\u003ch4\u003eBook Description\u003c/h4\u003eIf you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.\u003ch4\u003eWhat you will learn\u003c/h4\u003e\u003cul\u003e\u003cli\u003eMaster data management in R\u003c/li\u003e\u003cli\u003ePerform hypothesis tests using both parametric and nonparametric methods\u003c/li\u003e\u003cli\u003eUnderstand how to perform statistical modeling using linear methods\u003c/li\u003e\u003cli\u003eModel nonlinear relationships in data with kernel density methods\u003c/li\u003e\u003cli\u003eUse matrix operations to improve coding productivity\u003c/li\u003e\u003cli\u003eUtilize the observed data to model unobserved variables\u003c/li\u003e\u003cli\u003eDeal with missing data using multiple imputations\u003c/li\u003e\u003cli\u003eSimplify highdimensional data using principal components, singular value decomposition, and factor analysis\u003c/li\u003e\u003c/ul\u003e\u003ch4\u003eWho this book is for\u003c/h4\u003eIf you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.
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