Statistics using stata: an integrative approach
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY, USA
Cambridge University Press
2016
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes index |
| Beschreibung: | xix, 660 Seiten Diagramme |
| ISBN: | 9781107461185 |
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| 245 | 1 | 0 | |a Statistics using stata |b an integrative approach |c Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University |
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Datensatz im Suchindex
| _version_ | 1804176899151233024 |
|---|---|
| adam_text | Titel: Statistics using stata
Autor: Weinberg, Sharon Lawner
Jahr: 2016
Contents
Preface page xv
Acknowledgments xix
1 INTRODUCTION 1
The Role of the Computer in Data Analysis 1
Statistics: Descriptive and Inferential 2
Variables and Constants 3
The Measurement of Variables 3
Discrete and Continuous Variables 8
Setting a Context with Real Data 11
Exercises 14
2 EXAMINING UNIVARIATE DISTRIBUTIONS 26
Counting the Occurrence of Data Values 26
When Variables Are Measured at the Nominal Level 26
Frequency and Percent Distribution Tables 27
Bar Charts 28
Pie Charts 31
When Variables Are Measured at the Ordinal, Interval, or Ratio Level 31
Frequency and Percent Distribution Tables 32
Stem-and-Leaf Displays 35
Histograms 37
Line Graphs 39
Describing the Shape of a Distribution 41
Accumulating Data 43
Cumulative Percent Distributions 43
Ogive Curves 44
Percentile Ranks 45
Percentiles 46
Five-Number Summaries and Boxplots 49
Modifying the Appearance of Graphs 54
Summary of Graphical Selection 54
Summary of Stata Commands in Chapter 2 55
Exercises 57
3 MEASURES OF LOCATION, SPREAD, AND SKEWNESS 72
Characterizing the Location of a Distribution 72
The Mode 72
The Median 76
CONTENTS
The Arithmetic Mean 78
Interpreting the Mean of a Dichotomous Variable 80
The Weighted Mean 81
Comparing the Mode, Median, and Mean 82
Characterizing the Spread of a Distribution 84
The Range and Interquartile Range 86
The Variance 88
The Standard Deviation 91
Characterizing the Skewness of a Distribution 92
Selecting Measures of Location and Spread 96
Applying What We Have Learned 96
Summary of Stata Commands in Chapter 3 100
Helpful Hints When Using Stata 102
Online Resources 102
The Stata Command 102
Stata TIPS 104
Exercises 105
4 REEXPRESSING VARIABLES 113
Linear and Nonlinear Transformations 113
Linear Transformations: Addition, Subtraction, Multiplication,
and Division 114
The Effect on the Shape of a Distribution 115
The Effect on Summary Statistics of a Distribution 116
Common Linear Transformations 119
Standard Scores 121
z-Scores 122
Using z-Scores to Detect Outliers 125
Using z-Scores to Compare Scores in Different Distributions 127
Relating z-Scores to Percentile Ranks 128
Nonlinear Transformations: Square Roots and Logarithms 129
Nonlinear Transformations: Ranking Variables 136
Other Transformations: Recoding and Combining Variables 138
Recoding Variables 138
Combining Variables 141
Data Management Fundamentals - the Do-File 141
Summary of Stata Commands in Chapter 4 144
Exercises 145
5 EXPLORING RELATIONSHIPS BETWEEN TWO VARIABLES 153
When Both Variables Are at Least Interval-Leveled 153
Scatterplots 154
The Pearson Product Moment Correlation Coefficient 160
Interpreting the Pearson Correlation Coefficient 164
• Judging the Strength of the Linear Relationship, 164 • The Correlation Scale
Itself Is Ordinal, 165 • Correlation Does Not Imply Causation, 166 • The Effect
of Linear Transformations, 166 »Restriction of Range, 167 • The Shape of the
Underlying Distributions, 167 • The Reliability of the Data, 167
When at Least One Variable Is Ordinal and the Other Is at Least Ordinal:
The Spearman Rank Correlation Coefficient 168
CONTENTS
When at Least One Variable Is Dichotomous: Other Special Cases of
the Pearson Correlation Coefficient 169
The Point Biserial Correlation Coefficient: The Case of One at Least Interval
and One Dichotomous Variable 169
The Phi Coefficient: The Case of Two Dichotomous Variables 174
Other Visual Displays of Bivariate Relationships 179
Selection of Appropriate Statistic/Graph to Summarize a Relationship 182
Summary of Stata Commands in Chapter 5 182
Exercises 183
6 SIMPLE LINEAR REGRESSION 196
The Best-Fitting Linear Equation 196
The Accuracy of Prediction Using the Linear Regression Model 203
The Standardized Regression Equation 204
R as a Measure of the Overall Fit of the Linear Regression Model 204
Simple Linear Regression When the Independent Variable Is Dichotomous 208
Using r and R as Measures of Effect Size 211
Emphasizing the Importance of the Scatterplot 211
Summary of Stata Commands in Chapter 6 213
Exercises 213
7 PROBABILITY FUNDAMENTALS 222
The Discrete Case 222
The Complement Rule of Probability 224
The Additive Rules of Probability 225
First Additive Rule of Probability 225
Second Additive Rule of Probability 226
The Multiplicative Rule of Probability 227
The Relationship between Independence and Mutual Exclusivity 230
Conditional Probability 230
The Law of Large Numbers 232
Exercises 232
8 THEORETICAL PROBABILITY MODELS 235
The Binomial Probability Model and Distribution 235
The Applicability of the Binomial Probability Model 240
The Normal Probability Model and Distribution 244
Using the Normal Distribution to Approximate the Binomial Distribution 251
Summary of Chapter 8 Stata Commands 251
Exercises 252
9 THE ROLE OF SAMPLING IN INFERENTIAL STATISTICS 259
Samples and Populations 259
Random Samples 260
Obtaining a Simple Random Sample 261
Sampling with and without Replacement 263
Sampling Distributions 265
Describing the Sampling Distribution of Means Empirically 265
Describing the Sampling Distribution of Means Theoretically: The Central Limit
Theorem 270
Central Limit Theorem (CLT) 271
CONTENTS
Estimators and BIAS 275
Summary of Chapter 9 Stata Commands 276
Exercises 277
10 INFERENCES INVOLVING THE MEAN OF A SINGLE POPULATION
WHEN o IS KNOWN 281
Estimating the Population Mean, fi, When the Population Standard
Deviation, o~, Is Known 281
Interval Estimation 283
Relating the Length of a Confidence Interval, the Level of Confidence,
and the Sample Size 286
Hypothesis Testing 287
The Relationship between Hypothesis Testing and Interval Estimation 295
Effect Size 296
Type II Error and the Concept of Power 297
Increasing the Level of Significance, a 300
Increasing the Effect Size, 8 301
Decreasing the Standard Error of the Mean, (7^ 301
Closing Remarks 302
Summary of Chapter 10 Stata Commands 302
Exercises 304
11 INFERENCES INVOLVING THE MEAN WHEN a IS NOT KNOWN:
ONE- AND TWO-SAMPLE DESIGNS 308
Single Sample Designs When the Parameter of Interest Is the Mean
and a Is Not Known 308
The t Distribution 309
Degrees of Freedom for the One Sample f-Test 310
Violating the Assumption of a Normally Distributed Parent Population
in the One Sample f-Test 311
Confidence Intervals for the One Sample f-Test 312
Hypothesis Tests: The One Sample f-Test 319
Effect Size for the One Sample f-Test 322
Two Sample Designs When the Parameter of Interest Is u, and a Is Not Known 325
Independent (or Unrelated) and Dependent (or Related) Samples 326
Independent Samples f-Test and Confidence Interval 327
The Assumptions of the Independent Samples f-Test 329
Effect Size for the Independent Samples f-Test 338
Paired Samples f-Test and Confidence Interval 342
The Assumptions of the Paired Samples f-Test 343
Effect Size for the Paired Samples f-Test 348
The Bootstrap 349
Summary 354
Summary of Chapter 11 Stata Commands 355
Exercises 359
12 RESEARCH DESIGN: INTRODUCTION AND OVERVIEW 374
Questions and Their Link to Descriptive, Relational, and Causal Research Studies 374
The Need for a Good Measure of Our Construct, Weight 374
The Descriptive Study 375
CONTENTS
From Descriptive to Relational Studies 376
From Relational to Causal Studies 376
The Gold Standard of Causal Studies: The True Experiment and
Random Assignment 378
Comparing Two Kidney Stone Treatments Using a Non-randomized
Controlled Study 379
Including Blocking in a Research Design 380
Underscoring the Importance of Having a True Control Group
Using Randomization 381
Analytic Methods for Bolstering Claims of Causality from Observational
Data (Optional Reading) 385
Quasi-Experimental Designs 387
Threats to the Internal Validity of a Quasi-Experimental Design 387
Threats to the External Validity of a Quasi-Experimental Design 388
Threats to the Validity of a Study: Some Clarifications and Caveats 389
Threats to the Validity of a Study: Some Examples 390
Exercises 391
13 ONE-WAY ANALYSIS OF VARIANCE 395
The Disadvantage of Multiple f-Tests 395
The One-Way Analysis of Variance 397
A Graphical Illustration of the Role of Variance in Tests on Means 397
ANOVA as an Extension of the Independent Samples f-Test 399
Developing an Index of Separation for the Analysis of Variance 399
Carrying Out the ANOVA Computation 400
The Between Group Variance (MSB) 401
The Within Group Variance (MSW) 401
The Assumptions of the One-Way ANOVA 402
Testing the Equality of Population Means: The F-Ratio 403
How to Read the Tables and Use Stata Functions for the F-Distribution 404
ANOVA Summary Table 408
Measuring the Effect Size 408
Post-Hoc Multiple Comparison Tests 413
The Bonferroni Adjustment: Testing Planned Comparisons 426
The Bonferroni Tests on Multiple Measures 428
Summary of Stata Commands in Chapter 13 429
Exercises 431
14 TWO-WAY ANALYSIS OF VARIANCE 436
The Two-Factor Design 436
The Concept of Interaction 439
The Hypotheses That Are Tested by a Two-Way Analysis of Variance 444
Assumptions of the Two-Way Analysis of Variance 444
Balanced versus Unbalanced Factorial Designs 446
Partitioning the Total Sum of Squares 447
Using the F-Ratio to Test the Effects in Two-Way ANOVA 447
Carrying Out the Two-Way ANOVA Computation by Hand 448
Decomposing Score Deviations about the Grand Mean 453
Modeling Each Score as a Sum of Component Parts 454
Explaining the Interaction as a Joint (or Multiplicative) Effect 454
CONTENTS
Measuring Effect Size 455
Fixed versus Random Factors 457
Post-hoc Multiple Comparison Tests 458
Summary of Steps to be Taken in a Two-Way ANOVA Procedure 462
Summary of Stata Commands in Chapter 14 466
Exercises 467
15 CORRELATION AND SIMPLE REGRESSION AS INFERENTIAL TECHNIQUES 476
The Bivariate Normal Distribution 476
Testing Whether the Population Pearson Product Moment Correlation
Equals Zero 479
Using a Confidence Interval to Estimate the Size of the Population
Correlation Coefficient, p 482
Revisiting Simple Linear Regression for Prediction 485
Estimating the Population Standard Error of Prediction, aY^ 486
Testing the b- Weight for Statistical Significance 487
Explaining Simple Regression Using an Analysis of Variance Framework 491
Measuring the Fit of the Overall Regression Equation: Using JR and R2 493
Relating R2 to a2ypt 494
Testing R2 for Statistical Significance 495
Estimating the True Population R2: The Adjusted R2 496
Exploring the Goodness of Fit of the Regression Equation: Using
Regression Diagnostics 497
Residual Plots: Evaluating the Assumptions Underlying Regression 498
Detecting Influential Observations: Discrepancy and Leverage 501
Using Stata to Obtain Leverage 503
Using Stata to Obtain Discrepancy 503
Using Stata to Obtain Influence 504
Using Diagnostics to Evaluate the Ice Cream Sales Example 505
Using the Prediction Model to Predict Ice Cream Sales 508
Simple Regression When the Predictor is Dichotomous 509
Summary of Stata Commands in Chapter 15 511
Exercises 511
16 AN INTRODUCTION TO MULTIPLE REGRESSION 523
The Basic Equation with Two Predictors 524
Equations for b, ß, and RY.l2 When the Predictors Are Not Correlated 525
Equations for b, ß, and RY.i2 When the Predictors Are Correlated 526
Summarizing and Expanding on Some Important Principles of Multiple Regression 527
Testing the b-Weights for Statistical Significance 533
Assessing the Relative Importance of the Independent Variables in the Equation 535
Measuring the Drop in R2 Directly: An Alternative to the Squared
Semipartial Correlation 536
Evaluating the Statistical Significance of the Change in R2 536
The b-Weight as a Partial Slope in Multiple Regression 538
Multiple Regression When One of the Two Independent Variables is Dichotomous 540
The Concept of Interaction between Two Variables That Are at Least Interval-Leveled 545
Testing the Statistical Significance of an Interaction Using Stata 548
Centering First-Order Effects to Achieve Meaningful Interpretations of b-Weights 552
Understanding the Nature of a Statistically Significant Two-Way Interaction 553
CONTENTS
Interaction When One of the Independent Variables Is Dichotomous
and the Other Is Continuous 556
Summary of Stata Commands in Chapter 16 559
Exercises 561
17 NONPARAMETRIC METHODS 573
Parametric versus Nonparametric Methods 573
Nonparametric Methods When the Dependent Variable Is at the Nominal Level 574
The Chi-Square Distribution (x2) 574
The Chi-Square Goodness-of-Fit Test 576
The Chi-Square Test of Independence 581
Assumptions of the Chi-Square Test of Independence 584
Fisher s Exact Test 586
Calculating the Fisher Exact Test by Hand Using the
Hypergeometric Distribution 587
Nonparametric Methods When the Dependent Variable Is Ordinal-Leveled 590
Wilcoxon Sign Test 591
The Mann-Whitney U Test 594
The Kruskal-Wallis Analysis of Variance 598
Summary of Stata Commands in Chapter 17 600
Exercises 601
Appendix A Data Set Descriptions 607
Appendix B Stata .do Files and Data Sets in Stata Format 621
Appendix C Statistical Tables 622
Appendix D References 644
Appendix E Solutions 648
Index 649
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| spelling | Weinberg, Sharon Lawner 1947- Verfasser (DE-588)1050691148 aut Statistics using stata an integrative approach Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University New York, NY, USA Cambridge University Press 2016 xix, 660 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Includes index Stata Datenverarbeitung Mathematical statistics Data processing Stata (DE-588)4617285-3 gnd rswk-swf Stata (DE-588)4617285-3 s DE-604 Abramowitz, Sarah Knapp 1967- Verfasser (DE-588)173601332 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029358649&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Weinberg, Sharon Lawner 1947- Abramowitz, Sarah Knapp 1967- Statistics using stata an integrative approach Stata Datenverarbeitung Mathematical statistics Data processing Stata (DE-588)4617285-3 gnd |
| subject_GND | (DE-588)4617285-3 |
| title | Statistics using stata an integrative approach |
| title_auth | Statistics using stata an integrative approach |
| title_exact_search | Statistics using stata an integrative approach |
| title_full | Statistics using stata an integrative approach Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University |
| title_fullStr | Statistics using stata an integrative approach Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University |
| title_full_unstemmed | Statistics using stata an integrative approach Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University |
| title_short | Statistics using stata |
| title_sort | statistics using stata an integrative approach |
| title_sub | an integrative approach |
| topic | Stata Datenverarbeitung Mathematical statistics Data processing Stata (DE-588)4617285-3 gnd |
| topic_facet | Stata Datenverarbeitung Mathematical statistics Data processing |
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