| Descript |
1 online resource (272 pages) |
| Content |
text txt |
| Media |
computer c |
| Carrier |
online resource cr |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Foreword by Alexis Fink -- Introduction -- 1. The Importance of Regression in People Analytics -- 1.1. Why is regression modeling so important in people analytics? -- 1.2. What do we mean by 'modeling' ? -- 1.2.1. The theory of inferential modeling -- 1.2.2. The process of inferential modeling -- 1.3. The structure, system and organization of this book -- 2. The Basics of the R Programming Language -- 2.1. What is R? -- 2.2. How to start using R -- 2.3. Data in R -- 2.3.1. Data types -- 2.3.2. Homogeneous data structures -- 2.3.3. Heterogeneous data structures -- 2.4. Working with dataframes -- 2.4.1. Loading and tidying data in dataframes -- 2.4.2. Manipulating dataframes -- 2.5. Functions, packages and libraries -- 2.5.1. Using functions -- 2.5.2. Help with functions -- 2.5.3. Writing your own functions -- 2.5.4. Installing packages -- 2.5.5. Using packages -- 2.5.6. The pipe operator -- 2.6. Errors, warnings and messages -- 2.7. Plotting and graphing -- 2.7.1. Plotting in base R -- 2.7.2. Specialist plotting and graphing packages -- 2.8. Documenting your work using R Markdown -- 2.9. Learning exercises -- 2.9.1. Discussion questions -- 2.9.2. Data exercises -- 3. Statistics Foundations -- 3.1. Elementary descriptive statistics of populations and samples -- 3.1.1. Mean, variance and standard deviation -- 3.1.2. Covariance and correlation -- 3.2. Distribution of random variables -- 3.2.1. Sampling of random variables -- 3.2.2. Standard errors, the t-distribution and confidence intervals -- 3.3. Hypothesis testing -- 3.3.1. Testing for a difference in means (Welch's t-test) -- 3.3.2. Testing for a non-zero correlation between two variables t-test for correlation). |
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3.3.3. Testing for a difference in frequency distribution between different categories in a data set (Chi-square test) -- 3.4. Foundational statistics in Python -- 3.5. Learning exercises -- 3.5.1. Discussion questions -- 3.5.2. Data exercises -- 4. Linear Regression for Continuous Outcomes -- 4.1. When to use it -- 4.1.1. Origins and intuition of linear regression -- 4.1.2. Use cases for linear regression -- 4.1.3. Walkthrough example -- 4.2. Simple linear regression -- 4.2.1. Linear relationship between a single input and an outcome -- 4.2.2. Minimising the error -- 4.2.3. Determining the best fit -- 4.2.4. Measuring the fit of the model -- 4.3. Multiple linear regression -- 4.3.1. Running a multiple linear regression model and interpreting its coefficients -- 4.3.2. Coefficient confidence -- 4.3.3. Model 'goodness-of-fit' -- 4.3.4. Making predictions from your model -- 4.4. Managing inputs in linear regression -- 4.4.1. Relevance of input variables -- 4.4.2. Sparseness ('missingness') of data -- 4.4.3. Transforming categorical inputs to dummy variables -- 4.5. Testing your model assumptions -- 4.5.1. Assumption of linearity and additivity -- 4.5.2. Assumption of constant error variance -- 4.5.3. Assumption of normally distributed errors -- 4.5.4. Avoiding high collinearity and multicollinearity between input variables -- 4.6. Extending multiple linear regression -- 4.6.1. Interactions between input variables -- 4.6.2. Quadratic and higher-order polynomial terms -- 4.7. Learning exercises -- 4.7.1. Discussion questions -- 4.7.2. Data exercises -- 5. Binomial Logistic Regression for Binary Outcomes -- 5.1. When to use it -- 5.1.1. Origins and intuition of binomial logistic regression -- 5.1.2. Use cases for binomial logistic regression -- 5.1.3. Walkthrough example -- 5.2. Modeling probabilistic outcomes using a logistic function. |
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5.2.1. Deriving the concept of log odds -- 5.2.2. Modeling the log odds and interpreting the coefficients -- 5.2.3. Odds versus probability -- 5.3. Running a multivariate binomial logistic regression model -- 5.3.1. Running and interpreting a multivariate binomial logistic regression model -- 5.3.2. Understanding the fit and goodness-of-fit of a binomial logistic regression model -- 5.3.3. Model parsimony -- 5.4. Other considerations in binomial logistic regression -- 5.5. Learning exercises -- 5.5.1. Discussion questions -- 5.5.2. Data exercises -- 6. Multinomial Logistic Regression for Nominal Category Outcomes -- 6.1. When to use it -- 6.1.1. Intuition for multinomial logistic regression -- 6.1.2. Use cases for multinomial logistic regression -- 6.1.3. Walkthrough example -- 6.2. Running stratified binomial models -- 6.2.1. Modeling the choice of Product A versus other products -- 6.2.2. Modeling other choices -- 6.3. Running a multinomial regression model -- 6.3.1. Defining a reference level and running the model -- 6.3.2. Interpreting the model -- 6.3.3. Changing the reference -- 6.4. Model simplification, fit and goodness-of-fit for multinomial logistic regression models -- 6.4.1. Gradual safe elimination of variables -- 6.4.2. Model fit and goodness-of-fit -- 6.5. Learning exercises -- 6.5.1. Discussion questions -- 6.5.2. Data exercises -- 7. Proportional Odds Logistic Regression for Ordered Category Outcomes -- 7.1. When to use it -- 7.1.1. Intuition for proportional odds logistic regression -- 7.1.2. Use cases for proportional odds logistic regression -- 7.1.3. Walkthrough example -- 7.2. Modeling ordinal outcomes under the assumption of proportional odds -- 7.2.1. Using a latent continuous outcome variable to derive a proportional odds model -- 7.2.2. Running a proportional odds logistic regression model. |
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7.2.3. Calculating the likelihood of an observation being in a specific ordinal category -- 7.2.4. Model diagnostics -- 7.3. Testing the proportional odds assumption -- 7.3.1. Sighting the coefficients of stratified binomial models -- 7.3.2. The Brant-Wald test -- 7.3.3. Alternatives to proportional odds models -- 7.4. Learning exercises -- 7.4.1. Discussion questions -- 7.4.2. Data exercises -- 8. Modeling Explicit and Latent Hierarchy in Data -- 8.1. Mixed models for explicit hierarchy in data -- 8.1.1. Fixed and random effects -- 8.1.2. Running a mixed model -- 8.2. Structural equation models for latent hierarchy in data -- 8.2.1. Running and assessing the measurement model -- 8.2.2. Running and interpreting the structural model -- 8.3. Learning exercises -- 8.3.1. Discussion questions -- 8.3.2. Data exercises -- 9. Survival Analysis for Modeling Singular Events Over Time -- 9.1. Tracking and illustrating survival rates over the study period -- 9.2. Cox proportional hazard regression models -- 9.2.1. Running a Cox proportional hazard regression model -- 9.2.2. Checking the proportional hazard assumption -- 9.3. Frailty models -- 9.4. Learning exercises -- 9.4.1. Discussion questions -- 9.4.2. Data exercises -- 10. Alternative Technical Approaches in R and Python -- 10.1. 'Tidier' modeling approaches in R -- 10.1.1. The broom package -- 10.1.2. The parsnip package -- 10.2. Inferential statistical modeling in Python -- 10.2.1. Ordinary Least Squares (OLS) linear regression -- 10.2.2. Binomial logistic regression -- 10.2.3. Multinomial logistic regression -- 10.2.4. Structural equation models -- 10.2.5. Survival analysis -- 10.2.6. Other model variants -- 11. Power Analysis to Estimate Required Sample Sizes for Modeling -- 11.1. Errors, effect sizes and statistical power -- 11.2. Power analysis for simple hypothesis tests. |
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11.3. Power analysis for linear regression models -- 11.4. Power analysis for log-likelihood regression models -- 11.5. Power analysis for hierarchical regression models -- 11.6. Power analysis using Python -- 12. Further Exercises for Practice -- 12.1. Analyzing graduate salaries -- 12.1.1. The graduates data set -- 12.1.2. Discussion questions -- 12.1.3. Data exercises -- 12.2. Analyzing a recruiting process -- 12.2.1. The recruiting data set -- 12.2.2. Discussion questions -- 12.2.3. Data exercises -- 12.3. Analyzing the drivers of performance ratings -- 12.3.1. The employee_performance data set -- 12.3.2. Discussion questions -- 12.3.3. Data exercises -- 12.4. Analyzing promotion differences between groups -- 12.4.1. The promotion data set -- 12.4.2. Discussion questions -- 12.4.3. Data exercises -- 12.5. Analyzing feedback on learning programs -- 12.5.1. The learning data set -- 12.5.2. Discussion questions -- 12.5.3. Data exercises -- References -- Glossary -- Index. |
| Note |
Unlimited number of concurrent users. UkHlHU |
| ISBN |
9781000427899 (electronic bk.) |
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9781032041742 |
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