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  • 1.8: Sampling and Data (Exercises)
    https://stats.libretexts.org/Workbench/Statistics_for_Behavioral_Science_Majors/01%3A_Naming_Collecting_Data_and_Research_Design/1.08%3A_Sampling_and_Data_(Exercises)
    These are homework exercises to accompany the Textmap created for "Introductory Statistics" by OpenStax.
  • 1.1: Introduction
    https://stats.libretexts.org/Workbench/Statistics_for_Behavioral_Science_Majors/01%3A_Naming_Collecting_Data_and_Research_Design/1.01%3A_Introduction
    Included in this chapter are the basic ideas of statistics. You will soon understand how to classify data in statistics. You will also learn how data are gathered and what "good" data can be distingui...Included in this chapter are the basic ideas of statistics. You will soon understand how to classify data in statistics. You will also learn how data are gathered and what "good" data can be distinguished from "bad."
  • Glossary
    https://stats.libretexts.org/Bookshelves/Applied_Statistics/Learning_Statistics_with_R_-_A_tutorial_for_Psychology_Students_and_other_Beginners_(Navarro)/zz%3A_Back_Matter/20%3A_Glossary
    Example and Directions Words (or words that have the same definition) The definition is case sensitive (Optional) Image to display with the definition [Not displayed in Glossary, only in pop-up on pag...Example and Directions Words (or words that have the same definition) The definition is case sensitive (Optional) Image to display with the definition [Not displayed in Glossary, only in pop-up on pages] (Optional) Caption for Image (Optional) External or Internal Link (Optional) Source for Definition "Genetic, Hereditary, DNA ...") (Eg. "Relating to genes or heredity") The infamous double helix CC-BY-SA; Delmar Larsen Glossary Entries Definition Image Sample Word 1 Sample Definition 1
  • 3.3: Card Shuffling
    https://stats.libretexts.org/Bookshelves/Probability_Theory/Introductory_Probability_(Grinstead_and_Snell)/03%3A_Combinatorics/3.03%3A_Card_Shuffling
    Given a deck of n cards, how many times must we shuffle it to make it “random"? Of course, the answer depends upon the method of shuffling which is used and what we mean by “random."
  • 1.2: Discrete Probability Distribution
    https://stats.libretexts.org/Bookshelves/Probability_Theory/Introductory_Probability_(Grinstead_and_Snell)/01%3A_Discrete_Probability_Distributions/1.02%3A_Discrete_Probability_Distribution
    In this book we shall study many different experiments from a probabilistic point of view.
  • 12: Random Walks
    https://stats.libretexts.org/Bookshelves/Probability_Theory/Introductory_Probability_(Grinstead_and_Snell)/12%3A_Random_Walks
    Thumbnail: Random walk in two dimensions. (Public Domain; László Németh via Wikipedia).
  • 11: Markov Chains
    https://stats.libretexts.org/Bookshelves/Probability_Theory/Introductory_Probability_(Grinstead_and_Snell)/11%3A_Markov_Chains
    Modern probability theory studies chance processes for which the knowledge of previous outcomes influences predictions for future experiments. In principle, when we observe a sequence of chance experi...Modern probability theory studies chance processes for which the knowledge of previous outcomes influences predictions for future experiments. In principle, when we observe a sequence of chance experiments, all of the past outcomes could influence our predictions for the next experiment.  In a Markov process, the outcome of a given experiment can affect the outcome of the next experiment. This type of process is called a Markov chain.
  • 11.5: Mean First Passage Time for Ergodic Chains
    https://stats.libretexts.org/Bookshelves/Probability_Theory/Introductory_Probability_(Grinstead_and_Snell)/11%3A_Markov_Chains/11.05%3A_Mean_First_Passage_Time_for_Ergodic_Chains
    In this section we consider two closely related descriptive quantities of interest for ergodic chains: the mean time to return to a state and the mean time to go from one state to another state.
  • 9.3: Central Limit Theorem for Continuous Independent Trials
    https://stats.libretexts.org/Bookshelves/Probability_Theory/Introductory_Probability_(Grinstead_and_Snell)/09%3A_Central_Limit_Theorem/9.03%3A_Central_Limit_Theorem_for_Continuous_Independent_Trials
    We have seen in Section 1.2 that the distribution function for the sum of a large number \(n\) of independent discrete random variables with mean \(\mu\) and variance \(\sigma^2\) tends to look like a...We have seen in Section 1.2 that the distribution function for the sum of a large number \(n\) of independent discrete random variables with mean \(\mu\) and variance \(\sigma^2\) tends to look like a normal density with mean \(n\mu\) and variance \(n\sigma^2\). Let us begin by looking at some examples to see whether such a result is even plausible.
  • 9.2: Central Limit Theorem for Discrete Independent Trials
    https://stats.libretexts.org/Bookshelves/Probability_Theory/Introductory_Probability_(Grinstead_and_Snell)/09%3A_Central_Limit_Theorem/9.02%3A_Central_Limit_Theorem_for_Discrete_Independent_Trials
    We have illustrated the Central Limit Theorem in the case of Bernoulli trials, but this theorem applies to a much more general class of chance processes.
  • 1: Discrete Probability Distributions
    https://stats.libretexts.org/Bookshelves/Probability_Theory/Introductory_Probability_(Grinstead_and_Snell)/01%3A_Discrete_Probability_Distributions

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