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发表于 2009-12-7 09:34:19
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Using regression使用回归
Through research, you know that your residential gas usage is primarily affected by three variables: new home starts, the temperature, and the price of natural gas. However, you aren’t sure how much effect each has on gas usage.
通过调查,可以知道住在天然气使用量是收到三个方面因素的影响:新房的数量、问题和天然气的价格。但是你不能确认天然气是使用效果是多大?
Because you have independent variables affecting a dependent variable (the variable that you are interested in), this forecast requires regression. For regression, CB Predictor uses a technique called HyperCasting™, which in one easy step:
因为对因变量影响的有多个自变量,预测的结果需要进行回归。用回归的方法,CB Predictor使用一种叫做HyperCasting的技术:
a. Creates an equation that defines the mathematical relationship between the independent variables and your dependent variable.
a、根据因变量和自变量之间的关系建立一个数学方程式。
b. Forecasts each independent variable using time-series forecasting methods.
b、使用时间序列预测方法预测每一个因变量
c. Uses the equation it created in the first step, combining the forecasted independent variable values, to create the forecast for the dependent variable.
c、使用第一步创建的方程式,结合自变量的预测值,生成因变量的预测值。
In the Toledo Gas spreadsheet, the dependent variable is the historical residential gas usage. The independent variables are:
•Number of occupancy permits issued (new housing completions)
•Average temperature per month
•Unit cost of natural gas
在托莱多天然气电子表格中,因变量是居民天然气使用量的数据,自变量是:
允许使用的数量(新房的居住率)
每个月的平均气温
天然气的单位成本
To resume your tutorial:
6.Under Step 5, select Use Multiple Linear Regression.
6、在第五步之后,选择多元线性回归。
The Regression Variables dialog appears.
回归变量的对话框显示出来
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图1.12.png
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Figure 1.12 Regression Variables dialog
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