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How to calculate 76% × 5 out of 19 + 0.24 × 5 out of 24
76%×5/19+0.24×5/24
=0.76×5/19+0.24×5/24
=0.2+0.05
=0.25
Calculation of operation law of fourth grade problems in primary school: 240 * 23 + 76 * 230
240*23+76*230
=24×230+76×230
=230×（24+76）
=230×100
=23000
Fourth grade primary school recursive equation calculation (can skillfully calculate to skillfully calculate) urgent!
125×（8+40）×25
38×45+45×11+49×55
Question 1: Method 1: (125 × 8) × 25 + 125 × (40 × 25)
=1000 ×25+125×1000
=150000
Method 2: 125 × 48 × 25
=（125×8）×（6×25）
=1000×150
=150000
Question 2: 45 × (38 + 11) + 49 × 55
=49×（45+55）
=49×100
=4900
Calculation of 100 channel breakout
My friend wants to do! Urgent, can add completely!
Not necessarily 100! A little more difficult!
408-12 × 24 (46 + 28) × 60 42 × 50-1715 △ 532 + 105 △ 5 (108 + 47) × 52 420 × (327-238) (4121 + 2389) △ 7 671 × 15-974 469 × 12 + 1492 405 × (3213-3189) 5000-56 × 23 125 × (97-81) 6942 + 480 △ 3 304 × 32-1
The economic meaning of a and B in linear regression equation?
The regression linear equation y = a + BX passing through the fixed point (0, a) represents the average change value of dependent variable y when the independent variable x changes by one unit of measurement. It is mathematically called the slope of the straight line, also known as the regression coefficient. The regression coefficient means that when other factors remain unchanged, the degree of change of the dependent variable caused by the unit change of the independent variable is determined by SS
On the meaning of coefficient of nonlinear regression equation
For example, the linear regression equation y = a + b1x1 + b2x2 + b3x3... Bnxn
So I know, for example, when x2 and X3 are fixed, when X1 is increased by 1 unit, y will increase by B1 units
So for a nonlinear regression equation, for example, I have y = a * X1 ^ B1 * x2 ^ B2 * X3 ^ B3
So how does the coefficient B1, B2, B3 of this nonlinear regression equation explain y? Is it X1 increasing by one unit, y increasing by B1 power?
This is the exponential form. When X1 is doubled, y is doubled to 2 ^ B1
How to solve linear trend equation fitting method (linear regression equation)?
According to the formula, the regression equation of y = ax + B is obtained, and x = 2011 is substituted into x average = (2005 + 2006 + 2007 + 2008 + 2009 + 2010) △ 6y average = (442 + 457 + 471 + 479 + 504 + 582) △ 6A = [(442 × 2005 + 457 × 2006 + 471 × 2007 + 479 × 2008 + 504 × 2009 + 582 × 2010) - 6x average × y average]
What is the meaning of linear regression equation R and R, and what is the size related to
The definition formula of sample correlation coefficient is: sample correlation coefficient r has the following characteristics: & nbsp; & nbsp; & nbsp; 1. The value of R is between - 1 and 1. When r = 0, there is no linear relationship between X and y
If the regression coefficient b = 0, the correlation index ()
A. R = 1b. R = 0C. R = - 1D
In the calculation formula of regression coefficient b, their molecules are the same as those of correlation index, so B
Derivation process of a, B coefficients of linear regression equation
It's how to get the coefficients of a and B from the linear regression equation. It's better to see the screenshot of word clearly in this way,
We assume that there is no error in the abscissa (for the sample designed by ourselves), so we think that the error completely appears in the ordinate, that is, the measured value. So we only need to get the minimum distance between the point on the fitting line and the ordinate value of the sample. We think that the line is closest to all points
Let the regression line be y = MX + B. any point is (Xi, Yi), I is a running mark, which means any value. That is to find the minimum distance from the point (Xi, Yi) to the point (Xi, MXI + b) on the fitting line with the same abscissa of the point. So the distance is the ordinate subtraction, that is, d = | y-yi | = | MXI + b-yi |. If the absolute value is not easy to calculate, it will be squared. There is d ^ 2 = (MXI + b-yi) ^ 2. Now add all the distances
That is, Σ (I = 1, n), from 1 to the nth, (I will not write too hard). ∑ d ^ 2 = ∑ (MXI + b-yi) ^ 2
Let d ^ 2 be the partial derivative of M and B, because you should have learned that the derivative should be equal to 0
For m, M is the slope, the slope is considered as a variable, others are regarded as constants
Σ[2*(mXi+b-Yi)Xi]=0,
Expand m ∑ Xi ^ 2 + B ∑ Xi - ∑ Xiyi = 0, solve B = (∑ yi-m ∑ XI) / N, n represents the total number of points, is the algebraic budget, try it yourself
For B, the partial derivative is obtained,
The solution is m ∑ Xi + Nb = ∑ Yi
We solve the simultaneous equations m and B,
m=(nΣXiYi-ΣXiΣYi) / (nΣXi^2-(ΣXi)^2)
b=(ΣYi-mΣXi)/n
Because the sum of ∑ Xi equals n times the average
So if you continue to deform, there will be
The formula in the second link of hjg3604. I won't write it. It's too hard to type
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