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Additive and Multiplicative Models, Business Mathematics and Statistics Video Lecture | Business Mathematics and Statistics - B Com

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FAQs on Additive and Multiplicative Models, Business Mathematics and Statistics Video Lecture - Business Mathematics and Statistics - B Com

1. What is the difference between additive and multiplicative models in business mathematics and statistics?
Ans. Additive models are used when the relationship between variables is linear, meaning that the effect of one variable is simply added to the effect of another. On the other hand, multiplicative models are used when the relationship between variables is non-linear, meaning that the effect of one variable is multiplied by the effect of another.
2. How are additive models applied in business mathematics and statistics?
Ans. Additive models are commonly used in forecasting and time series analysis. They assume that the observed data can be decomposed into a systematic component and a residual component, where the systematic component represents the underlying trend and seasonality, and the residual component represents the random fluctuations.
3. When are multiplicative models more appropriate in business mathematics and statistics?
Ans. Multiplicative models are more appropriate when the relationship between variables is not strictly linear and cannot be adequately captured using additive models. For example, when the effect of one variable on another is proportional rather than additive, a multiplicative model may be more suitable.
4. What are the advantages and disadvantages of additive models?
Ans. The advantages of additive models include their simplicity and interpretability, as well as their ability to handle time series data with a constant variance. However, additive models may not capture non-linear relationships accurately and may not be appropriate for data with changing variance.
5. Can additive and multiplicative models be combined in business mathematics and statistics?
Ans. Yes, additive and multiplicative models can be combined to create hybrid models that capture both linear and non-linear relationships. These hybrid models, such as the additive-multiplicative seasonal decomposition of time series (AMSD), can provide a more accurate representation of the underlying data and improve forecasting accuracy.
115 videos|142 docs
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