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Introduction to Statistics and Data Analysis 5th Edition
ISBN-13: 9781305115347 | ISBN-10: 1305115341
© 2016 | Published |  844  Pages
Previous Editions: 9780840054906

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US \$275.95
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Roxy Peck, Chris Olsen, and Jay Devore's new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Hands-on activities and interactive applets allow students to practice statistics firsthand. INTRODUCTION TO STATISTICS AND DATA ANALYSIS, 5e includes updated coverage of most major technologies, as well as expanded coverage of probability.

• 1. THE ROLE OF STATISTICS AND THE DATA ANALYSIS PROCESS: Why Study Statistics? The Nature and Role of Variability. Statistics and the Data Analysis Process. Types of Data and Some Simple Graphical Displays.
2. COLLECTING DATA SENSIBLY: Statistical Studies: Observation and Experimentation. Sampling. Simple Comparative Experiments. More on Experimental Design. Interpreting and Communicating the Results of Statistical Analyses. More on Observational Studies: Designing Surveys (online).
3. GRAPHICAL METHODS FOR DESCRIBING DATA: Displaying Categorical Data: Comparative Bar Charts and Pie Charts. Displaying Numerical Data: Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Displaying Bivariate Numerical Data. Interpreting and Communicating the Results of Statistical Analyses.
4. NUMERICAL METHODS FOR DESCRIBING DATA: Describing the Center of a Data Set. Describing Variability in a Data Set. Summarizing a Data Set: Boxplots. Interpreting Center and Variability: Chebyshev’s Rule, the Empirical Rule, and z Scores. Interpreting and Communicating the Results of Statistical Analyses.
5. SUMMARIZING BIVARIATE DATA: Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Nonlinear Relationships and Transformations. Interpreting and Communicating the Results of Statistical Analyses. Logistic Regression (online).
6. PROBABILITY: Chance Experiments and Events. Definition of Probability. Basic Properties of Probability. Conditional Probability. Independence. Some General Probability Rules. Estimating Probabilities Empirically Using Simulation.
7. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS: Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. Mean and Standard Deviation of a Random Variable. Binomial and Geometric Distributions. Normal Distributions. Checking for Normality and Normalizing Transformations. Using the Normal Distribution to Approximate a Discrete Distribution.
8. SAMPLING VARIABILITY AND SAMPLING DISTRIBUTIONS: Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion.
9. Estimation Using a Single Sample: Point Estimation. Large-Sample Confidence Interval for a Population Proportion. Confidence Interval for a Population Mean. Interpreting and Communicating the Results of Statistical Analyses.
10. HYPOTHESIS TESTING USING A SINGLE SAMPLE: Hypotheses and Test Procedures. Errors in Hypothesis Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypothesis Tests for a Population Mean. Power and Probability of Type II Error. Interpreting and Communicating the Results of Statistical Analyses.
11. COMPARING TWO POPULATIONS OR TREATMENTS: Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples. Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples. Large-Sample Inferences Concerning the Difference Between Two Population or Treatment Proportions. Interpreting and Communicating the Results of Statistical Analyses.
12. THE ANALYSIS OF CATEGORICAL DATA AND GOODNESS-OF-FIT TESTS: Chi-Square Tests for Univariate Data. Tests for Homogeneity and Independence in a Two-way Table. Interpreting and Communicating the Results of Statistical Analyses.
13. SIMPLE LINEAR REGRESSION AND CORRELATION: INFERENTIAL METHODS: Simple Linear Regression Model. Inferences about the Slope of the Population Regression Line. Checking Model Adequacy. Inferences Based on the Estimated Regression Line (online). Inferences About the Population Correlation Coefficient (online). Interpreting and Communicating the Results of Statistical Analyses (online).
14. MULTIPLE REGRESSION ANALYSIS: Multiple Regression Models. Fitting a Model and Assessing Its Utility. Inferences Based on an Estimated Model (online). Other Issues in Multiple Regression (online). Interpreting and Communicating the Results of Statistical Analyses (online).
15. ANALYSIS OF VARIANCE: Single-Factor ANOVA and the F Test. Multiple Comparisons. The F Test for a Randomized Block Experiment (online). Two-Factor ANOVA (online). Interpreting and Communicating the Results of Statistical Analyses (online).
16. NONPARAMETRIC (DISTRIBUTION-FREE) STATISTICAL METHODS (ONLINE): Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples (Optional). Distribution Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples. Distribution-Free ANOVA.

• Access to Aplia™, an online interactive learning solution that improves comprehension and outcomes by increasing student effort and engagement is available with the text. Aplia provides automatically graded assignments with detailed, immediate explanations on every question, along with innovative teaching materials. Aplia homework engages students in critical thinking, requiring them to synthesize and apply knowledge, not simply recall it. All homework is written by subject matter experts in the field who have taught the course before. Aplia contains a robust course management system with powerful analytics, enabling you to track student performance easily.
• Real data gives students authentic scenarios that help them understand statistical concepts in relevant, interesting contexts.
• A chapter on Nonparametric Methods is available online, giving you the option of covering this topic if you wish.
• "Interpreting and Communicating the Results of Statistical Analysis" sections, which emphasize the importance of being able to interpret statistical output and communicate its meaning to non-statisticians, have assignable end-of-section questions associated with them.
• The book emphasizes graphical display as a necessary component of data analysis and provides broad coverage of sampling, survey design, experimental design and transformations, and nonlinear regression.
• The role of the computer in contemporary statistics is highlighted through numerous printouts and exercises that can be solved using the computer.
• Several Java™ applets, used in conjunction with activities that appear at the end of the chapter, provide visual insight into statistical concepts.
• A digital image bank in the Instructor's companion website (www.cengage.com/login) makes lecture and class preparation quick and easy.
• JMP statistical software is now packaged with new copies of the text.
• Chapter-ending Technology Notes on JMP, Minitab, SPSS, Microsoft Excel 2007, TI-83/84, and TI-nspire provide helpful hints and guidance on completing tasks associated with a particular chapter, as well as display screens to help students visualize and better understand the steps. More complete technology manuals are available on the text website.
• Helpful hints in exercises that direct students to relevant examples in the text help students who may be having trouble getting started.
• Many of the multistep exercises carried over from the previous edition are now broken into parts to make them more manageable and to help students organize their thinking about the solution.
• Margin Notes, including “Understanding the context,” “Consider the data,” “Formulate a plan,” “Do the work,” and “Interpret the results” appear in appropriate places in the examples to highlight the importance of context and to increase student awareness of the steps in the data analysis process.
• “This is a great mathematical statistics textbook”

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