標(biāo)題: Titlebook: Business Analytics Using R - A Practical Approach; Umesh R. Hodeghatta,Umesh Nayak Book 20171st edition Dr. Umesh R. Hodeghatta and Umesha [打印本頁] 作者: ETHOS 時間: 2025-3-21 18:05
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作者: Alveoli 時間: 2025-3-21 23:31
,The Gurov–Reshetnyak Class of Functions,ous storage and extended computing capabilities of the cloud, especially, have ensured that knowledge can be quickly derived from huge amounts of data and also can be used for further preventive or productive purposes. This chapter provides you with the basic knowledge of where and how business analytics is used.作者: 大氣層 時間: 2025-3-22 04:24
https://doi.org/10.1007/978-94-017-6101-7 the market, and so on. These factors may be a little difficult to gauge, but the past success record of the promoters can be easily found from the available market data. If the promoter had started ten ventures and eight were successful, we can say that 80% is the success rate of the promoter.作者: COMA 時間: 2025-3-22 06:39
Overview of Business Analytics,ous storage and extended computing capabilities of the cloud, especially, have ensured that knowledge can be quickly derived from huge amounts of data and also can be used for further preventive or productive purposes. This chapter provides you with the basic knowledge of where and how business analytics is used.作者: ostensible 時間: 2025-3-22 12:38
Simple Linear Regression, the market, and so on. These factors may be a little difficult to gauge, but the past success record of the promoters can be easily found from the available market data. If the promoter had started ten ventures and eight were successful, we can say that 80% is the success rate of the promoter.作者: Loathe 時間: 2025-3-22 14:39 作者: ECG769 時間: 2025-3-22 19:02 作者: 值得贊賞 時間: 2025-3-23 00:43 作者: 動機(jī) 時間: 2025-3-23 02:47
https://doi.org/10.1007/978-3-540-74709-3This 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.作者: Allege 時間: 2025-3-23 05:47
https://doi.org/10.1007/978-3-540-74709-3Imagine you are traveling and have just reached the bank of a muddy river, but there are no bridges or boats or anybody to help you to cross the river. Unfortunately, you do not know to swim. When you look around in this confused situation where there is no help available to you, you notice a sign board as shown in Figure 4-1:作者: 駕駛 時間: 2025-3-23 11:52
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.作者: Anal-Canal 時間: 2025-3-23 14:57
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.作者: 秘傳 時間: 2025-3-23 19:09
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.作者: figure 時間: 2025-3-23 22:13 作者: 攤位 時間: 2025-3-24 05:47
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.作者: 山羊 時間: 2025-3-24 08:02
Logistic Regression,In both types of regression, we have a dependent variable or response variable as a continuous variable that is normally distributed.作者: 易受騙 時間: 2025-3-24 10:46
https://doi.org/10.1007/978-1-4842-2514-1Busniess; Analytics; Business Analytics; R; Descriptive Analytics; Predictive Analytics; Data Mining; LInea作者: 技術(shù) 時間: 2025-3-24 18:12
Dr. Umesh R. Hodeghatta and Umesha Nayak 2017作者: 治愈 時間: 2025-3-24 23:04
,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作者: Transfusion 時間: 2025-3-25 02:34
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.作者: circuit 時間: 2025-3-25 06:30
,The Gurov–Reshetnyak Class of Functions,(or discrete values), whereas regression and other models predict continuous valued functions. For example, a classification model may be built to predict the results of a credit-card application approval process (credit card approved or denied) or to determine the outcome of an insurance claim. Man作者: parsimony 時間: 2025-3-25 11:24 作者: 華而不實 時間: 2025-3-25 15:34
https://doi.org/10.1007/978-94-017-6101-7 for in the company you want to invest in? Maybe the innovativeness of the products of the startups, maybe the past success records of the promoters. In this case, we say the profitability of the venture is dependent on or associated with innovativeness of the products and past success records of th作者: 最有利 時間: 2025-3-25 19:51
https://doi.org/10.1007/978-94-017-6101-7te variable, you use a different regression method. If the response variable can take values such as yes/no or multiple discrete variables (for example, views such as strongly agree, agree, partially agree, and do not agree), you use logistic regression. You will explore logistic regression in a sep作者: 多山 時間: 2025-3-25 23:43
https://doi.org/10.1007/978-94-017-2223-0itutions have woken up to the value of data and are trying to collate data from various sources and mine it for its value. Businesses are trying to understand consumer/market behavior in order to get the maximum out of each consumer with the minimum effort possible. Fortunately, these organizations 作者: ETHER 時間: 2025-3-26 02:28
R for Data Analysis,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.作者: 表狀態(tài) 時間: 2025-3-26 08:14 作者: 做事過頭 時間: 2025-3-26 11:51 作者: lavish 時間: 2025-3-26 13:55
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.作者: 凝結(jié)劑 時間: 2025-3-26 18:02 作者: 贊美者 時間: 2025-3-26 20:59
https://doi.org/10.1007/978-94-017-6101-7te variable, you use a different regression method. If the response variable can take values such as yes/no or multiple discrete variables (for example, views such as strongly agree, agree, partially agree, and do not agree), you use logistic regression. You will explore logistic regression in a separate chapter.作者: 出血 時間: 2025-3-27 01:08 作者: 鴿子 時間: 2025-3-27 06:43
http://image.papertrans.cn/b/image/192030.jpg作者: 小故事 時間: 2025-3-27 10:23 作者: Tailor 時間: 2025-3-27 16:38 作者: Jingoism 時間: 2025-3-27 21:49
,Big Data Analysis—Introduction and Future Trends,ed for thinking the value of it and use it to tell them the value the data has for them. Data is made to learn from itself and thus throw light on many that have been hitherto unknown or possibly a logically thinking person may not think of or accept at face value.作者: graphy 時間: 2025-3-28 00:06 作者: GIST 時間: 2025-3-28 04:56
Overview of Business Analytics,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作者: ASSAY 時間: 2025-3-28 06:15 作者: 凝乳 時間: 2025-3-28 10:51
,Supervised Machine Learning—Classification,(or discrete values), whereas regression and other models predict continuous valued functions. For example, a classification model may be built to predict the results of a credit-card application approval process (credit card approved or denied) or to determine the outcome of an insurance claim. Man作者: 整潔 時間: 2025-3-28 18:33 作者: 小口啜飲 時間: 2025-3-28 22:37 作者: Hallowed 時間: 2025-3-29 00:32 作者: Instantaneous 時間: 2025-3-29 04:59
,Big Data Analysis—Introduction and Future Trends,itutions have woken up to the value of data and are trying to collate data from various sources and mine it for its value. Businesses are trying to understand consumer/market behavior in order to get the maximum out of each consumer with the minimum effort possible. Fortunately, these organizations
