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2: Descriptive Statistics

  • Page ID
    469804
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    • 2.1: Measures of Center
      Both graphical and numerical methods of summarizing data make up the branch of statistics known as descriptive statistics. Later, descriptive statistics will be used to estimate and make inferences about population parameters using methods that are part of the branch called inferential statistics. This section introduces numerical measurements to describe sample data.
    • 2.2: Measures of Spread
      Variability describes how the data are spread out. If the data are very close to each other, then there is low variability. If the data are very spread out, then there is high variability. How do you measure variability? It would be good to have a number that measures it. This section will describe some of the different measures of variability, also known as variation.
    • 2.3: Confidence Intervals
      Finally, we will often use a confidence interval as a more detailed estimate of spread - taking sample size, standard deviation and mean into account.
    • 2.4: Statistical Methods for Normal Distributions
      The most common distribution for our results is a normal distribution. Because the area between any two limits of a normal distribution curve is well defined, constructing and evaluating significance tests is straightforward.
    • 2.5: Python Basics for Analytical Chemists
      Introduction to basic concepts in programming - simple operators, variables, iteration and conditionals
    • 2.6: Chapter 3 Formulas
    • 2.7: Chapter 3 Exercises


    This page titled 2: Descriptive Statistics is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Rachel Webb via source content that was edited to the style and standards of the LibreTexts platform.

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