covariance 中文 - Writing Clip Art

covariance 中文

I’m still not sure how covariance is a real word. I’ve heard it when I’m talking to my students or in class and I’ve also heard it when I’m talking to my mom. It is an informal term referring to two or more things being the same. In this article are some definitions of covariance […]

I’m still not sure how covariance is a real word. I’ve heard it when I’m talking to my students or in class and I’ve also heard it when I’m talking to my mom. It is an informal term referring to two or more things being the same. In this article are some definitions of covariance that I have found very helpful when I’m trying to explain it to people.

Covariance is a property of two or more concepts being the same even though they are different and different concepts are themselves not the same. Covariance can be thought of as the condition of being the same. For example, the three of us sitting on the couch watching TV is covariant with the three of us sitting at the same table eating dinner. Covariance can also be thought of as the condition of being the same thing.

Covariance is something I’ve seen people talk about a lot on Twitter, and it’s a topic I’ve been trying to wrap my brain around. I’ve been working on a paper about covariance recently and would love to hear what you think.

Covariance is a condition that occurs when two or more things are the same. For example, if the room is the same size, there is covariance between the chairs on the couch and the table on my coffee table. Covariance is also a condition of being the same thing. For example, if two chairs are the same height and the table is the same height, there is covariance between the chairs and the table.

Covariance occurs when the same thing (or things) both have the same characteristics. For example, if I have the same chair, then the table and the chair are both the same height and the table and the chair have the same width.

Covariance is also one of those terms that you know you will use in the future, but it also tends to come up in conversation that you never used in the past. For example, if I asked you what your favorite food is, chances are that you will instantly mention your favorite sandwich. But if we really wanted to know what your favorite food is, we would need to ask you what your favorite sandwich is, because your favorite sandwich is the same sandwich that you both like.

Covariance is a generalization of covariance that was coined by statistician, William I. Welch (1890–1963) in his book “Covariance and its Applications” (1932). It was used to describe the phenomenon of correlation between two variables; two variables that are not themselves independent but are correlated. For example, if you take two boxes of apples and put them in a refrigerator, the apples will probably be the same in both boxes.

You can think of covariance as a linear relationship, where the covariance between two variables is the correlation that exists between the two variables. That’s the same as saying that if you take two boxes of apples and put them in a refrigerator, the apples in one refrigerator will be the same apples as the apples in the other refrigerator. The covariance between two variables is the correlation between the two variables.

Covariance is one of the most important statistics to know about. Covariance is one of the most important statistics to know about. Covariance reveals the relationship between two or more variables (in this case between two variables). Covariance is usually used in the context of linear regression, where a more complex relationship is analyzed. For example, suppose you had two variables x and y, and you wanted to find the average relationship between x and y.

Covariance reveals the relationship between two variables. Covariance is one of the most important statistics to know about. Covariance is one of the most important statistics to know about. Covariance reveals the relationship between two or more variables in this case between two variables. Covariance is usually used in the context of linear regression, where a more complex relationship is analyzed.

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