Cause and Effect Forecast models use the historical relationship between an independent variable(s) and a dependent variable to predict future demand.

Question

BlackTom AI · Accepted Answer

Cause-and-effect forecast models (also called causal forecasting or regression-based forecasting) explicitly use one or more independent (explanatory) variables to predict a dependent variable such as demand.

1. Relevant knowledge points and concepts
- Independent variables (explanatory/exogenous) influence the dependent variable; examples: advertising spend, price, season index, GDP.  
- Dependent variable: the quantity being predicted (here, demand).  
- Typical techniques: simple/multiple linear regression, econometric models, time-series models with explanatory variables.  
- Key assumption: the historical relationship between the independent variables and demand is stable enough to project forward.

2. Complete reasoning process step by step
- Step 1: Collect historical data on demand (dependent variable) and candidate independent variables.  
- Step 2: Estimate a causal model (e.g., regression) to quantify how changes in each independent variable affect demand.  
- Step 3: Validate the model (goodness-of-fit, residual checks, out-of-sample tests).  
- Step 4: Produce forecasts for the independent variables (or use planned values).  
- Step 5: Input those forecasted/planned independent variable values into the estimated model to compute predicted future demand.

3. Illustrative calculation with intermediate steps
- Suppose we estimate a simple linear model: Demand = 100 + 5 × AdSpend. (Intercept = 100; slope = 5).  
- If planned AdSpend next period = 20 (units), then Predicted Demand = 100 + 5 × 20 = 100 + 100 = 200.  
- Intermediate steps shown: multiply slope by ad spend (5×20=100), add intercept (100+100=200).

4. Why the correct answer is correct
- The statement precisely describes causal forecasting: it uses the historical relationship between independent variable(s) and the dependent variable (demand) to predict future values. Regression and econometric models are built on that principle.

5. Why the other option is incorrect
- “False” is incorrect because it contradicts the definition and common practice of cause-and-effect forecasting; these models do rely on historical relationships to make predictions.

6. Conclusion
- The correct answer is True, which matches the provided answer.

Cause and Effect Forecast models use the historical relationship between an independent variable(s) and a dependent variable to predict future demand.判断题

类似问题

In which security measure will you find anti-virus software?

What are user permissions?

Which security measure will stop someone reading your messages if they find your smart phone?

On a network a firewall

What is the name for emails that are sent to users, pretending to be from their bank and asking for username and password?

To stop users of a network from playing games all day instead of working, which would be the best security measure to use?

Which of the following security threats is the cause of the greatest amount of data loss?

A person who accesses a network by walking through an unlocked door is known as

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单