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Introduction to Statistics and Data Analysis 5th Edition By Roxy Peck, Chris Olsen, Jay L Devore

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About this ebook
The 5th Edition of Introduction to Statistics and Data Analysis by Roxy Peck, Chris Olsen, and Jay L. Devore covers 15 core chapters. The textbook is structurally sequenced to mirror the natural progression of the data analysis process: moving from data collection and description to probability and statistical inference. [1, 2]
The main topics and chapters covered in this edition include: [1, 2]
Part 1: Data Collection and Descriptive Statistics
  • Chapter 1: The Role of Statistics and the Data Analysis Process – Covers the nature of variability, types of data, and simple graphical displays. [1]
  • Chapter 2: Collecting Data Sensibly – Focuses on statistical studies, observational studies vs. experiments, sampling techniques, and experimental design. [1]
  • Chapter 3: Graphical Methods for Describing Data – Includes visual summaries like dotplots, bar charts, histograms, and scatterplots. [1]
  • Chapter 4: Numerical Methods for Describing Data – Explores measures of center (mean, median) and measures of variability (range, variance, standard deviation).
  • Chapter 5: Summarizing Bivariate Data – Introduces correlation and basic linear regression to analyze relationships between two variables. [1]
Part 2: Probability and Random Variables
  • Chapter 6: Probability – Covers basic probability rules, independent events, and conditional probability.
  • Chapter 7: Random Variables and Probability Distributions – Explores discrete and continuous random variables, binomial distributions, and normal distributions.
  • Chapter 8: Sampling Variability and Sampling Distributions – Focuses on the Central Limit Theorem and the behavior of sample means and sample proportions. [1, 2]
Part 3: Inferential Statistics (Foundations & Single Samples)
  • Chapter 9: Estimation Using a Single Sample – Covers point estimation and constructing confidence intervals for a single population mean or proportion.
  • Chapter 10: Hypothesis Testing Using a Single Sample – Introduces the mechanics of hypothesis testing, p-values, Type I and Type II errors, and power. [1, 2]
Part 4: Advanced Inferential Methodology
  • Chapter 11: Comparing Two Populations or Treatments – Covers two-sample t-tests and confidence intervals for comparing two independent or paired groups.
  • Chapter 12: The Analysis of Categorical Data and Goodness-of-Fit Tests – Explores Chi-Square tests for independence and goodness-of-fit.
  • Chapter 13: Simple Linear Regression and Correlation: Inferential Methods – Looks at the inferential side of regression, testing the significance of slopes and predicting outcomes.
  • Chapter 14: Multiple Regression Analysis – Extends regression models to incorporate multiple predictor variables.
  • Chapter 15: Analysis of Variance (ANOVA) – Covers single-factor ANOVA to compare means across three or more distinct groups. [1, 2, 3, 4, 5]

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9781305115347 9781305445963 9781305449299 9781305683785 9798214346809 Advanced Inferential Methodology Analysis of Variance ANOVA Covers single-factor ANOVA to compare means across three or more distinct groups Collecting Data Sensibly Focuses on statistical studies Comparing Two Populations or Treatments Covers two-sample t-tests and confidence intervals for comparing two independent or paired groups Data Collection and Descriptive Statistics Estimation Using a Single Sample Covers point estimation and constructing confidence intervals for a single population mean or proportion Graphical Methods for Describing Data Includes visual summaries like dotplots Hypothesis Testing Using a Single Sample Introduces the mechanics of hypothesis testing Inferential Statistics Foundations & Single Samples Multiple Regression Analysis Extends regression models to incorporate multiple predictor variables Numerical Methods for Describing Data Explores measures of center Probability and Random Variables Probability Covers basic probability rules Random Variables and Probability Distributions Explores discrete and continuous random variables Sampling Variability and Sampling Distributions Focuses on the Central Limit Theorem and the behavior of sample means and sample proportions Simple Linear Regression and Correlation: Inferential Methods Looks at the inferential side of regression Summarizing Bivariate Data Introduces correlation and basic linear regression to analyze relationships between two variables The Analysis of Categorical Data and Goodness-of-Fit Tests Explores Chi-Square tests for independence and goodness-of-fit The Role of Statistics and the Data Analysis Process Covers the nature of variability