Data-driven business decisions /
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| Author / Creator: | Lloyd, Chris J. |
|---|---|
| Imprint: | Hoboken, N.J. : John Wiley & Sons, ©2011. |
| Description: | 1 online resource (1 volume) : illustrations. |
| Language: | English |
| Series: | Statistics in practice Statistics in practice. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/13604438 |
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| 100 | 1 | |a Lloyd, Chris J. |0 http://id.loc.gov/authorities/names/n98069359 | |
| 245 | 1 | 0 | |a Data-driven business decisions / |c Chris J. Lloyd. |
| 260 | |a Hoboken, N.J. : |b John Wiley & Sons, |c ©2011. | ||
| 300 | |a 1 online resource (1 volume) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Statistics in practice | |
| 500 | |a Includes link to CD content. | ||
| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from title screen (Books24x7, viewed Mar. 14, 2012). | |
| 505 | 0 | |a Machine generated contents note: ch. 1 How Are We Doing? Data-Driven Views of Business Performance -- 1.1.Setting Out Business Data -- 1.2.Different Kinds of Variables -- 1.3.The Idea of a Distribution -- 1.4.Typical Performance: The Sample Mean -- 1.5.Uncertainty in Performance: SD -- 1.6.Changing Units -- 1.7.Shapes of Distributions -- ch. 2 What Stands Out and Why? Who Wins? Data-Driven Views of Performance Dynamics -- 2.1.Different Layouts of Business Data -- 2.2.Comparing Performance across Different Segments -- 2.3.Complex Comparisons: Using Pivotables -- 2.4.Unusually High or Low Outcomes: z-Scores -- 2.5.Homogeneous Peer Groups -- 2.6.Combining Different Performance Measures -- ch. 3 Dealing with Uncertainty and Chance -- 3.1.Framing What Could Happen: Outcomes and Events -- 3.2.How Likely Is It? Probability Basics -- 3.3.Market Segments and Behavior; Probability Tables -- 3.4.Example in Health Care: Testing for a Disease -- 3.5.Conditional Probability | |
| 505 | 0 | |a Note continued: 3.6.How Strong Is the Relationship? Measuring Dependence -- 3.7.Probability Trees -- ch. 4 Let the Data Change Your Views: The Bayes Method -- 4.1.The Bayes Method in Pictures -- 4.2.The Bayes Method as an Algorithm -- 4.3.Example 1: A Simple Gambling Game -- 4.4.Example 2: Bayes in the Courtroom -- 4.5.Some Typical Business Applications -- ch. 5 Valuing an Uncertain Payoff -- 5.1.What Is a Probability Distribution? -- 5.2.Displaying a Probability Distribution -- 5.3.The Mean of a Distribution -- 5.4.Example: Fines and Violations -- 5.5.Why Use the Mean? -- 5.6.The Standard Deviation of a Distribution -- 5.7.Comparing Two Distributions -- 5.8.Conditional Distributions and Means -- ch. 6 Business Problems That Depend on Knowing "How Many" -- 6.1.The Binomial Distribution -- 6.2.The Mean and Standard Deviation -- 6.3.The Negative Binomial Distribution -- 6.4.The Poisson Distribution -- 6.5.Some Typical Business Applications | |
| 505 | 0 | |a Note continued: ch. 7 Business Problems That Depend on Knowing "How Much" -- 7.1.The Normal Distribution -- 7.2.Calculating Normal Probabilities in Excel -- 7.3.Combining Normal Variables -- 7.4.Comparing Two Normal Distributions -- 7.5.The Standard Normal Distribution -- 7.6.Example 3: Dealing with Uncertain Demand -- 7.7.Dealing with Proportional Variation -- ch. 8 Making Complex Decisions with Trees -- 8.1.Elements of Decision Trees -- 8.2.Solving the Decision Tree -- 8.3.Multistage Decision Trees -- 8.4.Valuing a Decision Option -- 8.5.The Cost of Uncertainty -- ch. 9 Data, Estimation, and Statistical Reliability -- 9.1.Describing the Past and the Future -- 9.2.How Were the Data Generated? -- 9.3.Law of Large Numbers -- 9.4.The Variability of the Sample Mean -- 9.5.The Standard Error of the Mean -- 9.6.The Normal Limit Theorem -- 9.7.Samples and Populations -- ch. 10 Managing Mean Performance -- 10.1.Benchmarking Mean Performance | |
| 505 | 0 | |a Note continued: 10.2.The Statistical Size of a Deviation -- 10.3.Decision Making, Hypothesis Testing, and p-Values -- 10.4.Confidence Intervals -- 10.5.One-Sided and Two-Sided Tests -- 10.6.Using StatproGo -- 10.7.Why Standard Deviation Matters -- 10.8.Assessing Detection Power -- ch. 11 Are These Customers Different? Did the Intervention Work? Looking at Changes in Mean Performance -- 11.1.How Variable Is a Difference? -- 11.2.Describing Changes in Mean Performance -- 11.3.Example 2: Is Product Placement Worth It? -- 11.4.Performing the f-Test with StatproGo -- 11.5.Different Standard Deviations -- 11.6.Analyzing Matched-Pairs Data -- ch. 12 What Is My Brand Recognition? Will It Sell? Analyzing Counts and Proportions -- 12.1.How Accurate Are Percentages? -- 12.2.Tests and Confidence Intervals for Proportions -- 12.3.Assessing Changes in Proportions -- 12.4.Using StatproGo -- 12.5.Alternative Methods -- ch. 13 Using the Relationship between Shares to Build a Portfolio | |
| 505 | 0 | |a Note continued: 13.1.How to Measure Financial Growth -- 13.2.Risk and Return: Both Matter -- 13.3.Correlation and Industry Structure -- 13.4.The Riskiness of a Portfolio -- 13.5.Balancing Risk and Return -- 13.6.Controlling Risk with TBs -- ch. 14 Investigating Relationships between Business Variables -- 14.1.Measuring Association with Correlation -- 14.2.Looking at Complex Relationships -- 14.3.Interpreting Correlations -- 14.4.What Is Autocorrelation? -- 14.5.Untangling Relationships with Partial Correlation -- ch. 15 Describing the Effect of a Business Input: Linear Regression -- 15.1.Linear Relationships -- 15.2.The Line of Best Fit -- 15.3.Computing the Least Squares Line -- 15.4.The Regression Model -- 15.5.How Reliable Is the Regression Line? -- ch. 16 The Reliability of Regression-Based Decisions -- 16.1.Three Kinds of Questions that Regression Answers -- 16.2.Estimating the Effect of a Change -- 16.3.Estimating the Trend Mean -- 16.4.Prediction | |
| 505 | 0 | |a Note continued: 16.5.Prediction Errors and What They Tell You -- ch. 17 Multicausal Relationships and Multiple Regression -- 17.1.Multilinear Relationships -- 17.2.Multiple Regression -- 17.3.Model Assessment -- 17.4.Prediction and Trend Estimation -- ch. 18 Product Features, Nonlinear Relationships, and Market Segments -- 18.1.Accounting for Yes-No Features -- 18.2.Quadratic Relationships -- 18.3.Quadratic Regression -- 18.4.Allowing for Segments and Groups -- 18.5.Automatic Model Selection -- ch. 19 Analyzing Data That Is Collected Regularly Over Time -- 19.1.Measuring Growth and Seasonality -- 19.2.How Is the Growth Rate Changing? -- 19.3.Seasonally Adjusting Data -- 19.4.Delayed Effects -- 19.5.Predicting the Future (Using Autoregression) -- ch. 20 Extending Regression Models: The Sky Is the Limit -- 20.1.Inputs That Have Varying Effects: Interactions -- 20.2.Inputs That Have Proportional Impacts -- 20.3.Case Study: How Effective Are Catalog Mail-Outs? | |
| 520 | |a A hands-on guide to the use of quantitative methods and software for making successful business decisions. The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel(r), uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Decision making |x Statistical methods. | |
| 650 | 0 | |a Problem solving |x Statistical methods. | |
| 650 | 0 | |a Statistical decision. |0 http://id.loc.gov/authorities/subjects/sh85127565 | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 6 | |a Prise de décision |x Méthodes statistiques. | |
| 650 | 6 | |a Résolution de problème |x Méthodes statistiques. | |
| 650 | 6 | |a Prise de décision (Statistique) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Decision making |x Statistical methods |2 fast | |
| 650 | 7 | |a Problem solving |x Statistical methods |2 fast | |
| 650 | 7 | |a Statistical decision |2 fast | |
| 650 | 7 | |a Management. |2 hilcc | |
| 650 | 7 | |a Business & Economics. |2 hilcc | |
| 650 | 7 | |a Management Theory. |2 hilcc | |
| 758 | |i has work: |a Data-driven business decisions (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGDWC3ypmFxGvVvQYymtDq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Lloyd, Chris J. |t Data-driven business decisions. |b [1st ed.]. |d Hoboken, N.J. : Wiley, ©2011 |z 9780470619605 |w (DLC) 2010046373 |w (OCoLC)678924561 |
| 830 | 0 | |a Statistics in practice. |0 http://id.loc.gov/authorities/names/n93024160 | |
| 856 | 4 | 0 | |u https://go.oreilly.com/uchicago/library/view/-/9780470619605/?ar |y O'Reilly |
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| 928 | |t Library of Congress classification |a HD30.23.L593 2011eb |l Online |c UC-FullText |u https://go.oreilly.com/uchicago/library/view/-/9780470619605/?ar |z O'Reilly |g ebooks |i 13747379 | ||