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Excel 2016 for Marketing Statistics: A Guide to Solving Practical Problems 1st ed. 2016 [Pehme köide]

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  • Formaat: Paperback / softback, 242 pages, kõrgus x laius: 235x155 mm, kaal: 4459 g, 167 Tables, color; 167 Illustrations, color; XVII, 242 p. 167 illus. in color., 1 Paperback / softback
  • Sari: Excel for Statistics
  • Ilmumisaeg: 10-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331943375X
  • ISBN-13: 9783319433752
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Excel 2016 for Marketing Statistics: A Guide to Solving Practical Problems 1st ed. 2016Suurem pilt
  • Formaat: Paperback / softback, 242 pages, kõrgus x laius: 235x155 mm, kaal: 4459 g, 167 Tables, color; 167 Illustrations, color; XVII, 242 p. 167 illus. in color., 1 Paperback / softback
  • Sari: Excel for Statistics
  • Ilmumisaeg: 10-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331943375X
  • ISBN-13: 9783319433752
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319433752.html
Märksõnad:

This is the first book to show the capabilities of Microsoft Excel in teaching marketing statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical marketing 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 marketing courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Marketing 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 marketing 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.

Arvustused

The monograph teaches Microsoft Excel application to the main statistical estimations needed to students and practitioners in their studies and research. the book is definitely useful for statistical evaluations of data in any area. The presented material is very convenient for teaching and studying main statistical tools and their applications via Excel. (Stan Lipovetsky, Technometrics, Vol. 59 (3), July, 2017)

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
3(9)
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)
Reference
20(1)
2 Random Number Generator
21(16)
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(5)
2.5 End-of-Chapter Practice Problems
34(3)
Reference
35(2)
3 Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing
37(28)
3.1 Confidence Interval About the Mean
37(10)
3.1.1 How to Estimate the Population Mean
37(1)
3.1.2 Estimating the Lower Limit and the Upper Limit of the 95% Confidence Interval About the Mean
38(1)
3.1.3 Estimating the Confidence Interval for the Chevy Impala in Miles Per Gallon
39(1)
3.1.4 Where Did the Number "1.96" Come From?
40(1)
3.1.5 Finding the Value for t in the Confidence Interval Formula
41(1)
3.1.6 Using Excel's TINV Function to Find the Confidence Interval About the Mean
42(1)
3.1.7 Using Excel to Find the 95% Confidence Interval for a Car's mpg Claim
42(5)
3.2 Hypothesis Testing
47(11)
3.2.1 Hypotheses Always Refer to the Population of People or Events That You Are Studying
48(1)
3.2.2 The Null Hypothesis and the Research (Alternative) Hypothesis
49(3)
3.2.3 The 7 Steps for Hypothesis-Testing Using the Confidence Interval About the Mean
52(6)
3.3 Alternative Ways to Summarize the Result of a Hypothesis Test
58(1)
3.3.1 Different Ways to Accept the Null Hypothesis
58(1)
3.3.2 Different Ways to Reject the Null Hypothesis
59(1)
3.4 End-of-Chapter Practice Problems
59(6)
References
64(1)
4 One-Group t-Test for the Mean
65(16)
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(7)
References
79(2)
5 Two-Group t-Test of the Difference of the Means for Independent Groups
81(26)
5.1 The 9 STEPS for Hypothesis-Testing Using the Two-Group t-Test
82(8)
5.1.1 STEP 1: Name One Group, Group 1, and the Other Group, Group 2
82(1)
5.1.2 STEP 2: Create a Table That Summarizes the Sample Size, Mean Score, and Standard Deviation of Each Group
82(2)
5.1.3 STEP 3: State the Null Hypothesis and the Research Hypothesis for the Two-Group t-Test
84(1)
5.1.4 STEP 4: Select the Appropriate Statistical Test
84(1)
5.1.5 STEP 5: Decide on a Decision Rule for the Two-Group t-Test
84(1)
5.1.6 STEP 6: Calculate the Formula for the Two-Group t-Test
84(1)
5.1.7 STEP 7: Find the Critical Value of t in the t-Table in Appendix E
85(1)
5.1.8 STEP 8: State the Result of Your Statistical Test
86(1)
5.1.9 STEP 9: State the Conclusion of Your Statistical Test in Plain English!
86(4)
5.2 Formula #1: Both Groups Have More Than 30 People in Them
90(7)
5.2.1 An Example of Formula #1 for the Two-Group t-Test
91(6)
5.3 Formula #2: One or Both Groups Have Less Than 30 People in Them
97(6)
5.4 End-of-Chapter Practice Problems
103(4)
References
106(1)
6 Correlation and Simple Linear Regression
107(44)
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(4)
6.3 Creating a Chart and Drawing the Regression Line onto the Chart
118(9)
6.3.1 Using Excel to Create a Chart and the Regression Line Through the Data Points
120(7)
6.4 Printing a Spreadsheet So That the Table and Chart Fit onto One Page
127(2)
6.5 Finding the Regression Equation
129(10)
6.5.1 Installing the Data Analysis ToolPak into Excel
130(3)
6.5.2 Using Excel to Find the SUMMARY OUTPUT of Regression
133(5)
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
138(1)
6.6 Adding the Regression Equation to the Chart
139(4)
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(6)
References
149(2)
7 Multiple Correlation and Multiple Regression
151(16)
7.1 Multiple Regression Equation
151(2)
7.2 Finding the Multiple Correlation and the Multiple Regression Equation
153(4)
7.3 Using the Regression Equation to Predict Annual Sales
157(1)
7.4 Using Excel to Create a Correlation Matrix in Multiple Regression
158(3)
7.5 End-of-Chapter Practice Problems
161(6)
References
166(1)
8 One-Way Analysis of Variance (ANOVA)
167(18)
8.1 Using Excel to Perform a One-Way Analysis of Variance (ANOVA)
169(2)
8.2 How to Interpret the ANOVA Table Correctly
171(1)
8.3 Using the Decision Rule for the ANOVA F-Test
171(1)
8.4 Testing the Difference Between Two Groups Using the ANOVA t-Test
172(5)
8.4.1 Comparing Dierberg's vs. Shop 'n Save in Their Prices Using the ANOVA t-Test
173(4)
8.5 End-of-Chapter Practice Problems
177(8)
References
184(1)
Appendices
185(56)
Appendix A Answers to End-of-Chapter Practice Problems
185(31)
Appendix B Practice Test
216(12)
Appendix C Answers to Practice Test
228(10)
Appendix D Statistical Formulas
238(2)
Appendix E t-Table
240(1)
Index 241
Prof. Tom Quirk spent six years in educational research at The American Institutes for Research and Educational Testing Service.  He is Professor of Marketing in the Walker School of Business & Technology at Webster University in St. Louis, Missouri (USA).  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 MBA from The University of Missouri-St. Louis.

Prof. Eric Rhiney is currently an Assistant Professor of Marketing in The Walker School of Business at Webster University in St. Louis, Missouri (US) where he teaches Research Design, Marketing Research and Marketing Strategies.  He holds a B.S.B.A. with an Emphasis in Marketing from University of Central Missouri, an M.B.A. with an Emphasis in Marketing from Webster University, and a Ph.D. in Marketing and International Business from St. Louis University.  He did marketing research professionally for over ten years engaging in research for companies such as Pizza Hut, Monsanto, Chrysler and Hardees.  He is involved in a number of quantitative research studies focused on in-group out-group orientation on consumer attitudes, digital marketing behavior, and cross-cultural marketing and has presented is work at a number of conferences including The American Marketing Association, the International Business Association, and the Marketing Management Association and the University of Missouri-St. Louis (UMSL) Digital Marketing Conference.