Preface |
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vii | |
Acknowledgements |
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xi | |
About the Authors |
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xii | |
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1 | (184) |
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Exploring Data by Graphical Methods |
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3 | (54) |
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The Science of Statistics |
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5 | (6) |
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Displaying Small Sets of Numbers: Dotplots and Stem-and-Leaf Displays |
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11 | (11) |
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Graphing Categorical Data |
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22 | (5) |
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27 | (7) |
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34 | (7) |
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41 | (16) |
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51 | (6) |
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Summarizing Data by Numerical Measures: Center and Spread |
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57 | (70) |
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59 | (8) |
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Mean versus Median versus Mode as a Measure of Center |
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67 | (10) |
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Measuring the Spread of a Data Set: The Standard Deviation |
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77 | (16) |
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93 | (7) |
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Boxplot: The Five-Number Summary |
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100 | (11) |
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Descriptive Data Analysis |
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111 | (16) |
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121 | (6) |
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Linear Relationships: Regression and Correlation |
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127 | (58) |
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129 | (5) |
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The Correlation Coefficient |
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134 | (12) |
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146 | (31) |
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The Question of Causation |
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177 | (8) |
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181 | (4) |
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Part II Probability Modeling and Obtaining Data |
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185 | (206) |
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Probabilities and Simulation |
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187 | (74) |
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189 | (8) |
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197 | (21) |
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Simulation: A Powerful Tool for Learning and Doing Statistics |
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218 | (21) |
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Simulating Random Sampling via a Box Model |
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239 | (7) |
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Random Sampling with or without Replacement |
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246 | (15) |
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254 | (7) |
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Expected Value and Simulation |
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261 | (34) |
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The Expected Value (Theoretical Mean) of a Random Variable |
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263 | (12) |
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Using Five-Step Simulation to Estimate Mean Values |
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275 | (10) |
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The Standard Deviation of a Random Variable |
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285 | (10) |
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292 | (3) |
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Probability Distributions: The Essentials |
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295 | (48) |
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The Binomial Distribution |
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297 | (10) |
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The Geometric Distribution |
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307 | (4) |
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311 | (12) |
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The Normal Probability Distribution |
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323 | (20) |
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339 | (4) |
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Obtaining Data: Random Sampling and Randomized Experiments |
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343 | (48) |
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345 | (5) |
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Experimental Design: Observational Studies versus Randomized Experiments |
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350 | (16) |
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Randomized Block Designs, Including Matched-Pairs Designs |
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366 | (6) |
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Survey Sampling from a Real Population: Probability Sampling versus Non-Probability Sampling |
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372 | (8) |
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From Research Question to Data Collection: Some Examples |
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380 | (11) |
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388 | (3) |
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Part III Statistical Inference: Estimation and Hypothesis Testing |
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391 | (277) |
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Confidence Interval Estimation |
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393 | (74) |
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An Introduction to the Statistical Estimation Problem |
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395 | (13) |
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The Central Limit Theorum for X and p |
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408 | (18) |
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Large-Sample Confidence Interval for the Population Mean μ |
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426 | (5) |
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Large-Sample Confidence Interval for the Population Proportion p |
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431 | (2) |
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Bootstrapping a Sample, Bootstrapped SEs, and SE-based Bootstrapped Confidence Intervals |
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433 | (15) |
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Confidence Interval for the Difference between Two Population Means μx - μY |
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448 | (7) |
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Large-Sample Confidence Interval for the Difference p1--p2 Between Two Population Proportions |
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455 | (3) |
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Confidence Interval for the Difference of Two Population Means in Matched-Pairs Design Case: μD - μX - μY |
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458 | (2) |
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Point Estimate for the Population Variance σ2 and SD σ, Unbiasedness of an Estimator |
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460 | (7) |
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464 | (3) |
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467 | (92) |
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The Null Hypothesis and the Alternative Hypothesis |
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472 | (3) |
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Tests for a Population Proportion |
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475 | (12) |
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Tests for Randomized Controlled Experiments Producing Sample Proportions |
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487 | (11) |
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Tests for a Population Mean |
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498 | (18) |
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Tests for Equality of Two Population Proportions and Equality of Two Population Means |
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516 | (16) |
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One-sided and Two-sided Hypothesis Testing |
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532 | (7) |
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Significance Testing versus Acceptance/Rejection Testing: Concepts and Methods |
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539 | (12) |
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Test for a Population Standard Deviation: Issues |
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551 | (8) |
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552 | (7) |
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559 | (66) |
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561 | (3) |
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How Big a Difference in the D Statistic Makes a Difference? |
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564 | (5) |
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569 | (5) |
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Real-Life Chi-Square Examples |
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574 | (9) |
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583 | (9) |
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The Chi-Square Distribution and Its Use for Chi-Square Testing |
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592 | (8) |
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Unequal Expected Frequencies |
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600 | (9) |
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Chi-Square Tests of Independence and Homogeneity for a Two-Way Contingency Table |
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609 | (16) |
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619 | (6) |
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Inference About Regression |
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625 | (2) |
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627 | (23) |
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Confidence Interval for Regression-Based Prediction of Y Given x and For Estimation of the Line E(Y | x) |
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650 | (4) |
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Applying Regression to Nonlinear Relationships by Transforming the Variables |
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654 | (10) |
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664 | |
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Analysis of Variance and Multiple Regression (Provided on included CD-Rom) |
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2 | (666) |
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Introduction to the Analysis of Variance |
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4 | (9) |
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Using the Bootstrap to Test for Differences Among Several Population Means |
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13 | (5) |
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18 | (2) |
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One-Way Analysis of Variance |
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20 | (8) |
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Multiple Comparisons of Population Means for a One-Way ANOVA |
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28 | (6) |
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Two-Way Analysis of Variance |
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34 | (12) |
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Multiple Linear Regression: Simulation-Based Testing and F-Distribution Testing |
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46 | (10) |
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Multiple Regression with Many Explanatory Variables |
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56 | (612) |
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62 | (606) |
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668 | (106) |
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A Computationally Generated Random Digits |
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690 | (2) |
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692 | (4) |
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C Chi-Square Probabilities |
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696 | (2) |
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698 | (2) |
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700 | (2) |
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F Student's t Probabilities |
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702 | (2) |
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704 | (8) |
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H F-Distribution Probabilities |
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712 | (3) |
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I Bonferroni Confidence Intervals |
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715 | (1) |
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J Cumulative Poisson Probabilities |
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716 | (702) |
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K TI Graphing Calculator Supplement |
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718 | (56) |
Index |
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774 | |