One of the assignments in college is Calculating Regression Equations and Discussing

Selectivity S% = (Yield of the target product and conversion rate) * 100%.

Conversion rate ca% = the amount of reactant A converted into du The total amount of reactant A flowing through the catalytic bed feed.

Yield = DAO Selectivity * Conversion Rate.

Yield = Actual Yield Theoretical Yield.

Yield = (amount of target product generated and starting amount of key components) 100%.

According to the steps of solving ordinary differential equations, the initial solution of this ordinary differential equation is first solved.

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A regression straight line equation is a straight line that best reflects the relationship between x and y between the data (x and y) of a set of variables that have a correlation. The values of a and b can be calculated according to the following regression straight line formula.

And we want one of them to best reflect the relationship between x and y, that is, we want to find a straight line so that this straight line is "closest to" a known data point, and we write this straight line equation as (as shown on the right, denoted as equation) here with a notation above y " " " in order to distinguish the actual value y of y, which means that when x takes the value xi=1,2,......6), the corresponding observed value of y is yi, and the ordinate corresponding to yi on the line is the equation of the regression line of y versus x, the corresponding line is called the regression line, and b is called the regression coefficient.

Regression to the method of finding a straight line.

Least Squares:

The total dispersion cannot be the sum of n dispersions.

to denote it, usually in terms of the sum of the squares of the dispersion, ie.

As the total dispersion, and make it to a minimum, so that the regression line is the one where q takes the minimum value of all straight lines, this method of making the "sum of squares of the dispersion minimized" is called least squares:

Since the absolute value makes the calculation constant, in practical applications people prefer to use: q=(y1-bx1-a) +y2-bx-a )+yn-bxn-a).

In this way, the problem boils down to the fact that q is the smallest when a,b is taken, i.e., the "overall distance" to the point line y=bx+a is the smallest.

To find a,b in a regression linear equation using least squares, there is the following formula:

b=x1y1+x2y2+……xnyn-nx'(the average of x) y'(y's average) (x1) 2+(x2) 2+......xn)^2-n(x'(the average of x) 2

a=y'(mean of y) -bx'(the average of x).

According to the sample, the x,y mean, variance, and dispersion are found and brought into the formula to solve the system of equations about ab.

You can use Excel's intercept function to get the intercept b, and the slope function to get the slope a

1.Seek B first, then A.

That is, the numerator lxy is the value of x minus the mean of x, multiplied by the value of y corresponding to this x minus the mean of y, and finally the product of the two differences under each x is added up, (xi-x)(yi-y), i is taken from 1 to n.

The denominator is (xi-x), take care to square first and then sum it.

Finally, ay (mean) - bx (mean).

That's how I memorized this formula, and I should remember it by looking at two more examples.