Improving the user experience through practical data analytics : gain meaningful insight and increase your bottom line /

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This book shows you how to make UX design decisions based on data-not hunches. The authors recognize the enormous potential of user data that is collected as a natural by-product of routine UX research methods, including moderated usability tests, unmoderated usability tests, surveys, and contextual...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Fritz, Mike (Autor), Berger, Paul D., 1943- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Morgan Kaufmann, an imprint of Elsevier, �2015.
Temas:
Data mining.
Quantitative research.
Exploration de donn�ees (Informatique)
Recherche quantitative.
COMPUTERS > Database Management > Data Mining.
Quantitative research
Data mining
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: ch. 1 Introduction to a variety of useful statistical ideas and techniques
  • 1.1. Introduction
  • 1.2. Great Normal Curve in the Sky
  • 1.2.1. Finding Probabilities of Completion Times or Satisfaction Levels, or Anything Else, on a Normal Curve
  • 1.2.1.1. Vignette: how long does it take to hook up DSL Internet service?
  • 1.2.2. Finding Completion Times or Satisfaction Levels, or Anything Else, on a Normal Curve
  • 1.2.3. Probability Curve for the Mean of Many Results
  • 1.2.4. Central Limit Theorem
  • 1.3. Confidence Intervals
  • 1.3.1. Logic and Meaning of a Confidence Interval
  • 1.3.2. Finding a Confidence Interval Using Excel
  • 1.3.3. Finding a Confidence Interval Using SPSS
  • 1.4. Hypothesis Testing
  • 1.4.1. P-Value
  • 1.5. Summary
  • 1.6. Addendum: Activating "Data Analysis"
  • References
  • ch. 2 Comparing two designs (or anything else!) using independent sample T-tests
  • 2.1. Introduction
  • 2.2. Case Study: Comparing Designs at Mademoiselle La La
  • 2.3. Comparing Two Means
  • 2.4. Independent Samples
  • 2.5. Mademoiselle La La Redux
  • 2.5.1. Excel
  • 2.5.2. SPSS
  • 2.6. But What If We Conclude that the Means Aren't Different?
  • 2.7. Final Outcome at Mademoiselle La La
  • 2.8. Addendum: Confidence Intervals
  • 2.9. Summary
  • 2.10. Exercise
  • Reference
  • ch. 3 Comparing two designs (or anything else!) using paired sample T-tests
  • 3.1. Introduction
  • 3.2. Vignette: How Fast Can You Post a Job at Behemoth.com?
  • 3.3. Introduction to Paired Samples
  • 3.4. Example of Paired (Two-Sample) T-test
  • 3.4.1. Excel
  • 3.4.2. SPSS
  • 3.5. Behemoth.com Revisited
  • 3.6. Addendum: A Mini-Discussion Why the Independent and Paired Tests Need to be Different
  • 3.7. Summary
  • 3.8. Exercise
  • References
  • ch. 4 Pass or fail? Binomial-related hypothesis testing and confidence intervals using independent samples
  • 4.1. Introduction
  • 4.2. Case Study: Is Our Expensive New Search Engine at Behemoth.com Better Than What We Already Have?
  • 4.3. Hypothesis Testing Using the Chi-Square Test of Independence or Fisher's Exact Test
  • 4.3.1. Excel
  • 4.3.2. SPSS
  • 4.4. Meanwhile, Back at Behemoth.com
  • 4.5. Binomial Confidence Intervals and the Adjusted Wald Method
  • 4.6. Summary
  • 4.7. Addendum 1: How to Run the Chi-Square Test for Different Sample Sizes
  • 4.8. Addendum 2: Comparing More than Two Treatments
  • 4.8.1. Excel
  • 4.8.2. SPSS
  • 4.9. Appendix: Confidence Intervals for all Possible Sample-Proportion Outcomes from N = 1 to N = 15, in Table A.1
  • 4.10. Exercises
  • References
  • ch. 5 Pass or fail? Binomial-related hypothesis testing and confidence intervals using paired samples
  • 5.1. Introduction
  • 5.2. Case Study: Can I Register for a Course at Backboard.com?
  • 5.3. Hypothesis Testing Using the Cochran Q Test
  • 5.3.1. Excel
  • 5.3.2. SPSS
  • 5.4. Meanwhile, Back at Backboard
  • 5.5. Summary
  • 5.6. Exercise
  • References
  • ch. 6 Comparing more than two means: one factor ANOVA with independent samples. Multiple comparison testing with the Newman-Keuls test
  • 6.1. Introduction
  • 6.2. Case Study: Sophisticated for Whom?
  • 6.3. Independent Samples: One-Factor ANOVA
  • 6.4. Analyses
  • 6.4.1. Excel
  • 6.4.2. SPSS
  • 6.5. Multiple Comparison Testing
  • 6.6. Illustration of the S-N-K Test
  • 6.7. Application of the S-N-K to this Result
  • 6.8. Discussion of the Result
  • 6.8.1. Suppose That Your Only Software Available Is Excel
  • 6.9. Meanwhile, Back at Mademoiselle La La
  • 6.10. Summary
  • 6.11. Exercises
  • References
  • ch. 7 Comparing more than two means: one factor ANOVA with a within-subject design
  • 7.1. Introduction
  • 7.2. Case Study: Comparing Multiple Ease-of-Use Ratings at Mademoiselle La La
  • 7.3. Comparing Several Means with a Within-Subjects Design
  • 7.3.1. Key
  • 7.4. Hypotheses for Comparing Several Means
  • 7.5. SPSS Analysis
  • 7.6. Newman-Keuls Analysis
  • 7.7. Excel Analysis
  • 7.8. Mademoiselle La La: Let's Fix the Checkout ASAP!
  • 7.9. Summary
  • 7.10. Exercise
  • ch. 8 Comparing more than two means: two factor ANOVA with independent samples; the important role of interaction
  • 8.1. Introduction
  • 8.2. Case Study: Comparing Age and Gender at Mademoiselle La La
  • 8.3. Interaction
  • 8.3.1. Interaction
  • Definition 1
  • 8.3.2. Interaction
  • Definition 2
  • 8.4. Working the Example in SPSS
  • 8.5. Meanwhile, Back at Mademoiselle La La
  • 8.6. Summary
  • 8.7. Exercise
  • ch. 9 Can you relate? Correlation and simple linear regression
  • 9.1. Introduction
  • 9.2. Case Study: Do Recruiters Really Care about Boolean at Behemoth.com?
  • 9.3. Correlation Coefficient
  • 9.3.1. Excel
  • 9.3.2. SPSS
  • 9.3.3. CorrelationApplicationtoBehemoth.com
  • 9.4. Linear Regression
  • 9.4.1. Excel
  • 9.4.2. SPSS
  • 9.5. Linear Regression Analysis of Behemoth.com Data
  • 9.6. Meanwhile, Back at Behemoth
  • 9.7. Summary
  • 9.8. Addendum: A Quick Discussion of Some Assumptions Implicit in Interpreting the Results
  • 9.9. Exercise
  • ch. 10 Can you relate in multiple ways? Multiple linear regression and stepwise regression
  • 10.1. Introduction
  • 10.2. Case Study: Determining the Ideal Search Engine at Behemoth.com
  • 10.3. Multiple Regression
  • 10.3.1. Excel
  • 10.3.2. SPSS
  • 10.4. Confidence Interval for the Prediction
  • 10.5. BacktoBehemoth.com
  • 10.6. Stepwise Regression
  • 10.6.1. How Does Stepwise Regression Work?
  • 10.6.2. Stepwise Regression Analysis of the Behemoth.com Data
  • 10.7. Meanwhile, Back at Behemoth.com
  • 10.8. Summary
  • 10.9. Exercise
  • ch. 11 Will anybody buy? Logistic regression
  • 11.1. Introduction
  • 11.2. Case Study: Will Anybody Buy at the Charleston Globe?
  • 11.3. Logistic Regression
  • 11.4. Logistic Regression Using SPSS
  • 11.4.1. Computing a Predicted Probability
  • 11.4.2. Some Additional Useful Output to Request from SPSS
  • 11.4.2.1. Hosmer and Lemeshow goodness-of-fit test
  • 11.4.2.2. Finding the predicted probability of a "1" for each data point
  • 11.5. CharlestonGlobe.com Survey Data and its Analysis
  • 11.5.1. Stepwise Regression Analysis of the CharlestonGlobe.com Data
  • 11.5.2. Due Diligence Comparing Stepwise Results To Revised Binary Regression Results
  • 11.6. Implications of the Survey-Data Analysis Results
  • Back to CharlestonGlobe.com
  • 11.6.1. Results Are In: Showtime At CharlestonGlobe.com
  • 11.7. Summary
  • 11.8. Exercise.

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