Introduction to Probability and Statistics Mendenhall 13th Edition

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Introduction to Probability and Statistics Mendenhall 13th Edition

Battling with how to estimate statistical data and probability outcomes? Then the introduction to probability and statistics mendenhall pdf book is all you need to stay abreast of statistical findings and theories. The introduction to probability and statistics mendenhall 13th edition pdf book explains in details what students and researchers need to understand statistical outcomes and research.

About Introduction to Probability and Statistics Mendenhall Pdf  Book

This introduction to probability and statistics mendenhall pdf  book is used by hundreds of thousands of students, the market-leading INTRODUCTION TO PROBABILITY AND STATISTICS 13TH EDITION BOOK, blends proven coverage with new innovations to ensure you gain a solid understanding of statistical concepts–and see their relevance to your everyday life. The new thirteenth edition of the introduction to probability and statistics mendenhall pdf  book retains the text’s straightforward presentation and traditional outline for descriptive and inferential statistics while incorporating modern technology–including computational software and interactive visual tools–to help you master statistical reasoning and skillfully interpret statistical results, its is a supplement material for  Statistics Data Analysis and Statistical Inference courses. Exciting learning tools like MyPersonal Trainer, MyApplet, and MyTip ensure that you thoroughly understand chapter material and give you hands-on experience putting it into action. Drawing from decades of classroom teaching experience, the authors clearly illustrate how to apply statistical procedures as they explain how to describe real sets of data, what statistical tests mean in terms of practical application, how to evaluate the validity of the assumptions behind statistical tests, and what to do when statistical assumptions have been violated. Statistics can be an intimidating course, but with this text you will be well prepared. With its thorough explanations, insightful examples, practical exercises, and innovative technology features, INTRODUCTION TO PROBABILITY AND STATISTICS MENDENHALL PDF Book,   equips you with a firm foundation in statistical concepts, as well as the tools to apply them to the world around you.


Table of Contents

Introduction: An Invitation to Statistics 1
The Population and the Sample 2
Descriptive and Inferential Statistics 3
Achieving the Objective of Inferential Statistics: The Necessary Steps 4
1 Describing Data with Graphs 7
1.1 Variables and Data 8
1.2 Types of Variables 9
1.3 Graphs for Categorical Data 11
1.4 Graphs for Quantitative Data 17
1.5 Relative Frequency Histograms 23
2 Describing Data with Numerical Measures 50
2.1 Describing a Set of Data with Numerical Measures 51
2.2 Measures of Center 51
2.3 Measures of Variability 57
2.4 On the Practical Significance of the Standard Deviation 63
2.5 A Check on the Calculation of s 67
2.6 Measures of Relative Standing 73
2.7 The Five-Number Summary and the Box Plot 76
3 Describing Bivariate Data 93
3.1 Bivariate Data 94
3.2 Graphs for Qualitative Variables 94
3.3 Scatterplots for Two Quantitative Variables 98
3.4 Numerical Measures for Quantitative Bivariate Data 100
4 Probability and Probability Distributions 119
4.1 The Role of Probability in Statistics 120
4.2 Events and the Sample Space 120
4.3 Calculating Probabilities Using Simple Events 123
4.4 Useful Counting Rules (Optional) 129
4.5 Event Relations and Probability Rules 136
4.6 Conditional Probability, Independence, and the Multiplicative Rule 140
4.7 Bayes’ Rule (Optional) 149
4.8 Discrete Random Variables and Their Probability Distributions 154
5 Several Useful Discrete Distributions 174
5.1 Introduction 175
5.2 The Binomial Probability Distribution 175
5.3 The Poisson Probability Distribution 187
5.4 The Hypergeometric Probability Distribution 191
6 The Normal Probability Distribution 205
6.1 Probability Distributions for Continuous Random Variables 206
6.2 The Normal Probability Distribution 208
6.3 Tabulated Areas of the Normal Probability Distribution 210
6.4 The Normal Approximation to the Binomial Probability Distribution (Optional) 220
7 Sampling Distributions 236
7.1 Introduction 237
7.2 Sampling Plans and Experimental Designs 237
7.3 Statistics and Sampling Distributions 241
7.4 The Central Limit Theorem 243
7.5 The Sampling Distribution of the Sample Mean 247
7.6 The Sampling Distribution of the Sample Proportion 253
7.7 A Sampling Application: Statistical Process Control (Optional) 258
8 Large-Sample Estimation 274
8.1 Where We’ve Been 275
8.2 Where We’re Going–Statistical Inference 275
8.3 Types of Estimators 276
8.4 Point Estimation 277
8.5 Interval Estimation 284
8.6 Estimating the Difference between Two Population Means 294
8.7 Estimating the Difference between Two Binomial Proportions 299
8.8 One-Sided Confidence Bounds 303
8.9 Choosing the Sample Size 305
9 Large-Sample Tests of Hypotheses 320
9.1 Testing Hypotheses about Population Parameters 321
9.2 A Statistical Test of Hypothesis 321
9.3 A Large-Sample Test about a Population Mean 324
9.4 A Large-Sample Test of Hypothesis for the Difference between Two Population Means 337
9.5 A Large-Sample Test of Hypothesis for a Binomial Proportion 343
9.6 A Large-Sample Test of Hypothesis for the Difference between Two Binomial Proportions 348
9.7 Some Comments on Testing Hypotheses 353
10 Inference from Small Samples 362
10.1 Introduction 363
10.2 Student’s t Distribution 363
10.3 Small-Sample Inferences Concerning a Population Mean 367
10.4 Small-Sample Inferences for the Difference between Two Population Means: Independent Random Samples 375
10.5 Small-Sample Inferences for the Difference between Two Means: A Paired-Difference Test 386
10.6 Inferences Concerning a Population Variance 394
10.7 Comparing Two Population Variances 401
10.8 Revisiting the Small-Sample Assumptions 409
11 The Analysis of Variance 426
11.1 The Design of an Experiment 427
11.2 What Is an Analysis of Variance? 428
11.3 The Assumptions for an Analysis of Variance 428
11.4 The Completely Randomized Design: A One-Way Classification 429
11.5 The Analysis of Variance for a Completely Randomized Design 430
11.6 Ranking Population Means 442
11.7 The Randomized Block Design: A Two-Way Classification 445
11.8 The Analysis of Variance for a Randomized Block Design 446
11.9 The a x b Factorial Experiment: A Two-Way Classification 458
11.10 The Analysis of Variance for an a x b Factorial Experiment 459
11.11 Revisiting the Analysis of Variance Assumptions 467
11.12 A Brief Summary 470
12 Linear Regression and Correlation 483
12.1 Introduction 484
12.2 A Simple Linear Probabilistic Model 484
12.3 The Method of Least Squares 486
12.4 An Analysis of Variance for Linear Regression 489
12.5 Testing the Usefulness of the Linear Regression Model 494
12.6 Diagnostic Tools for Checking the Regression Assumptions 502
12.7 Estimation and Prediction Using the Fitted Line 506
12.8 Correlation Analysis 513
13 Multiple Regression Analysis 532
13.1 Introduction 533
13.2 The Multiple Regression Model 533
13.3 A Multiple Regression Analysis 534
13.4 A Polynomial Regression Model 540
13.5 Using Quantitative and Qualitative Predictor Variables in a Regression Model 548
13.6 Testing Sets of Regression Coefficients 556
13.7 Interpreting Residual Plots 559
13.8 Stepwise Regression Analysis 560
13.9 Misinterpreting a Regression Analysis 561
13.10 Steps to Follow When Building a Multiple Regression Model 563
14 Analysis of Categorical Data 575
14.1 A Description of the Experiment 576
14.2 Pearson’s Chi-Square Statistic 577
14.3 Testing Specified Cell Probabilities: The Goodness-of-Fit Test 578
14.4 Contingency Tables: A Two-Way Classification 582
14.5 Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column Totals 590
14.6 The Equivalence of Statistical Tests 594
14.7 Other Applications of the Chi-Square Test 595
15 Nonparametric Statistics 610
15.1 Introduction 611
15.2 The Wilcoxon Rank Sum Test: Independent Random Samples 611
15.3 The Sign Test for a Paired Experiment 620
15.4 A Comparison of Statistical Tests 625
15.5 The Wilcoxon Signed-Rank Test for a Paired Experiment 626
15.6 The Kruskal-Wallis H Test for Completely Randomized Designs 632
15.7 The Friedman F[subscript r] Test for Randomized Block Designs 638
15.8 Rank Correlation Coefficient 643
15.9 Summary 650
Appendix I 663
Table 1 Cumulative Binomial Probabilities 664
Table 2 Cumulative Poisson Probabilities 670
Table 3 Areas under the Normal Curve 672
Table 4 Critical Values of t 675
Table 5 Critical Values of Chi-Square 676
Table 6 Percentage Points of the F Distribution 678
Table 7 Critical Values of T for the Wilcoxon Rank Sum Test, n[subscript 1] [less than or equal] n[subscript 2] 686
Table 8 Critical Values of T for the Wilcoxon Signed-Rank Test, n = 5(1)50 688
Table 9 Critical Values of Spearman’s Rank Correlation Coefficient for a One-Tailed Test 689
Table 10 Random Numbers 690
Table 11 Percentage Points of the Studentized Range, q[subscript [alpha](k, df) 692
Answers to Selected Exercises 696

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