Who first proposed the word regression, and what are the meanings of regression, write at least four

Regression refers to the study of one set of random variables (y1, y2, yi) and another set (x1, x2,..., xk) is a statistical analysis method for the relationship between variables, also known as multiple regression analysis. Usually y1, y2 ,..., yi is the dependent variable, x1, x2 ,..., xk is the independent variable. Regression analysis is a mathematical model.

Regression refers to the study of one set of random variables (y1, y2, yi) and another set (x1, x2,..., xk) is a statistical analysis method for the relationship between variables, also known as multiple regression analysis. Usually the former is the dependent variable and the latter is the independent variable.

Regression analysis is a mathematical model. It is a special type of linear model when the dependent and independent variables are linear. In its simplest form, univariate linear regression consists of an independent variable and a dependent variable that are roughly linear; The model is y=a+bx+ (x is the independent variable, y is the dependent variable, is the random error).

It is generally assumed that the mean of the random error is 0 and the variance is 2 (2 0, 2 is independent of the value of x). If the random error is further assumed to follow a normal distribution, it is called a normal linear model.

In general, if there are k independent variables and 1 dependent variable, the value of the dependent variable is divided into two parts: one part is influenced by the independent variable, that is, it is expressed as its function, and the form of the function is known and contains unknown parameters; The other part is affected by other unconsidered factors and randomness, i.e., random error.

When the function is a linear function with unknown parameters, it is called a linear regression analysis model. When the function is a nonlinear function with unknown parameters, it is called a nonlinear regression analysis model. When the number of independent variables is greater than 1, it is called multiple regression, and when the number of dependent variables is greater than 1, it is called multiple regression.

The main contents of regression analysis are:

Starting from a set of data, determine the quantitative relationship between certain variables; That is, building mathematical models and estimating unknown parameters. Least squares is usually used.

Test the trustworthiness of these relationships.

In the relationship between multiple independent variables affecting a dependent variable, determine whether the influence of the independent variable is significant, and select the one with significant influence into the model, and eliminate the non-significant variable. Methods such as stepwise regression, forward regression, and backward regression are commonly used.

Use the desired relationship to ** or control a process.

Regression analysis is widely used, and the use of statistical software packages can make various algorithms more convenient.

The main types of regression are: linear regression, curvilinear regression, binary logistic regression, and multivariate logistic regression.

The term "regression" was coined by the famous British statistician Francil Sgalton in 1889, when he studied the relationship between the height of ancestors and offspring, and found that parents who are taller also have taller children, but the average height of these children is not as high as the average height of their parents; Shorter parents, who have shorter children, have shorter children, but the average height of these children is higher than the average height of their parents

1. Linear regression.

It is one of the most well-known modeling techniques. Linear regression is often one of the techniques of choice when people learn a model. In this technique, the dependent variable is continuous, the independent variable can be continuous or discrete, and the nature of the regression line is linear.

2. Logistic regression.

Logistic regression is used to calculate the probabilities of "event=success" and "event=failure". Logistic regression should be used when the type of dependent variable is a binary (1 0, true, false, yes or no) variable.

3. Polynomial regression.

For a regression equation, if the exponent of the independent variable is greater than 1, then it is a polynomial regression equation. In this regression technique, the best-fit line is not a straight line. Rather, it's a curve that fits the data points.

4. Gradual return.

We can use this form of regression when dealing with multiple independent variables. In this technique, the selection of independent variables is done in an automated process that includes non-human manipulation. This feat is to identify important variables by looking at statistical values such as R-Square, T-Stats, and AIC indicators.

Stepwise regression fits a model by simultaneously adding and removing covariates based on specified criteria.

The meaning of regression is as follows:

1.Return, return.

2.Backwards, backwards.

3.Return. 4.Return.

1. Hui pinyin: huí

Interpretation: 1Zigzag Wrapping: Swirling. Patrol. Circuitous. Shaped needles. Peak road turn.

2.from elsewhere to the original place; Also: Home. Village. Send it to the original place.

3.Turn** head. Come over.

4.Reply; Return: Faith. Respect.

5.Return. 6.declined (invitation); Refund (reserved banquet, etc.); Resignation (buddy, helper): All the gifts sent.

7.Refers to the number of things and actions: come one. I've heard about two. That's another story.

8.A paragraph of the storytelling, a chapter back to **: one hundred and twenty copies of "Dream of Red Mansions".

9.Hui: Min.

10.Surname. 11.Tendency verbs. Used after a verb to indicate that a person or thing moves from another place to its original place: to pick up a package from the post office. After reading the book, please put it in its place.

2. Gui Pinyin: guī

Interpretation: 1Return: Overseas Chinese. Homeless.

2.Back; Restitution: The original owner.

3.Tend or concentrate in one place: different paths. Thousands of rivers and seas. Treat issues of the same nature into one category.

4.(Who is responsible): This is the group that manages all chores.

6.Used between the same verbs, it indicates that the action does not cause a corresponding result: criticism criticism, and the bonus is not less than a point.

7.The division of a divisor in the abacus.

8.Surname. Evolution of Chinese characters:

1.**[huí shōu]

Recycle items (mostly scrap or used goods): Waste heat. Waste.

2.家家 [huí jiā].

Homecoming was written by the famous American saxophonist Kenny. Base creation. Its beautiful melody is known to people all over the world. It is precisely because of this piece that most ** lovers understand and are familiar with the instrument of saxophone.

3.Come back [huí lái].

From elsewhere to where he came: he had just come from a foreign country. He went out every morning and only in the evening.

4.Memories [huí yì].

Recall: In the past. Childhood life.

give an explanation of the problem; Comment on Request: Question. Satisfied.

You mean regression analysis in mathematics is not.

Return home.

Motherland. Return to the family.

Return to family. Return home.

Back to nature. Back to the humanities.

Return to simplicity. Return to simplicity.

Return to yourself. Back to square one.

Back to beauty. Return to simplicity.

Return to the end of the line. Back to square one.

Return to the tribe.