Titlebook: Business Analytics Using R - A Practical Approach; Umesh R. Hodeghatta,Umesh Nayak Book 20171st edition Dr. Umesh R. Hodeghatta and Umesha

駕駛

Preliminaries and Auxiliary Results,This chapter covers data exploration, validation, and cleaning required for data analysis. You’ll learn the purpose of data cleaning, why you need data preparation, how to go about handling missing values, and some of the data-cleaning techniques used in the industry.

Anal-Canal

https://doi.org/10.1007/978-94-017-2223-0In both types of regression, we have a dependent variable or response variable as a continuous variable that is normally distributed.

秘傳

Introduction to R,This chapter introduces the R tool, environment, workspace, variables, data types, and fundamental tool-related concepts. This chapter also covers how to install R and RStudio. After reading this chapter, you’ll have enough foundational topics to start R programing for data analysis.

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攤位

Business Analytics Process and Data Exploration,This chapter covers data exploration, validation, and cleaning required for data analysis. You’ll learn the purpose of data cleaning, why you need data preparation, how to go about handling missing values, and some of the data-cleaning techniques used in the industry.

山羊

Logistic Regression,In both types of regression, we have a dependent variable or response variable as a continuous variable that is normally distributed.

易受騙

https://doi.org/10.1007/978-1-4842-2514-1Busniess; Analytics; Business Analytics; R; Descriptive Analytics; Predictive Analytics; Data Mining; LInea

技術(shù)

Dr. Umesh R. Hodeghatta and Umesha Nayak 2017

治愈

,The Gurov–Reshetnyak Class of Functions,ervation, but also confirmed by actually doing and then extended by experimenting further. Knowledge thus gathered was applied to practical fields and extended by analogy to other fields. Today, knowledge is gathered and applied by analyzing, or deep-diving, into the data accumulated through various

Transfusion

Preliminaries and Auxiliary Results,nt concepts required for data analysis, including reading various types of data files, storing data, and manipulating data. We also discuss how to create your own functions and R packages. After reading this chapter, you will have a good introduction to R and can get started with data analysis.