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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Statistics Is Easy: Case Studies on Real Scientific Datasets

Statistics Is Easy: Case Studies on Real Scientific Datasets

Shasha, Dennis
Katari, Manpreet Singh
Tyagi, Sudarshini

Paperback

Series: Synthesis Lectures on Mathematics & Statistics

General ComputersGeneral MathematicsProbability & Statistics

ISBN10: 3031013050
ISBN13: 9783031013058
Publisher: Springer Nature
Published: Apr 8 2021
Pages: 62
Weight: 0.32
Height: 0.16 Width: 7.50 Depth: 9.25
Language: English
Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.

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