工贸类企业安全生产管理绩效趋势分析数据

主要用于管理部门对工贸类企业的安全生产管理情况进行阶段性走势和趋势的预测，以单位（如街道）月度工贸类企业安全生产管理综合评分为因变量，以时间（如月份）为自变量，以特定周期为数据采集范围（如年），采用线性回归分析方法，精准预测各单位下阶段（如下一年的第一个月）工贸类企业安全生产管理走势。辅助有关部门能精准掌握各基层工贸类企业安全生产管理趋势变化情况，有助于针对性指导管理绩效趋势不良的单位及时作出精准反应，辅助管理部门提升工贸类企业安全生产管理智能化水平，为有关部门制定有效的宏观管理决策提供科学的量化依据。以单位的工贸类企业安全生产月度评估得分值为因变量，以月份数字值为自变量（如13代表第二年1月），采用线性回归分析预测单位下一阶段（如第二年1月）工贸类企业安全生产管理绩效趋势。
1.计算单位评估关系强度量（Sxx）：Sxx=∑(xᵢ - x0)²，其中xᵢ是自变量月份数字值，x0是自变量月份数字值的平均值。
2.计算单位评估预测偏差度（Syx）：Syx=∑(xᵢ - x0)*(yᵢ - y0)，其中，yᵢ是单位的月份评估得分值即因变量实际观测值，y0是因变量观测值的平均值。如果为正，表示两个变量倾向于同时增加或同时减少；如果为负，表示一个变量增加时，另一个变量倾向于减少。
3.计算单位评估回归系数b：b = ∑(xᵢ - x0)*(yᵢ - y0)/∑(xᵢ - x0)²，其中，xᵢ和 yᵢ分别是自变量和因变量的各个观测值，x0和y0含义同上。
4.计算单位评估初始基准值（截距a）：a =y0-b*x0，其中，x0、y0含义同上，b是回归系数。
5.通过回归方程预测第13个月单位的工贸类企业安全生产评估分值：y =y0-b*x0+ bx，其中x=13，y0、x0、b含义同上。

This dataset is primarily designed for management departments to perform periodic trend analysis and prediction of the work safety management status of industrial and commercial enterprises. Specifically, taking the monthly comprehensive safety management score of industrial and commercial enterprises under a given unit (e.g., sub-district) as the dependent variable, time (i.e., month) as the independent variable, and a specific cycle (e.g., one year) as the data collection scope, it adopts linear regression analysis to accurately predict the work safety management trend of each unit in the next stage (e.g., the first month of the following year). This tool assists relevant departments in accurately grasping the trend changes of work safety management for grass-roots industrial and commercial enterprises, supports targeted guidance for units with poor management performance trends to make timely and accurate responses, helps management departments improve the intelligent level of work safety management for industrial and commercial enterprises, and provides scientific quantitative basis for relevant departments to formulate effective macro management decisions.

In the core modeling process, the monthly safety assessment score of industrial and commercial enterprises of a unit is taken as the dependent variable, and the numerical value of the month (e.g., 13 represents January of the next year) is taken as the independent variable, and linear regression analysis is used to predict the work safety management performance trend of the unit in the next stage (e.g., January of the next year). The specific calculation steps are as follows:
1. Calculate the unit assessment relationship strength metric ($S_{xx}$): $S_{xx} = sum (x_i - ar{x})^2$, where $x_i$ is the numerical value of the independent variable month, and $ar{x}$ is the average value of the independent variable month numerical values.
2. Calculate the unit assessment prediction deviation degree ($S_{yx}$): $S_{yx} = sum (x_i - ar{x})(y_i - ar{y})$, where $y_i$ is the actual observed value of the unit's monthly assessment score (i.e., the dependent variable), and $ar{y}$ is the average value of the dependent variable observed values. A positive value indicates that the two variables tend to increase or decrease simultaneously; a negative value indicates that when one variable increases, the other tends to decrease.
3. Calculate the unit assessment regression coefficient $b$: $b = frac{sum (x_i - ar{x})(y_i - ar{y})}{sum (x_i - ar{x})^2}$, where $x_i$ and $y_i$ are the respective observed values of the independent and dependent variables, and $ar{x}$ and $ar{y}$ have the same meanings as above.
4. Calculate the unit assessment initial benchmark value (intercept $a$): $a = ar{y} - b cdot ar{x}$, where $ar{x}$ and $ar{y}$ have the same meanings as above, and $b$ is the regression coefficient.
5. Predict the work safety assessment score of industrial and commercial enterprises of the unit in the 13th month through the regression equation: $y = ar{y} - b cdot ar{x} + b cdot x$, where $x=13$, and $ar{y}$, $ar{x}$, $b$ have the same meanings as above.

搜集汇总

数据集介绍

特点

该数据集包含799条记录，每月更新，主要用于通过线性回归分析预测工贸类企业安全生产管理的绩效趋势。数据结构包括单位名称、各月份评估得分及相关统计指标，应用场景为辅助管理部门精准掌握企业安全生产管理趋势变化，提升管理智能化水平。