Production Cost Method Of Financial Accounting Method

< p > fluctuant cost is closely related to the change of the output of the product, the output of the product increases, the variable cost increases, the output of the product decreases, and the variable cost decreases.

It represents a variable in total cost.

Based on this relationship, the linear equation of product cost can be established: < /p >

< p > y=a BX < /p >

< p >: Y - total cost; < /p >

< p > A - fixed cost; < /p >

< p > b - unit variable cost; < /p >

< p > x - product yield; < /p >

< p > BX - total variable cost.

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< p > the total variable cost varies with the increase or decrease of output.

Using historical data, we use certain methods to determine the value of a and B, and predict the total cost of products under any output, that is, the historical cost method.

The methods of determining a and b values in the formula include high and low point method, scatter plot method and regression analysis method.

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< p > the specific method of production cost method < /p >

< p > < strong > 1, < a href= "http:// www.91se91.com/news/index_c.asp" > high and low point method < /a > /strong > /p >

< p > high and low point method is to select the highest and lowest point of product output and the highest and lowest point of product cost from the record data of product output and product cost in a certain period and related range. Based on this, the variable cost of unit product B is calculated, then the fixed cost a is determined according to the total cost and unit variable cost, and the cost equation is established to determine the future cost.

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< p > high and low point method is relatively simple. However, since only two sets of data are relied on, if the data selected can not reflect the normal production and operation activities of enterprises, the calculation results will also have great errors.

Therefore, according to historical data, when calculating a and b values by mathematical methods, we should pay attention to actual investigation and study, take into account the factors that affect the cost changes in the budget period, adjust the A and b values appropriately, and calculate the total cost and the single location cost according to the planned production period.

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< p > < strong > 2, < a href= "http:// www.91se91.com/news/index_c.asp" > scatter diagram < /a > /strong > /p >

< p > scatter plot method is based on a number of historical data to draw cost maps for each period, draw a straight line that reflects the average trend of cost changes among different points, and the intercept of a straight line is fixed cost, and then calculate the unit variable cost, and then get the method of product cost fixed in the unit and unit variable cost forecast period.

Its working procedures are: < /p >

< p > 1) collecting historical data < /p >

< p > collect historical data of output and total cost in previous periods.

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< p > 2) draw scatter diagram < /p >.

< p > the total cost data of each period are marked into the rectangular coordinate system and the scatter diagram is drawn.

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< p > 3) determine the fixed cost average value < /p >.

"P >" according to the trend of the variation of visual cost with the output of the discrete historical cost point, a line that can reflect the average change trend of the cost is drawn, and the intersection point of the straight line and the longitudinal axis is the average value of the fixed cost.

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< p > 4) calculating unit variable cost < /p >

< p > take a point in the straight line, calculate the unit variable cost according to the formula.

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< p > 5) the total cost of calculation is < /p >.

< p > according to the calculated fixed cost average (a) and unit variable cost (b), the total cost of future production is predicted. The prediction formula is: < /p >

< p > y=a BX < /p >

< p >: Y - the total cost of the future forecast period; < /p >

< p > x - yield.

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< p > < strong > 3, < a href= "http:// www.91se91.com/news/index_c.asp" > regression analysis < /a > /strong > /p >

< p > regression analysis is a mathematical statistical method to study the relationship between variables.

Based on a series of historical cost data, it calculates the intercept and slope of the straight line that represents the average cost level by using the mathematical least square principle, and uses it as the fixed cost and variable cost, and then carries out the cost prediction method.

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< p > using regression analysis, we also need several sets of observation data. All the observation data can be replaced by visual calculation by calculation, and the A and b values and the straight line agenda are determined. This is different from the scatter plot method.

The least squares method is used to determine the regression line, and the minimum square sum of the distance between the observation points and this line is required.

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< p > based on the historical data of y=a BX and previous n (n), we can establish a set of simultaneous equations that determine the regression line: < /p >

< p > first, the above agendas are expressed in the form of observations. The formula is as follows: < /p >

< p >, and then weighted the output of the upper and left sides, and concluded that: < /p >

< p > according to the above two procedures, after pformation, the formula for calculating a and b values is directly listed.

That is: < /p >

< p > with the formula of a and b value, the values of a and B can be obtained by substituting all other relevant values into the formula, and then the straight line equation can be obtained.

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< p > the y value calculated according to the regression equation is just an average. This value is only an approximate value. It has a certain error with the actual value.

The smaller the error, the more reliable the data is obtained.

The magnitude of the error depends on the degree of correlation between the linear relationship between Y and X in the regression equation. The higher the correlation is, the smaller the error.

The correlation coefficient is usually measured by R2. The greater the measurement coefficient, the higher the correlation degree and the smaller the error. The smaller the measurement coefficient, the lower the correlation degree and the greater the error.

The formula for determining the coefficient is as follows: < /p >

< p >: Yi - actual product cost per month; < /p >

< p > -- the average of actual product cost; < /p >

< p > - the monthly cost of regression line; < /p >

< p > SSR - the square sum of the regression deviation; < /p >

< p > SST - the sum of squares of deviations.

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< p > from the above formula, we can see that the greater the proportion of the sum of squares of the regression deviations in the sum of squares of the deviations, the greater the R2 value.

This shows that the stronger the correlation between X and Y and the smaller the error, the more reliable the straight line equation is.

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< p > enterprises in a variety of financial accounting models, according to their own financial situation to choose their own financial accounting methods, so as to help enterprises continue to develop.

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