000			a
999			_c33772 _d33772
008			250306b xxu||||| |||| 00| 0 eng d
020			_a9783031013058
082			_a519.5 _bKAT
100			_aKatari, Manpreet Singh
245			_aStatistics is easy : case studies on real scientific datasets
260			_bSpringer, _c2021 _aCham :
300			_axi, 62 p. ; _bill., (chiefly col.), _c24 cm
365			_b19.99 _c€ _d93.20
490			_aSynthesis lectures on mathematics and statistics ; _v39
504			_aIncludes bibliographical references.
520			_aComputational 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.
650			_aNonparametric statistics
650			_aWeight Change
650			_aDiet
650			_aBreast cancer
650			_aRegression analysis
650			_aDecision tree
650			_aRandom Forest classifier
650			_aRNA-seq Set
700			_aTyagi, Sudarshini
700			_aShasha, Dennis Elliott
942			_2ddc _cBK