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所在平台: Udemy |
课程主页: https://www.udemy.com/course/regression-in-data-analytics-business-statistics/
课程评论：没有评论
课程名称：数据分析与商业统计中的回归分析 课程概述：本课程聚焦于简单回归分析，探讨一个因变量与一个自变量之间的关系。通过分析，回归统计可以在已知自变量的情况下预测因变量的值。回归分析超越了相关性，通过增加预测能力，使得研究者能够更深入地理解变量之间的关系。课程内容包括：使用最小二乘法构建回归方程，利用回归系数构建回归方程，以及通过回归方程计算相关系数。回归方程为不同个体的不同Y值预测提供了有效基础，预测值将更接近实际值。 我们将探讨相关性和简单线性回归如何在满足特定假设的前提下检验两个变量之间的线性关系。然而，分析结果需要谨慎解读，尤其是在寻找因果关系或使用回归方程进行预测时。回归分析使从业人员和研究人员能够根据样本分析推断个体在某项测量上的得分。因而，回归分析的解读在推断统计中具有重要意义。
Simple regression is used to examine the relationship between one dependent and one independent variable. After performing an analysis, the regression statistics can be used to predict the dependent variable when the independent variable is known. Regression goes beyond correlation by adding prediction capabilities. Correlation shows the relation between two variables, e.g. X and Y Regression takes this one step further. It predicts or estimates a score for an individual on one variable based his/her/its score on the other variable, and on the correlation between the two variables. Regression is a great and an effective method of forecasting. In this course , we will explain:(i) how to construct the regression equations using Method of Least squares. (ii) Then the construction of regression equations using Regression coefficients. (iii) Calculation of correlation coefficient using Regression equationsThe regression equation gives you a valid basis for predicting different Y values for different individuals. The predicted scores will fall closer to the actual scores. Both correlation and simple linear regression can be used to examine the presence of a linear relationship between two variables providing certain assumptions. The results of the analysis, however, need to be interpreted with care, particularly when looking for a causal relationship or when using the regression equation for prediction. Regression allows practitioners and researchers to infer how an individual will score on some measure based on the analysis of a sample. Thus the interpretation of regression has a great significance in inferential statistics.
