coursera statistical inference

The most common and multidimensional stochastic variables in data analysis work, the fourth chapter introduces the basic knowledge of multidimensional stochastic variables, in which the core concept is conditional distribution and conditional probability. Conditional distribution and conditional probability can abstract the concept of conditional expectation, and in the study of stochastic analysis, it is "conditional expectation" to understand the theory and keywords of stochastic integral and

The two most important thinking paradigms in scientific research are "simplification" and "reduction", so-called "simplification" refers to the understanding of the world according to the less complicated and understandable laws. The so-called " reduction " means that any complex phenomenon can be explained in the final analysis by a few simple mechanisms. All kinds of statistical distribution families are the result of "simplifying" thought abstracti

The stat2.3x inference (statistical inference) course was taught at the EdX platform by the University of California, Berkeley (University of California, Berkeley) in 2014.Download PDF Note (academia.edu)Summary Test of Hypotheses $$\text{null}: h_0$$ $$\text{alternative}: h_a$$ Assuming the Null is true, the chance of Gett ing data like the "The data in

With the first four chapters of knowledge, the fifth chapter entered the topic of statistical research-the study of the sample. Sample can be said to be the most basic object in the study of statistics, the mathematical nature of the sample is also the most important research topic, the major task of statistics is to extract valuable knowledge from a lot of samples, just as the study of atoms and molecules is chemical. Here is the mind map of this cha

In layman's eyes, one of the things statisticians often do is to put together a bunch of assorted data, figure out a few inexplicable numbers, and then deduce a seemingly plausible conclusion from these numbers, just as alchemists used "Sage's stone" to turn a heap of stones into gold. The sixth chapter, should be the most abstract chapter of the book, is to introduce the statistics of "sage stone"-the principle of data simplification. From the point of view of information, the sample contains a

The stat2.3x inference (statistical inference) course was taught at the EdX platform by the University of California, Berkeley (University of California, Berkeley) in 2014.Download PDF Note (academia.edu)SummaryDependent Variables (paired samples) SD of the difference is $$\sqrt{\sigma_x^2+\sigma_y^2-2\cdot r\cdot\sigma_x\cdot\sigma_y}$$ where $r $ is th

The stat2.3x inference (statistical inference) course was taught at the EdX platform by the University of California, Berkeley (University of California, Berkeley) in 2014.Download PDF Note (academia.edu)SummaryChi-Square test Random sample or Not/good or bad $ $H _0: \text{good model}$$ $ $H _a: \text{not Good model}$$ Based on the expected

Guidance:Chapter 6 sampling Inference　　 　　I. Parameters and statistics The number of parameters that describe the overall distribution;A statistical value refers to the number of samples.For example, if the average age of a class is 22 years, the average age is a parameter of the overall team. 10 students in the class are chosen to learn about their age, the average age of 10 students is 21.5 years, and 21.