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范文一：公摊系数的计算公式为
公摊系数的计算公式为:公摊系数,公摊面积,套内建筑面积。 然后，各套(单元)的套内建筑面积乘以公摊系数，就能得到购房者应合理分摊的分用建筑面积。即:每一户分摊的公用建筑面积,公摊系数,套内建筑面积.
计算公摊系数时要保留小数字后6位。最后的产权证上的面积即房层建筑面积(套内建筑面积，公摊建筑面积)保留两位小数。
公摊系数就是一套房子套内建筑面积和你这整幢楼房总建筑面积之比
成套房屋的套内建筑面积由套内房屋使用面积，套内墙体面积，套内阳台建筑面积三部分组成。
1、套内的使用面积(GB/T 17986.1-2000 B1.2):
套内房屋使用面积为套内房屋使用空间的面积，以水平投影面积按以下规定计算:
a.套内房屋使用面积为套内卧室、起居室、过厅、过道、厨房、卫生间、厕所、储藏室、壁橱等空间面积的总和。
b.套内内部楼梯按自然层数的面积总和计入使用面积。
c.不包括含在结构面积内的套内内部烟囱、通风道、管道井均计入使用面积。
d.内墙面装饰厚度计入使用面积。
2、套内墙体面积(GB/T 17986.1-2000 B1.3):
套内墙体面积是套内使用空间周围的维护或承重墙体或其他承重支撑体所占的面积，其中各套之间的分隔墙和套与公共建筑空间的分隔以及外墙(包括山墙)等共有墙，均按水平投影面积的一半计入套内墙体面积。套内自由墙体按水平投影面积全部计入套内墙体面积。
3、套内阳台建筑面积(GB/T 17986.1-2000 B1.4):
套内阳台建筑面积均按阳台外围与房屋外墙之间的水平投影面积计算。其中封闭的阳台按水平投影全部计算建筑面积，未封闭的阳台按水平投影的一半计算建筑面积。
附:房屋的共有建筑面积(GB/T 17986.1-2000 B1.4):
房屋共有建筑面积系指各产权业主共同占有或共同占有或共同使用的建筑面积。共有建筑面积的内容包括:电梯井、管道井、楼梯间、垃圾道、变电室、设备间、公共门
厅、过道、地下室、值班警卫室等，以及为整栋服务的公共用房和管理用房的建筑面积，以水平投影面积计算。
共有建筑面积还包括套与公共建筑空间之间分隔墙，以及外墙(包括山墙)水平投影面积一半的建筑面积。
独立使用的地下室、车棚、车库、为多栋服务的警卫室，管理用房，作为人防工程的地下室都不计入共有建筑面积。
范文二：相关系数计算公式
相关系数计算公式
Statistical correlation coefficient
Due to the statistical correlation coefficient used more frequently, so here is the use of a few articles introduce these coefficients.
The correlation coefficient: a study of two things (in the data we call the degree of correlation between the variables).
If there are two variables: X, Y, correlation coefficient obtained by the meaning can be understood as follows:
(1), when the correlation coefficient is 0, X and Y two variable relationship.
(2), when the value of X increases (decreases), Y value increases (decreases), the two variables are positive correlation, correlation coefficient between 0 and 1.
(3), when the value of X increases (decreases), the value of Y decreases (increases), two variables are negatively correlated, the correlation coefficient between -1.00 and 0.
The absolute value of the correlation coefficient is bigger, stronger correlations, the correlation coefficient is close to 1 or -1, the higher degree of correlation, the correlation coefficient is close to 0 and the correlation is weak.
The related strength normally through the following range of judgment variables:
The correlation coefficient 0.8-1.0 strong correlation
0.6-0.8 strong correlation
0.4-0.6 medium degree.
0.2-0.4 weak correlation
0.0-0.2 very weakly correlated or not correlated
Pearson (Pearson) correlation coefficient
1, introduction
Pearson is also known as the correlation (or correlation) is a kind of calculation method of the linear correlation of British statistician Pearson in twentieth Century.
Suppose there are two variables X, Y, then the Pearson correlation coefficient between the two variables can be calculated by the following formula:
A formula:
Formula two:
Formula three:
Formula four:
Four equivalent formulas listed above, where E is the mathematical expectation, cov said the covariance, N represents the number of variables.
2, scope of application
When the two variables of the standard deviation is not zero, the correlation coefficient is defined, the correlation coefficient for Pearson:
(1), is the linear relationship between the two variables, are continuous data.
(2) overall, two variables are normally distributed, or near normal unimodal distribution.
(3) and the observation values of two variables is in pairs, each pair of observations are independent of each other.
3, Matlab
Pearson correlation coefficient Matlab (according to the formula four):
[cpp] view plaincopy
Function coeff = myPearson (X, Y)
% of the function of the realization of the Pearson correlation coefficient calculating operation
%
% input:
% X: numerical sequence input
% Y: numerical sequence input
%
% output:
% coeff: two input numerical sequence X, the correlation coefficient of Y
%
If length (X) ~ = length (Y)
Error (two 'numerical sequence dimension is not equal to');
Return;
End
Fenzi = sum (X * Y) - (sum (X) * sum (Y)) / length (X);
(fenmu = sqrt (sum (X.^2) - sum (X) ^2 / length (X)) * (sum (Y.^2) - sum (Y) ^2 / length (X)));
Coeff = fenzi / fenmu;
End% myPearson end function
Calculate the Pearson correlation coefficient function can also be used in existing Matlab:
[cpp] view plaincopy
Coeff = corr (X, Y);
4, reference content
Spearman Rank (Spielman rank correlation coefficient)
1, introduction
In statistics, Spielman correlation coefficient is named for Charles Spearman, and often use the Greek symbol (rho) said its value. Spielman rank correlation coefficient is used to estimate the correlation between the two variables X and Y, the correlation between variables can be used to describe the monotone function.
If the two sets of two variable does not have the same two elements, so, when one of the variables can be expressed as a monotone function well when another variable (i.e. changes in two variables of the same trend), between the two variables can reach +1 or -1.
Suppose that two random variables were X, Y (also can be seen as a set of two), the number of their elements are N, two I
(1<><=n) random="" variables="" take="" values="" respectively="" with="" xi,="" yi="" said.="" sort="" of="" x,="" y="" (at="" the="" same="" time="" as="" ascending="" or="" descending),="" two="" ranking="" elements="" set="" x,="" y,="" xi,="" yi="" elements="" which="" are="" xi="" in="" x="" and="" yi="" ranking="" in="" the="" y="" ranking.="" the="" collection="" of="" x,="" y="" elements="" in="" the="" corresponding="" subtraction="" to="" get="" a="" list="" of="" difference="" set="" d,="" di="xi-yi,"><><=n. spielman="" rank="" correlation="" coefficient="" between="" random="" variables="" x="" and="" y="" can="" be="" obtained="" by="" x,="" y="" or="" d="" calculation,="" the="" calculation="" methods="" are="" as="" follows:="">
By ranking difference calculated from D diversity (formula one):
From the top set X, calculated from Y (Spielman rank correlation coefficient were also considered after ranking two random variables Pearson correlation coefficient, the following is the actual Pearson calculated the correlation coefficient X, y) (formula two):
The following is a set of elements in the list of examples of calculation (calculated only for Spielman rank correlation coefficient)
Note: when the two variables of the same, their ranking is obtained by the average of their positions.
2, scope of application
Spielman rank correlation coefficient of the data conditions without Pearson correlation coefficient is strict, as long as the observed values of two variables is the rating data pairs,
or transformed by continuous variable data level data, regardless of the overall distribution of the two variables of the form, the size of the sample, we can use Spielman correlation the coefficient of.
3, Matlab
A source program:
Spielman rank correlation coefficient Matlab (based on ranking difference diversity D calculated using the above formula)
[cpp] view plaincopy
Function coeff = mySpearman (X, Y)
% of the function used to achieve computing Spielman rank correlation coefficient
%
% input:
% X: numerical sequence input
% Y: numerical sequence input
%
% output:
% coeff: two input numerical sequence X, the correlation coefficient of Y
If length (X) ~ = length (Y)
Error (two 'numerical sequence dimension is not equal to');
Return;
End
N = length (X);% by the length of the sequence
Xrank = zeros (1, N);% of elements stored in the X list
Yrank = zeros (1, N);% of elements stored in the Y list
% calculated value in Xrank
For I: N = 1
Cont1 = 1; the number of records is higher than the specified element%
Cont2 = -1;% records with specific elements of the same number of elements
For J: N = 1
If X (I) < x="">
Cont1 = cont1 + 1;
Elseif X (I) = X (J)
Cont2 = cont2 + 1;
End
End
Xrank (I) = cont1 + mean ([0: cont2]);
End
% calculated value in Yrank
For I: N = 1
Cont1 = 1; the number of records is higher than the specified
element%
Cont2 = -1;% records with specific elements of the same number
of elements
For J: N = 1
If Y (I) < y="" (j)="">
Cont1 = cont1 + 1;
Elseif Y (I) = Y (J)
Cont2 = cont2 + 1;
End
End
范文三：挖方放坡系数及计算公式
挖方放坡系数及计算公式：(1)挖方形或长方形地坑放坡工程量计算： 计算公式： V=(a1+2 C+KH)×(a2+2 C+KH)×H+1/3K2 H3 式中：V=挖土方体积(立方米) ； H=地坑深度(米) ； a1=基础长度(米) ,a2==基础宽度(米) ， C=工作面宽度(米) ， K=坡度系数 , 1/3K2 H3 =角锥体体积
其中：m ＝ｂ/Ｈ
Ｃ—工作面
根据方案取C=0.8,K=0.75
根据现场测量得H=1.887m
第一段：1轴～17轴×A 轴～R 轴
a1=64.000+1.375+1.175=66.550m
a2=60.000+1.375+1.175=62.550m
代入公式：
V1=（66.55+0.8×2+0.75×1.887）×（62.55+0.8×2+0.75×1.887）×1.887+1／3×(0.75×0.75×1.887×1.887×1.887)=8607.955m3
第二段：17轴～28轴×A 轴～D 轴
a1=51.900
a2=17.800+1.375+1.375=20.550m
代入公式：
V2=(51.90+0.8×2+0.75×1.887) ×(20.55+0.8×2+0.75×1.887) ×1.887+1／3×(0.75×0.75×1.887×1.887×1.887)=2443.200m3
塔吊基础方量：
h1为原地面到基础底高，h2为塔吊承台高。
H=h1+h2=1.887+1.20=3.087m a1=5.600m a2=5.600m
代入公式：
V3=5.6 ×5.6 ×3.087=96.808m3
2个车道斜坡:
a1为坡长，a2为坡宽。
a1=7.000m a2=3.500m H=1.887m
V4=1／2×7×1.887×3.5×2=46.232m3
总土方量：=V1+V2+V3+V4=8607.955+2443.200+46.232+96.808=11194.195 m3
范文四：外遮阳系数简化计算公式
附件八 外遮阳系数简化计算公式
注：表中的A 、B 、C 、H 、L 分别为各种遮阳形式的几何参数，详附图A 和附图B 。
附表8－2 夏热冬暖地区公共建筑外遮阳系数计算公式
注：1、 表中的A 、C 、H 、L 分别为挡板挑出长度、挡板高度、窗高度、窗宽度，附图B 。 2、 对幕墙，因挡板与玻璃的间距很小，故挡板的轮廓透光比
η可近似取为0，附图C 。
表C 典型形式的建筑外遮阳系数SD
A —遮阳板外挑长度；B —遮阳板根部到窗对边距离 附图A 水平遮阳板和垂直遮阳板外挑参数A 、B 示意
A —挡板距外墙的距离；C —挡板的高度；H —外窗的高度；L —外窗的宽度
附图B 挡板遮阳参数A 、C 、H 、L 示意
附图C 玻璃幕墙遮阳计算简图
范文五：外遮阳系数简化计算公式
外遮阳系数简化计算公式
居住建筑外遮阳系数简化计算公式表
遮阳形式 朝向 外遮阳系数计算公式
2东、南 SD=0.35(A/B)-0.65(A/B)+1 注: c?h水平遮阳 2当计算出西、北 SD =0.20(A/B)-0.40(A/B)+1 c?h2A/B>1时，东 SD=0.25(A/B)-0.60(A/B)+1 c?v2取A/B=1。 垂直遮阳 南 SD =0.40(A/B)-0.75(A/B)+1 c?v2西、北 SD =0.30(A/B)-0.60(A/B)+1 c?v
SD=水平遮阳板遮阳系数×垂直遮阳板遮阳系数 综综合遮阳 各朝向 = SD×SD c?hc?v*计算公式 SD =1-(1-η)(1-η)
南 η=1-C/H+0.5(A?C)/(H?L) 注:玻璃幕
墙的挡板东、西 η=1-C/H+0.135(A?C)/(H?L) η挡板轮廓透光比 遮阳可近北 η=1-C/H+0.5(A?C)/(H?L) 挡 似取η=0。 板 *值 挡板材料 η遮 混凝土、金属类实挡板 0.1 阳 厚帆布、玻璃钢类挡板 0.4 *η挡板构造透射比 深色玻璃、有机玻璃、卡布隆类挡板 0.6
浅色玻璃、有机玻璃、卡布隆类挡板 0.8
金属或其他非透明材料制作的花格、百叶类 0.15
=SC×SD SW
=外窗本身的遮阳系数SC×窗口的建筑外遮阳系数SD
SCSeAASe,，,/0.80.7(,)玻窗
外窗综合遮阳系数AASe为窗玻璃的遮蔽系数，为窗玻璃的面积，为窗洞口面积，计算时铝合金窗取窗玻(S) W
，塑钢窗取。 AA/0.8,AA/0.7,玻窗玻窗
常用外窗的遮阳系数SC参照附表8，或核查企业的产品资料。
注:表中的A、B、C、H、L分别为各种遮阳形式的几何参数，详附图A和附图B。
夏热冬暖地区公共建筑外遮阳系数计算公式
遮阳形式 朝 向 外遮阳系数计算公式
2 东 SD=0.35(A/B)-0.73(A/B)+1 H2 东 南 SD=0.42(A/B)-0.75(A/B)+1 H2 南 SD=0.41(A/B)-0.72(A/B)+1 H2水平遮阳 西 南 SD=0.36(A/B)-0.67(A/B)+1 H2 西 SD=0.36(A/B)-0.72(A/B)+1 H2西 北 SD=0.36(A/B)-0.69(A/B)+1 H2 北 SD=0.32(A/B)-0.61(A/B)+1 H
2东 北 SD=0.43(A/B)-0.78(A/B)+1 H2 东 SD=0.34(A/B)-0.68(A/B)+1 V2 东 南 SD=0.42(A/B)-0.81(A/B)+1 V2 南 SD=0.41(A/B)-0.72(A/B)+1 V2垂直遮阳 西 南 SD=0.41(A/B)-0.82(A/B)+1 V2 西 SD=0.36(A/B)-0.72(A/B)+1 V2西 北 SD=0.40(A/B)-0.81(A/B)+1 V2 北 SD=0.32(A/B)-0.61(A/B)+1 V2东 北 SD=0.43(A/B)-0.83(A/B)+1 V
综合遮阳 各朝向 SD,SD×SD 综合HV
,(水平遮阳系数×垂直遮阳系数)
SD,1,(1,η)(1,η’)
档板遮阳 各朝向 式中，η——挡板轮廓透光比,档板透光面积/窗洞面积，
按不同朝向查下表A;对幕墙，取η,0。
η’ —— 挡板构造透射比，查下表B
表A 挡板轮廓透光比η的计算公式
朝 向 挡板轮廓透光比η的计算公式 南 向 η,1,C/H，0.5AC/HL
东 西 向 η,1,C/H，0.135AC/HL 北 向 η,1,C/H，0.5AC/HL
注:1、 表中的A、C、H、L分别为挡板挑出长度、挡板高度、窗高度、窗宽度，附图B。
2、 对幕墙，因挡板与玻璃的间距很小，故挡板的轮廓透光比η可近似取为0，附图C。 表B 挡板构造透射比η’
挡 板 材 料 η’值
0.1 混凝土、金属类实挡板
0.4 厚帆布、玻璃钢类挡板
0.6 深色玻璃、有机玻璃、卡布隆类挡板
0.8 浅色玻璃、有机玻璃、卡布隆类挡板
0.15 金属或其他非透明材料制作的花格、百叶类
表C 典型形式的建筑外遮阳系数SD
遮 阳 形 式 SD
0.5 可完全遮挡直射阳光的固定百叶、固定挡板 、遮阳板
0.7 可基本遮挡直射阳光的固定百叶、固定挡板 、遮阳板
0.7 较密的花格
0.6 非透明活动百叶或卷帘
A—遮阳板外挑长度;B—遮阳板根部到窗对边距离 附图A 水平遮阳板和垂直遮阳板外挑参数A、B示意
A
CA
HL
A—挡板距外墙的距离;C—挡板的高度;H—外窗的高度;L—外窗的宽度 附图B 挡板遮阳参数A、C、H、L示意
附图C 玻璃幕墙遮阳计算简图
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