Interpreting and visualizing regression models using Stata:
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| Main Author: | |
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| Format: | Book |
| Language: | English |
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College Station, Texas
Stata Press
2021
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| Edition: | Second edition |
| Series: | A Stata Press publication
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxx, 610 Seiten Illustrationen, Diagramme |
| ISBN: | 9781597183215 |
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MARC
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| 100 | 1 | |a Mitchell, Michael N. |e Verfasser |0 (DE-588)139115366 |4 aut | |
| 245 | 1 | 0 | |a Interpreting and visualizing regression models using Stata |c Michael N. Mitchell |
| 250 | |a Second edition | ||
| 264 | 1 | |a College Station, Texas |b Stata Press |c 2021 | |
| 300 | |a xxx, 610 Seiten |b Illustrationen, Diagramme | ||
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Contents List of tables xv List of figures xvii Preface to the Second Edition 1 xxvii Preface to the First Edition xxix Acknowledgments xxxi Introduction 1 1.1 Read me first. 1 1.2 The GSS dataset. 4 1.2.1 Income. 5 1.2.2 Age. 6 1.2.3 Education. 10 1.2.4 Gender. 12 1.3 The pain datasets. 12 1.4 The optimism datasets. 13 1.5 The school datasets. 13 1.6 The sleep datasets. 13 1.7 Overview of the book. 13 I Continuous predictors 17 2 Continuous predictors: Linear 19 2.1 Chapter overview. 19 2.2 Simple linear regression
. 19 2.2.1 Computing predicted means using the margins command . . 22 2.2.2 Graphing predicted means using the marginsplot command 24 2.3 Multiple regression. 27
Contents vi 2.4 2.5 2.6 3 Computing adjusted means using the margins command . . 28 2.3.2 Some technical details about adjusted means. 30 2.3.3 Graphing adjusted means using the marginsplot command . 31 Checking for nonlinearity graphically. 32 2.4.1 Using scatterplots to check for nonlinearity . 33 2.4.2 Checking for nonlinearity using residuals. 33 2.4.3 Checking for nonlinearity using locally weighted smoother . 35 2.4.4 Graphing outcome mean at each level of predictor. 36 2.4.5 Summary . 39 Checking for nonlinearity analytically. 39 2.5.1 Adding power terms . 40 2.5.2 Using factor variables. 42 Summary. 46 Continuous predictors: Polynomials 49 3.1 Chapter overview. 49 3.2 Quadratic (squared) terms. 49 3.2.1 Overview. 49 3.2.2 Examples . 53
Cubic (third power) terms. 59 3.3.1 Overview. 59 3.3.2 Examples . gQ 3.3 3.4 4 2.3.1 Fractional polynomial regression . gg 3.4.1 Overview. gg 3.4.2 Example using fractional polynomial regression. 70 3.5 Main effects with polynomial terms . . 3.6 Summary. Continuous predictors: Piecewise models 4.1 Chapter overview. 4.2 Introduction to piecewise regression models 4.3 Piecewise with one known knot
Contents 4.4 vii 4.3.1 Overview. 86 4.3.2 Examples using the GSS. 87 . 95 4.4.1 Overview. 95 4.4.2 Examples using the GSS. 95 Piecewise with one knot and one jump. 100 4.5.1 Overview. 100 4.5.2 Examples using the GSS. 101 Piecewise with two knots and two jumps. 106 4.6.1 Overview. 106 4.6.2 Examples using the GSS. 106 4.7 Piecewise with an unknown knot. 113 4.8 Piecewise model with multiple unknown knots. 117 4.9 Piecewise models and the marginsplot command . 124 . 127 Summary. 130 4.5 4.6 Piecewise with two known knots 4.10 Automating graphs of piecewise models 4.11 5 Continuous by
continuous interactions 131 5.1 Chapter overview. 131 5.2 Linearby linear interactions. 131 5.2.1 Overview. 131 5.2.2 Example usingGSS data. 136 5.2.3 Interpreting the interaction in terms of age. 137 5.2.4 Interpreting the interaction in terms of education. 139 5.2.5 Interpreting the interaction in terms of ageslope. 141 5.2.6 Interpreting the interaction in terms of the educ slope . . . 142 Linear by quadraticinteractions. 144 5.3.1 Overview. 144 5.3.2 Example using GSSdata. 147 Summary. 152 5.3 5.4
Contents viii 6 Continuous by continuous by continuous interactions 6.1 Chapter overview. 6.2 Overview 6.3 Examples using the GSS data. 6.4 . 15 153 1 54 6.3.1 A model without a three-way interaction. 158 6.3.2 A three-way interaction model. 1θ2 Summary. II Categorical predictors 171 7 Categorical predictors 173 7.1 Chapter overview. 173 7.2 Comparing two groups using a t test. 174 7.3 More groups and more predictors. 175 7.4 Overview of contrast operators. 181 7.5 Compare each group against a reference group. 182 7.5.1 Selecting a specific contrast. 183 7.5.2 Selecting a different referencegroup. 184 7.5.3 Selecting a contrast and referencegroup. 185 Compare each group against the grand mean . 185 Selecting a
specific contrast. 187 Compare adjacent means. 188 7.7.1 Reverse adjacent contrasts. 192 7.7.2 Selecting a specific contrast. 193 Comparing the mean of subsequent or previous levels. 194 7.8.1 Comparing the mean of previous levels. 198 7.8.2 Selecting a specific contrast. 199 7.9 Polynomial contrasts. 2qq 7.10 Custom contrasts. շցշ 7.11 Weighted contrasts. 2θθ 7.12 Pairwise comparisons. 2Qg 7.6 7.6.1 7.7 7.8
ix Contents 8 7.13 Interpreting confidence intervals. 210 7.14 Testing categorical variables using regression. 212 7.15 Summary. 215 Categorical by categorical interactions 217 8.1 Chapter overview. 217 8.2 Two by two models: Example 1. 219 8.2.1 Simple effects. 224 8.2.2 Estimating the sizeof the interaction. 225 8.2.3 More about interaction. 226 8.2.4 Summary . 227 Two by three models. 227 8.3.1 Example 2. 227 8.3.2 Example 3. 232 8.3.3 Summary . 237 Three by three models: Example 4. 237 8.4.1 Simple effects. 240 8.4.2
Simple contrasts. 240 8.4.3 Partial interaction . 242 8.4.4 Interaction contrasts. 243 8.4.5 Summary . 245 8.5 Unbalanced designs. 245 8.6 Main effects with interactions: anova versus regress. 250 8.7 Interpreting confidence intervals. 253 8.8 Summary. 255 8.3 8.4 9 Categorical by categorical by categorical interactions 257 9.1 Chapter overview. 257 9.2 Two by two by two models. 258 9.2.1 Simple interactions byseason. 260 9.2.2 Simple interactions by depression status. 261 9.2.3 Simple effects. 263
Contents x 9.3 9.4 9.5 Two by two by three models. 2®^ 9.3.1 Simple interactions by depression status. 9.3.2 Simple partial interaction by depression status. 266 9.3.3 Simple contrasts. 9.3.4 Partial interactions. 268 Three by three by three models and beyond. 270 9.4.1 Partial interactions and interaction contrasts 9.4.2 Simple interactions. 276 9.4.3 Simple effects and simple comparisons . 279 Summary. 28θ . 272 III Continuous and categorical predictors 10 11 266 281 Linear by categorical interactions 283 10.1 Chapter overview. 283 10.2 Linear and two-level categorical: No interaction. 283 10.2.1 Overview. 283 10.2.2 Examples using the GSS. 286 10.3 Linear by two-level categorical interactions. 291 10.3.1
Overview. 291 10.3.2 Examples using the GSS. 294 10.4 Linear by three-level categorical interactions. 299 10.4.1 Overview. 299 10.4.2 Examples using the GSS. 301 10.5 Summary. 308 Polynomial by categorical interactions 311 11.1 Chapter overview. 311 11.2 Quadratic by categorical interactions. 311 11.2.1 Overview. 312 11.2.2 Quadratic by two-level categorical. 315 11.2.3 Quadratic by three-level categorical. 323
Contents 12 xi 11.3 Cubic by categorical interactions . 329 11.4 Summary. 334 335 Piecewise by categorical interactions 12.1 Chapter overview. 335 12.2 One knot and one jump. 338 12.2.1 Comparing slopes across gender. 342 12.2.2 Comparing slopes across education. 343 12.2.3 Difference in differences of slopes. 343 12.2.4 Comparing changes in intercepts . 344 12.2.5 Computing and comparing adjusted means. 344 12.2.6 Graphing adjusted means . 347 Two knots and two jumps. 351 Comparing slopes across gender. 356 12.3.2 Comparing slopes across education. 357 12.3 12.3.1 12.3.3 Difference in differences of slopes. 358 12.3.4 Comparing changes in intercepts by gender . 359 12.3.5 Comparing changes in intercepts by education. 360 12.3.6 Computing and comparing adjusted
means. 12.3.7 12.4 Graphing adjusted means Comparing coding schemes . 361 364 . 366 12.4.1 Coding scheme #1. 12.4.2 Coding scheme #2. 368 12.4.3 Coding scheme #3. 367 370 12.4.4 Coding scheme #4. 372 12.4.5 13 Choosingcoding schemes. 373 12.5 Summary. 374 Continuous by continuous by categorical interactions 375 13.1 Chapter overview. 375 13.2 Linear by linear bycategorical interactions. 376 13.2.1 Fitting separate models for males and females. 376
Contents xii 13.2.2 Fitting a combined model for males and females. 378 13.2.3 Interpreting the interaction focusing in the age slope . 380 13.2.4 Interpreting the interaction focusing on the educ slope . . . 382 13.2.5 Estimating and comparing adjusted means by gender . 384 13.3 Linear by quadratic by categorical interactions . 386 13.3.1 Fitting separate models for males and females. 386 13.3.2 Fitting a common model for males and females. 388 13.3.3 Interpreting theinteraction . 389 13.3.4 Estimating and comparing adjusted means by gender . 390 13.4 Summary. 392 14 Continuous by categorical bycategorical interactions 393 14.1 Chapter overview. 393 14.2 Simple effects of gender on the age slope. 398 14.3 Simple effects of education on the age slope. 399 14.4 Simple contrasts on education for the age slope. 400 14.5 Partial interaction on education for the age slope. 400 14.6 Summary. 401 IV Beyond ordinary linear regression 403 15 Multilevel models 405 15.1 405 Chapter
overview. 15.2 Example 1: Continuous by continuous interaction.406 16 15.3 Example 2: Continuous by categorical interaction. 409 15.4 Example 3: Categorical by continuous interaction. 413 15.5 Example 4: Categorical by categorical interaction. 417 15.6 Summary. 4շլ Time as a continuous predictor 423 16.1 Chapter overview. 42$ 16.2 Example 1: Linear effect of time. 424 16.3 Example 2: Linear effect of time by a categorical predictor. 428
Contents 16.4 Example 3: Piecewise modeling of time. 433 16.5 Example 4: Piecewise effects of time by a categorical predictor . . . 16.6 17 xiii 438 16.5.1 Baseline slopes 16.5.2 Change in slopes: Treatment versus baseline. 444 16.5.3 Jump at treatment. 16.5.4 Comparisons among groups. 446 . Summary. 443 445 448 Time as a categorical predictor 449 17.1 Chapter overview. 449 17.2 Example 1: Time treated as a categorical variable. 450 17.3 Example 2: Time (categorical) by two groups. 455 17.4 Example 3: Time (categorical) by three groups. 459 17.5 Comparing models with different residual covariancestructures . . . 464 17.6 Analyses with small samples. 466 17.7 Summary. 474 18 Nonlinear models 475 18.1 Chapter overview. 475 18.2 Binary logistic
regression. 476 18.2.1 A logistic model with one categorical predictor 18.2.2 A logistic model with one continuous predictor. 484 18.2.3 A logistic model with covariates. 486 18.3 Multinomial logistic regression . 476 . 491 18.4 Ordinal logistic regression. 497 18.5 Poisson regression. 500 18.6 More applications of nonlinear models.503 19 18.6.1 Categorical by categorical interaction. 18.6.2 Categorical by continuous interaction.510 18.6.3 Piecewise modeling. 516 503 18.7 Summary. 522 Complex survey data 523
Contents xiv V Appendices A Customizing output from estimation commands В 531 A.l Omission of output. A.2 Specifying the confidence level. 533 A.3 Customizing the formatting of columns in the coefficient table . 534 A.4 Customizing the display of factor variables. 536 The margins command 545 B.l The predict() and expression() options. 545 B.2 The at() option. 548 B.3 Margins with factorvariables. 551 B.4 Margins with factorvariables and the at() option.557 B.5 The dydx() and related options. 559 B.6 Specifying the confidence level. 563 B.7 Customizing column formatting. 564 C The marginsplot command 567 D The contrast command 583 D.l Inclusion and omission of output. 584 D.2 Customizing the display of factor variables. 586 D.3 Adjustments for multiple comparisons. 588 D.4 Specifying the confidence
level. 588 D.5 Customizing column formatting. 589 E The pwcompare command 59լ References 597 Author index gø! Subject index |
| adam_txt |
Contents List of tables xv List of figures xvii Preface to the Second Edition 1 xxvii Preface to the First Edition xxix Acknowledgments xxxi Introduction 1 1.1 Read me first. 1 1.2 The GSS dataset. 4 1.2.1 Income. 5 1.2.2 Age. 6 1.2.3 Education. 10 1.2.4 Gender. 12 1.3 The pain datasets. 12 1.4 The optimism datasets. 13 1.5 The school datasets. 13 1.6 The sleep datasets. 13 1.7 Overview of the book. 13 I Continuous predictors 17 2 Continuous predictors: Linear 19 2.1 Chapter overview. 19 2.2 Simple linear regression
. 19 2.2.1 Computing predicted means using the margins command . . 22 2.2.2 Graphing predicted means using the marginsplot command 24 2.3 Multiple regression. 27
Contents vi 2.4 2.5 2.6 3 Computing adjusted means using the margins command . . 28 2.3.2 Some technical details about adjusted means. 30 2.3.3 Graphing adjusted means using the marginsplot command . 31 Checking for nonlinearity graphically. 32 2.4.1 Using scatterplots to check for nonlinearity . 33 2.4.2 Checking for nonlinearity using residuals. 33 2.4.3 Checking for nonlinearity using locally weighted smoother . 35 2.4.4 Graphing outcome mean at each level of predictor. 36 2.4.5 Summary . 39 Checking for nonlinearity analytically. 39 2.5.1 Adding power terms . 40 2.5.2 Using factor variables. 42 Summary. 46 Continuous predictors: Polynomials 49 3.1 Chapter overview. 49 3.2 Quadratic (squared) terms. 49 3.2.1 Overview. 49 3.2.2 Examples . 53
Cubic (third power) terms. 59 3.3.1 Overview. 59 3.3.2 Examples . gQ 3.3 3.4 4 2.3.1 Fractional polynomial regression . gg 3.4.1 Overview. gg 3.4.2 Example using fractional polynomial regression. 70 3.5 Main effects with polynomial terms . . 3.6 Summary. Continuous predictors: Piecewise models 4.1 Chapter overview. 4.2 Introduction to piecewise regression models 4.3 Piecewise with one known knot
Contents 4.4 vii 4.3.1 Overview. 86 4.3.2 Examples using the GSS. 87 . 95 4.4.1 Overview. 95 4.4.2 Examples using the GSS. 95 Piecewise with one knot and one jump. 100 4.5.1 Overview. 100 4.5.2 Examples using the GSS. 101 Piecewise with two knots and two jumps. 106 4.6.1 Overview. 106 4.6.2 Examples using the GSS. 106 4.7 Piecewise with an unknown knot. 113 4.8 Piecewise model with multiple unknown knots. 117 4.9 Piecewise models and the marginsplot command . 124 . 127 Summary. 130 4.5 4.6 Piecewise with two known knots 4.10 Automating graphs of piecewise models 4.11 5 Continuous by
continuous interactions 131 5.1 Chapter overview. 131 5.2 Linearby linear interactions. 131 5.2.1 Overview. 131 5.2.2 Example usingGSS data. 136 5.2.3 Interpreting the interaction in terms of age. 137 5.2.4 Interpreting the interaction in terms of education. 139 5.2.5 Interpreting the interaction in terms of ageslope. 141 5.2.6 Interpreting the interaction in terms of the educ slope . . . 142 Linear by quadraticinteractions. 144 5.3.1 Overview. 144 5.3.2 Example using GSSdata. 147 Summary. 152 5.3 5.4
Contents viii 6 Continuous by continuous by continuous interactions 6.1 Chapter overview. 6.2 Overview 6.3 Examples using the GSS data. 6.4 . 15 153 1 54 6.3.1 A model without a three-way interaction. 158 6.3.2 A three-way interaction model. 1θ2 Summary. II Categorical predictors 171 7 Categorical predictors 173 7.1 Chapter overview. 173 7.2 Comparing two groups using a t test. 174 7.3 More groups and more predictors. 175 7.4 Overview of contrast operators. 181 7.5 Compare each group against a reference group. 182 7.5.1 Selecting a specific contrast. 183 7.5.2 Selecting a different referencegroup. 184 7.5.3 Selecting a contrast and referencegroup. 185 Compare each group against the grand mean . 185 Selecting a
specific contrast. 187 Compare adjacent means. 188 7.7.1 Reverse adjacent contrasts. 192 7.7.2 Selecting a specific contrast. 193 Comparing the mean of subsequent or previous levels. 194 7.8.1 Comparing the mean of previous levels. 198 7.8.2 Selecting a specific contrast. 199 7.9 Polynomial contrasts. 2qq 7.10 Custom contrasts. շցշ 7.11 Weighted contrasts. 2θθ 7.12 Pairwise comparisons. 2Qg 7.6 7.6.1 7.7 7.8
ix Contents 8 7.13 Interpreting confidence intervals. 210 7.14 Testing categorical variables using regression. 212 7.15 Summary. 215 Categorical by categorical interactions 217 8.1 Chapter overview. 217 8.2 Two by two models: Example 1. 219 8.2.1 Simple effects. 224 8.2.2 Estimating the sizeof the interaction. 225 8.2.3 More about interaction. 226 8.2.4 Summary . 227 Two by three models. 227 8.3.1 Example 2. 227 8.3.2 Example 3. 232 8.3.3 Summary . 237 Three by three models: Example 4. 237 8.4.1 Simple effects. 240 8.4.2
Simple contrasts. 240 8.4.3 Partial interaction . 242 8.4.4 Interaction contrasts. 243 8.4.5 Summary . 245 8.5 Unbalanced designs. 245 8.6 Main effects with interactions: anova versus regress. 250 8.7 Interpreting confidence intervals. 253 8.8 Summary. 255 8.3 8.4 9 Categorical by categorical by categorical interactions 257 9.1 Chapter overview. 257 9.2 Two by two by two models. 258 9.2.1 Simple interactions byseason. 260 9.2.2 Simple interactions by depression status. 261 9.2.3 Simple effects. 263
Contents x 9.3 9.4 9.5 Two by two by three models. 2®^ 9.3.1 Simple interactions by depression status. 9.3.2 Simple partial interaction by depression status. 266 9.3.3 Simple contrasts. 9.3.4 Partial interactions. 268 Three by three by three models and beyond. 270 9.4.1 Partial interactions and interaction contrasts 9.4.2 Simple interactions. 276 9.4.3 Simple effects and simple comparisons . 279 Summary. 28θ . 272 III Continuous and categorical predictors 10 11 266 281 Linear by categorical interactions 283 10.1 Chapter overview. 283 10.2 Linear and two-level categorical: No interaction. 283 10.2.1 Overview. 283 10.2.2 Examples using the GSS. 286 10.3 Linear by two-level categorical interactions. 291 10.3.1
Overview. 291 10.3.2 Examples using the GSS. 294 10.4 Linear by three-level categorical interactions. 299 10.4.1 Overview. 299 10.4.2 Examples using the GSS. 301 10.5 Summary. 308 Polynomial by categorical interactions 311 11.1 Chapter overview. 311 11.2 Quadratic by categorical interactions. 311 11.2.1 Overview. 312 11.2.2 Quadratic by two-level categorical. 315 11.2.3 Quadratic by three-level categorical. 323
Contents 12 xi 11.3 Cubic by categorical interactions . 329 11.4 Summary. 334 335 Piecewise by categorical interactions 12.1 Chapter overview. 335 12.2 One knot and one jump. 338 12.2.1 Comparing slopes across gender. 342 12.2.2 Comparing slopes across education. 343 12.2.3 Difference in differences of slopes. 343 12.2.4 Comparing changes in intercepts . 344 12.2.5 Computing and comparing adjusted means. 344 12.2.6 Graphing adjusted means . 347 Two knots and two jumps. 351 Comparing slopes across gender. 356 12.3.2 Comparing slopes across education. 357 12.3 12.3.1 12.3.3 Difference in differences of slopes. 358 12.3.4 Comparing changes in intercepts by gender . 359 12.3.5 Comparing changes in intercepts by education. 360 12.3.6 Computing and comparing adjusted
means. 12.3.7 12.4 Graphing adjusted means Comparing coding schemes . 361 364 . 366 12.4.1 Coding scheme #1. 12.4.2 Coding scheme #2. 368 12.4.3 Coding scheme #3. 367 370 12.4.4 Coding scheme #4. 372 12.4.5 13 Choosingcoding schemes. 373 12.5 Summary. 374 Continuous by continuous by categorical interactions 375 13.1 Chapter overview. 375 13.2 Linear by linear bycategorical interactions. 376 13.2.1 Fitting separate models for males and females. 376
Contents xii 13.2.2 Fitting a combined model for males and females. 378 13.2.3 Interpreting the interaction focusing in the age slope . 380 13.2.4 Interpreting the interaction focusing on the educ slope . . . 382 13.2.5 Estimating and comparing adjusted means by gender . 384 13.3 Linear by quadratic by categorical interactions . 386 13.3.1 Fitting separate models for males and females. 386 13.3.2 Fitting a common model for males and females. 388 13.3.3 Interpreting theinteraction . 389 13.3.4 Estimating and comparing adjusted means by gender . 390 13.4 Summary. 392 14 Continuous by categorical bycategorical interactions 393 14.1 Chapter overview. 393 14.2 Simple effects of gender on the age slope. 398 14.3 Simple effects of education on the age slope. 399 14.4 Simple contrasts on education for the age slope. 400 14.5 Partial interaction on education for the age slope. 400 14.6 Summary. 401 IV Beyond ordinary linear regression 403 15 Multilevel models 405 15.1 405 Chapter
overview. 15.2 Example 1: Continuous by continuous interaction.406 16 15.3 Example 2: Continuous by categorical interaction. 409 15.4 Example 3: Categorical by continuous interaction. 413 15.5 Example 4: Categorical by categorical interaction. 417 15.6 Summary. 4շլ Time as a continuous predictor 423 16.1 Chapter overview. 42$ 16.2 Example 1: Linear effect of time. 424 16.3 Example 2: Linear effect of time by a categorical predictor. 428
Contents 16.4 Example 3: Piecewise modeling of time. 433 16.5 Example 4: Piecewise effects of time by a categorical predictor . . . 16.6 17 xiii 438 16.5.1 Baseline slopes 16.5.2 Change in slopes: Treatment versus baseline. 444 16.5.3 Jump at treatment. 16.5.4 Comparisons among groups. 446 . Summary. 443 445 448 Time as a categorical predictor 449 17.1 Chapter overview. 449 17.2 Example 1: Time treated as a categorical variable. 450 17.3 Example 2: Time (categorical) by two groups. 455 17.4 Example 3: Time (categorical) by three groups. 459 17.5 Comparing models with different residual covariancestructures . . . 464 17.6 Analyses with small samples. 466 17.7 Summary. 474 18 Nonlinear models 475 18.1 Chapter overview. 475 18.2 Binary logistic
regression. 476 18.2.1 A logistic model with one categorical predictor 18.2.2 A logistic model with one continuous predictor. 484 18.2.3 A logistic model with covariates. 486 18.3 Multinomial logistic regression . 476 . 491 18.4 Ordinal logistic regression. 497 18.5 Poisson regression. 500 18.6 More applications of nonlinear models.503 19 18.6.1 Categorical by categorical interaction. 18.6.2 Categorical by continuous interaction.510 18.6.3 Piecewise modeling. 516 503 18.7 Summary. 522 Complex survey data 523
Contents xiv V Appendices A Customizing output from estimation commands В 531 A.l Omission of output. A.2 Specifying the confidence level. 533 A.3 Customizing the formatting of columns in the coefficient table . 534 A.4 Customizing the display of factor variables. 536 The margins command 545 B.l The predict() and expression() options. 545 B.2 The at() option. 548 B.3 Margins with factorvariables. 551 B.4 Margins with factorvariables and the at() option.557 B.5 The dydx() and related options. 559 B.6 Specifying the confidence level. 563 B.7 Customizing column formatting. 564 C The marginsplot command 567 D The contrast command 583 D.l Inclusion and omission of output. 584 D.2 Customizing the display of factor variables. 586 D.3 Adjustments for multiple comparisons. 588 D.4 Specifying the confidence
level. 588 D.5 Customizing column formatting. 589 E The pwcompare command 59լ References 597 Author index gø! Subject index |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Mitchell, Michael N. |
| author_GND | (DE-588)139115366 |
| author_facet | Mitchell, Michael N. |
| author_role | aut |
| author_sort | Mitchell, Michael N. |
| author_variant | m n m mn mnm |
| building | Verbundindex |
| bvnumber | BV046995081 |
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| ctrlnum | (OCoLC)1235890129 (DE-599)BVBBV046995081 |
| discipline | Informatik Soziologie Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Soziologie Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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| id | DE-604.BV046995081 |
| illustrated | Illustrated |
| index_date | 2024-07-03T15:54:32Z |
| indexdate | 2024-07-20T05:20:54Z |
| institution | BVB |
| isbn | 9781597183215 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032402871 |
| oclc_num | 1235890129 |
| open_access_boolean | |
| owner | DE-11 DE-29T DE-20 DE-473 DE-BY-UBG DE-N2 DE-355 DE-BY-UBR DE-703 DE-824 |
| owner_facet | DE-11 DE-29T DE-20 DE-473 DE-BY-UBG DE-N2 DE-355 DE-BY-UBR DE-703 DE-824 |
| physical | xxx, 610 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Stata Press |
| record_format | marc |
| series2 | A Stata Press publication |
| spelling | Mitchell, Michael N. Verfasser (DE-588)139115366 aut Interpreting and visualizing regression models using Stata Michael N. Mitchell Second edition College Station, Texas Stata Press 2021 xxx, 610 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier A Stata Press publication Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Visualisierung (DE-588)4188417-6 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s Stata (DE-588)4617285-3 s DE-604 Regressionsmodell (DE-588)4127980-3 s Visualisierung (DE-588)4188417-6 s Erscheint auch als Online-Ausgabe, EPUB 978-1-59718-322-2 Erscheint auch als Online-Ausgabe, MOBI 978-1-59718-323-9 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032402871&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Mitchell, Michael N. Interpreting and visualizing regression models using Stata Regressionsanalyse (DE-588)4129903-6 gnd Visualisierung (DE-588)4188417-6 gnd Stata (DE-588)4617285-3 gnd Regressionsmodell (DE-588)4127980-3 gnd |
| subject_GND | (DE-588)4129903-6 (DE-588)4188417-6 (DE-588)4617285-3 (DE-588)4127980-3 |
| title | Interpreting and visualizing regression models using Stata |
| title_auth | Interpreting and visualizing regression models using Stata |
| title_exact_search | Interpreting and visualizing regression models using Stata |
| title_exact_search_txtP | Interpreting and visualizing regression models using Stata |
| title_full | Interpreting and visualizing regression models using Stata Michael N. Mitchell |
| title_fullStr | Interpreting and visualizing regression models using Stata Michael N. Mitchell |
| title_full_unstemmed | Interpreting and visualizing regression models using Stata Michael N. Mitchell |
| title_short | Interpreting and visualizing regression models using Stata |
| title_sort | interpreting and visualizing regression models using stata |
| topic | Regressionsanalyse (DE-588)4129903-6 gnd Visualisierung (DE-588)4188417-6 gnd Stata (DE-588)4617285-3 gnd Regressionsmodell (DE-588)4127980-3 gnd |
| topic_facet | Regressionsanalyse Visualisierung Stata Regressionsmodell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032402871&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT mitchellmichaeln interpretingandvisualizingregressionmodelsusingstata |