牧场环境因素与生猪死亡率和分娩率相关性分析数据

相关系数是衡量两个变量之间线性关系强度和方向的统计量。斜率和截距是线性回归方程的关键参数，它们共同决定了直线在坐标系中的位置和倾斜度，对于数据分析和预测至关重要。通过对生猪死亡率、分娩率以及五个牧场环境因素的监测数据的长期积累，并分析它们之间的相关系数、斜率和截距，可以为生猪养殖业者和相关科研人员提供重要的参考。随着数据量的不断增加，这些统计量的计算结果将变得更加精确。本数据集适用于生猪养殖企业、科研机构、技术人员、质量控制人员和检验人员，帮助他们进行生猪死亡率和分娩率的预测分析、趋势分析、因果关系研究、质量控制、科学研究和技术改进。1、数据采集和预处理：
（1）数据采集：采集公司生猪牧场环境因素的每日监测数据和牧场内生猪截至当日的死亡率与分娩率监测数据，具体包括生猪品种、日期、所在牧场、平均温度、平均湿度、平均光照度、平均氨气含量、平均二氧化碳含量、截至今日的死亡率、截至今日的分娩率。（2）数据预处理：对采集的数据进行清洗，去除重复、错误或无关的信息。
2、数据加工和分析：
（1）计算相关系数：①将历史采集（包含本次）的平均温度、平均湿度、平均光照度、平均氨气含量、平均二氧化碳含量、截至今日的死亡率、截至今日的分娩率的数据汇总形成A~G七个变量集合；②利用CORREL函数，分别计算出变量集合A与F、A与G、B与F、B与G、C与F、C与G、D与F、D与G、E与F、E与G的相关系数；以A与F为例，具体公式为：A与F的相关系数=Cov（A,F）/sA*sF；其中Cov（A,F）为A和F的协方差，sA、sF分别为A和F的标准差。（2）计算斜率和截距：利用LOGEST函数，对上述变量集合组合进行自然对数转换，并在此基础上运用指数回归分析，从而精确地计算出描述上述变量集合组合的指数关系的斜率和截距。

Correlation coefficients are statistical metrics that measure the strength and direction of the linear relationship between two variables. Slope and intercept are core parameters of linear regression equations, which jointly determine the position and inclination of a straight line in a coordinate system and are critical for data analysis and prediction. By analyzing the correlation coefficients, slopes and intercepts among long-term accumulated monitoring data of swine mortality, farrowing rate and five pasture environmental factors, valuable references can be provided for swine farming practitioners and relevant researchers. As the volume of data continues to grow, the calculation results of these statistical metrics will become more accurate. This dataset is applicable to swine farming enterprises, research institutions, technical personnel, quality control personnel and inspection personnel, assisting them in conducting predictive analysis, trend analysis, causal relationship research, quality control, scientific research and technical improvement related to swine mortality and farrowing rate.

1. Data Collection and Preprocessing
(1) Data Collection: Collect daily monitoring data of environmental factors in the company's swine pastures, as well as monitoring data of swine mortality and farrowing rate in the pastures up to the current day. The specific items include swine breed, date, pasture location, average temperature, average humidity, average illuminance, average ammonia concentration, average carbon dioxide concentration, mortality up to today, and farrowing rate up to today.
(2) Data Preprocessing: Clean the collected data to remove duplicate, erroneous or irrelevant information.

2. Data Processing and Analysis
(1) Calculation of Correlation Coefficients:
① Aggregate the historically collected (including the current) data of average temperature, average humidity, average illuminance, average ammonia concentration, average carbon dioxide concentration, mortality up to today, and farrowing rate up to today to form seven variable sets labeled A to G;
② Use the CORREL function to calculate the correlation coefficients between variable sets A and F, A and G, B and F, B and G, C and F, C and G, D and F, D and G, E and F, and E and G respectively. Taking A and F as an example, the specific formula is: Correlation coefficient of A and F = Cov(A,F)/(s_A * s_F), where Cov(A,F) is the covariance of A and F, and s_A and s_F are the standard deviations of A and F respectively.
(2) Calculation of Slope and Intercept: Use the LOGEST function to perform natural logarithmic transformation on the aforementioned variable set combinations, and conduct exponential regression analysis based on this, thereby accurately calculating the slope and intercept describing the exponential relationship between the aforementioned variable set combinations.

搜集汇总

数据集介绍

特点

该数据集详细记录了牧场环境因素与生猪死亡率和分娩率的相关性分析数据，包括多种环境因素的监测数据及其与死亡率和分娩率的统计关系，适用于生猪养殖和科研领域的数据分析和预测。