-2015
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319235554
  • Formāts - PDF+DRM
  • Cena: 53,52 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: PDF+DRM
  • Sērija : Excel for Statistics
  • Izdošanas datums: 15-Oct-2015
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319235554

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This is the first book to show the capabilities of Microsoft Excel to teach engineering statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical engineering 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 engineering courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2013 for Engineering 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 engineering 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.
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(4)
2.4 Printing an Excel File So That All of the Information Fits onto One Page
30(3)
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(12)
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 for 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
41(6)
3.2 Hypothesis Testing
47(10)
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
48(3)
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
75(4)
References
78(1)
5 Two-Group t-Test of the Difference of the Means for Independent Groups
79(28)
5.1 The 9 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
105(2)
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
112(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
120(9)
6.4 Printing a Spreadsheet So That the Table and Chart Fit onto One Page
129(2)
6.5 Finding the Regression Equation
131(9)
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(3)
6.5.3 Finding the Equation for the Regression Line
138(1)
6.5.4 Using the Regression Line to Predict the y-Value for a Given x-Value
139(1)
6.6 Adding the Regression Equation to the Chart
140(3)
6.7 How to Recognize Negative Correlations in the SUMMARY OUTPUT Table
143(1)
6.8 Printing Only Part of a Spreadsheet Instead of the Entire Spreadsheet
143(2)
6.8.1 Printing Only the Table and the Chart on a Separate Page
144(1)
6.8.2 Printing Only the Chart on a Separate Page
144(1)
6.8.3 Printing Only the SUMMARY OUTPUT of the Regression Analysis on a Separate Page
145(1)
6.9 End-of-Chapter Practice Problems
145(8)
References
150(3)
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
161(3)
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)
173(3)
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(58)
Appendix A Answers to End-of-Chapter Practice Problems
189(34)
Appendix B Practice Test
223(10)
Appendix C Answers to Practice Test
233(11)
Appendix D Statistical Formulas
244(2)
Appendix E t-Table
246(1)
Index 247
At the beginning of his academic career, Prof. 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.