標(biāo)題: Titlebook: Biostatistics with R; An Introduction to S Babak Shahbaba Book 2012 Springer Science+Business Media, LLC 2012 Biostatistics.R applications [打印本頁] 作者: Goiter 時間: 2025-3-21 18:40
書目名稱Biostatistics with R影響因子(影響力)
書目名稱Biostatistics with R影響因子(影響力)學(xué)科排名
書目名稱Biostatistics with R網(wǎng)絡(luò)公開度
書目名稱Biostatistics with R網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Biostatistics with R被引頻次
書目名稱Biostatistics with R被引頻次學(xué)科排名
書目名稱Biostatistics with R年度引用
書目名稱Biostatistics with R年度引用學(xué)科排名
書目名稱Biostatistics with R讀者反饋
書目名稱Biostatistics with R讀者反饋學(xué)科排名
作者: 隱語 時間: 2025-3-21 22:11 作者: 向下 時間: 2025-3-22 01:48 作者: 翻布尋找 時間: 2025-3-22 07:10
Probability,ainty. In?statistical analysis of data, probability is the mathematical tool we use to quantify the extent of our uncertainty. In this chapter, we discuss some general rules of probability. We start by defining random events and then talk about assigning a probability to each event to reflect our un作者: Obverse 時間: 2025-3-22 10:57 作者: Mettle 時間: 2025-3-22 16:29
Estimation,cess of finding unknown parameters based on the representative sample obtained from the population is called estimation. Estimating parameters helps us to evaluate hypotheses, predict unknown values of random variables, and make decisions. In this chapter, we start by discussing point estimation, wh作者: aplomb 時間: 2025-3-22 18:41 作者: 小平面 時間: 2025-3-23 00:01
Statistical Inference for the Relationship Between Two Variables,ship between two variables. We start our discussion by focusing on situations where one variable is numerical and the other variable is binary. Typically, the numerical variable, called the response variable, is regarded as the primary variable of interest that captures a specific characteristic of 作者: 我的巨大 時間: 2025-3-23 05:05
Analysis of Variance (ANOVA),f interest is divided into multiple groups. The response variable is numerical as before. Analysis of variance (ANOVA) is a method of comparing the means of the response variable across different groups specified by the factor variable. Our primary focus in this chapter is on problems where there is作者: troponins 時間: 2025-3-23 08:27 作者: 擴大 時間: 2025-3-23 12:55 作者: Bouquet 時間: 2025-3-23 17:33 作者: BANAL 時間: 2025-3-23 20:53
Bayesian Analysis, the basics of Bayesian analysis. Here, we use Bayesian inference regarding the population proportion as a simple example to discuss some basic concepts of Bayesian methods. We briefly discuss prior and posterior probability distributions. Prior probability distributions reflect our knowledge regard作者: nocturia 時間: 2025-3-23 22:44
2197-5736 scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text. .978-1-4614-1301-1978-1-4614-1302-8Series ISSN 2197-5736 Series E-ISSN 2197-5744 作者: Bumptious 時間: 2025-3-24 04:40 作者: Malfunction 時間: 2025-3-24 06:55 作者: 不公開 時間: 2025-3-24 14:08
Learning Technology for Education Challenges concepts is essential for learning the topics provided in the remaining parts of this book. More specifically, we talk about hypothesis testing vs. prediction, defining the target population, sampling data, observational studies vs. experiments, and statistical inference. The high-level road map of作者: finale 時間: 2025-3-24 15:25 作者: ostrish 時間: 2025-3-24 22:42 作者: Invigorate 時間: 2025-3-25 00:54
Sabine Siemsen,William Yu Chung Wangainty. In?statistical analysis of data, probability is the mathematical tool we use to quantify the extent of our uncertainty. In this chapter, we discuss some general rules of probability. We start by defining random events and then talk about assigning a probability to each event to reflect our un作者: venous-leak 時間: 2025-3-25 05:01
Learning Technology for Education Challengeses is crucial for performing statistical inference, which is discussed in the following chapters. In this chapter, we first talk about defining random variables for some underlying random events (discussed in the previous chapter). For each random variable, we assume a probability distribution, whic作者: anthesis 時間: 2025-3-25 10:07 作者: MUMP 時間: 2025-3-25 14:33 作者: 會犯錯誤 時間: 2025-3-25 17:56
Online Examination – A Case Studyship between two variables. We start our discussion by focusing on situations where one variable is numerical and the other variable is binary. Typically, the numerical variable, called the response variable, is regarded as the primary variable of interest that captures a specific characteristic of 作者: 大方一點 時間: 2025-3-25 20:36 作者: Throttle 時間: 2025-3-26 02:37 作者: PALL 時間: 2025-3-26 04:41
Michal ?ura?ík,Emil Kr?ák,Patrik Hrkúting the unknown value of the response variable. In this chapter, we discuss linear regression models, which are simple yet extremely useful. The underlying assumption of these models is that the overall relationship between explanatory variables and response variable is linear. We start our discussi作者: LEVER 時間: 2025-3-26 11:42 作者: Anemia 時間: 2025-3-26 13:14
Lorna Uden,Dario Liberona,Jozef Ristvej the basics of Bayesian analysis. Here, we use Bayesian inference regarding the population proportion as a simple example to discuss some basic concepts of Bayesian methods. We briefly discuss prior and posterior probability distributions. Prior probability distributions reflect our knowledge regard作者: CRP743 時間: 2025-3-26 19:14 作者: 國家明智 時間: 2025-3-26 23:55
978-1-4614-1301-1Springer Science+Business Media, LLC 2012作者: phlegm 時間: 2025-3-27 04:43 作者: Organization 時間: 2025-3-27 05:32 作者: 失敗主義者 時間: 2025-3-27 12:33
Regression Analysis,on by focusing on situations where there is only one explanatory variable. We refer to these models as simple linear regression models. We then extend simple linear regression models to multiple linear regression models, where there are two or more explanatory variables.作者: 軍火 時間: 2025-3-27 15:55 作者: crease 時間: 2025-3-27 18:06 作者: 教唆 時間: 2025-3-27 23:18
Statistical Inference for the Relationship Between Two Variables, investigate the difference between the two groups with respect to the characteristic represented by the numerical variable. Next, we discuss situations where both variables are binary. Finally, we talk about the situations where both variables are numerical.作者: mosque 時間: 2025-3-28 04:10
Book 2012ls. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text. .作者: Fatten 時間: 2025-3-28 09:00
2197-5736 ated by real examples.Intended for a wide range of readers.I.Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The develop作者: 羞辱 時間: 2025-3-28 14:26 作者: 澄清 時間: 2025-3-28 17:41
Vanessa Lauermann,Débora N. F. Barbosarvations in two different groups. We define similarity using some appropriate distance measures. Then, we discuss two main classes of clustering methods, namely, .-means clustering and hierarchical clustering.作者: 放棄 時間: 2025-3-28 19:25
Exploring Relationships,We start by discussing situations where we are interested in the relationship between two numerical variables. Next, we talk about techniques for investigating the relationship between two categorical variables. Finally, we discuss situations where one variable is numerical and the other one is categorical.作者: VAN 時間: 2025-3-29 02:48
Clustering,rvations in two different groups. We define similarity using some appropriate distance measures. Then, we discuss two main classes of clustering methods, namely, .-means clustering and hierarchical clustering.作者: apropos 時間: 2025-3-29 04:20
Online Examination – A Case Study only one categorical factor defining the groups. We refer to the ANOVA method for such problems as one-way ANOVA. Next, we briefly discuss two-way ANOVA methods, where the groups are defined based on two factors (i.e., categorical variables).作者: 翻布尋找 時間: 2025-3-29 07:30
Michal ?ura?ík,Emil Kr?ák,Patrik Hrkúton by focusing on situations where there is only one explanatory variable. We refer to these models as simple linear regression models. We then extend simple linear regression models to multiple linear regression models, where there are two or more explanatory variables.作者: surmount 時間: 2025-3-29 12:40
Lorna Uden,Dario Liberona,Jozef Ristvejing the possible values of unknown parameters (e.g., population proportion) before observing data. Posterior probability distributions reflect our updated knowledge about unknown parameters after observing data. We show how posterior probability distributions are used to estimate parameters and perform hypothesis testing.作者: ETCH 時間: 2025-3-29 16:34 作者: concentrate 時間: 2025-3-29 20:52
Sabine Siemsen,William Yu Chung Wangecifically, we divide variables into categorical and numerical. Distinguishing these two types of variables is important because the summary statistics and data visualization techniques appropriate for a variable usually depend on the type of that variable. This chapter also provides some discussion on data preprocessing.作者: Cloudburst 時間: 2025-3-30 00:51 作者: commonsense 時間: 2025-3-30 04:26
Learning Technology for Education Challengesa random variable depends on its type. We divide random variables into discrete and continuous based on the type values they take and whether these values are countable. We then talk about some commonly used discrete and continuous probability distributions.
