英语翻译回归分析给管理会计师提供了一个用来评估各项估计的准确性和可靠性的客观指标.在使用回归分析之前,绘制一个数据图形非常重要,以确定是否存在异常数据点.异常数据会对回归分

英语翻译回归分析给管理会计师提供了一个用来评估各项估计的准确性和可靠性的客观指标.在使用回归分析之前,绘制一个数据图形非常重要,以确定是否存在异常数据点.异常数据会对回归分
英语翻译
回归分析给管理会计师提供了一个用来评估各项估计的准确性和可靠性的客观指标.
在使用回归分析之前,绘制一个数据图形非常重要,以确定是否存在异常数据点.异常数据会对回归分析产生极大的影响,异常数据可能会导致最后得到的回归线不能表示大部分数据.如果存在异常数据,应对每个异常数据进行检查,以确定该异常数据的产生是否源于数据记录错误(一种很常见的操作错误)或者源于某个特定的、不常发生的事件.为提高准确度,回归分析要求所收集的数据点个数最好在30个以上.回归分析假设因变量与一个或多个自变量之间存在线性关系.在分析成本行为时,如果预期到成本将会由于学习曲线(参见本节稍后对学习曲线的讨论)的存在而下降,或由于相关作业范围发生变化而导致成本有所变动时,这种线性假设就会存在问题.回归分析还假设因变量与自变量之间先前存在的关系在未来仍然存在.在将回归分析用作预测工具时,非常重要的一点是应评估因变量与自变量之间的关系随时间发生的变化,或随时间推移不断调整因变量与自变量之间的关系.例如,如果能够肯定某项变动成本要素的部分成本在不断增加,这会导致回归方程中的系数b在未来期间不太可靠.在将回归分析结果用于预测时,必须牢记预测中所使用的自变量取值,不得超过先前为确立回归线所使用的数据集的范围.在前述Buildand Fix公司销售额和营销成本的例子中,估计回归线时所使用的营销成本范围为$30000至$110000.只要营销成本数额不超过这一范围,该公司就能利用该营销成本数据和回归线,可靠地预测公司的销售额水平.一旦预测中所使用的营销成本数据不在这一范围内(比如营销成本为$10000或$200000),使用该数据预测销售额水平就不太可靠.回归分析中应注意的一个方面是,使用者必须评估因变量与自变量之间关系的合理性.X真的能驱动Y发生变化吗?X和Y可以取任意数值,结果都能得到一个回归方程,但该方程是否有意义则需依赖使用者自己的主观判断.
机译的就不要打扰了

英语翻译回归分析给管理会计师提供了一个用来评估各项估计的准确性和可靠性的客观指标.在使用回归分析之前,绘制一个数据图形非常重要,以确定是否存在异常数据点.异常数据会对回归分
Regression analysis gives management accountants an objective measure to use in evaluating the precision and reliability of estimations.
It is important to prepare a graph of the data prior to using regression analysis and detennine whether any unusual data points, called outliers, are present. Regression analysis can be influenced strongly by out1iers, which may result in an estimation line that is not representative of most of the data. If present, each outlier should be reviewed to determine whether it is due to a data recording error, a normal operating condition, or a unique and nonrecurring event. Regression analysis requires a collection of data points-preferably 30 or more一to be accurate.
Regression analysis assumes a linear relationship between one dependent variable and one or more independent variables. In the case of analyzing cost behavior, this linear assumption can be problematic if it is expected that costs decline due to leaming curves (see the discussion of leaming curves later in this topic) or when there are different relevant ranges of activity that cause costs to shift.
Regression analysis also assumes that past relationships between dependent and independent variables will hold into the future. When using regression analysis as a forecasting tool,it is important to evaluate or make adjustments for changes in the relationship between the variables over time. For example, if there is clear knowledge that one part of a variable cost element is increasing, this makes the b coefficient less reliable for future periods.
When using the results of a regression analysis to make any prediction, it is important to remember that the dependent variables used for the prediction must fall within the range of the data set used to establish the regression line. For the Build and Fix sales and marketing costs, the marketing costs used to estimate the regression line fal1 in the range of $30,000 and $11 0,000. The company can reliably predict its sales for any marketing cost that falls within this range but it cannot rely on the resu1t if the marketing cost used in the prediction falls outside this range (e.g., $10,000 or $200,000).
One caveat regarding regression analysis is that the user must evaluate the reasonableness ofthe relationship between the dependent and independent variables. Does it make sense that X causes Y to change? Any numbers can be input as X and Y var?ables and wi11 result in an equation, but whether the equation makes sense must be based on the user's judgment.
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