Differentiate between the following: (a) Descriptive and Inferential statistics (b) Absolute measures and relative measures of dispersion
Additive and Multiplicative Models of Time Series 1. Additive Model: Definition: In an additive model, the components of a time series (trend, seasonality, and error) are added together to form the observed data. The model can be expressed as: [ Y_t = T_t + S_t + \varepsilon_t ] where: ( Y_t ) is thRead more
Additive and Multiplicative Models of Time Series
1. Additive Model:
 Definition: In an additive model, the components of a time series (trend, seasonality, and error) are added together to form the observed data. The model can be expressed as:
[ Y_t = T_t + S_t + \varepsilon_t ]
where: ( Y_t ) is the observed data at time ( t ),
 ( T_t ) is the trend component,
 ( S_t ) is the seasonal component,
 ( \varepsilon_t ) is the error term.
2. Multiplicative Model:
 Definition: In a multiplicative model, the components of a time series are multiplied together to form the observed data. The model can be expressed as:
[ Y_t = T_t \times S_t \times \varepsilon_t ]
3. Differences Between the Models:

Nature of Components:
 In an additive model, the components are added together, implying that the effects of trend and seasonality are constant over time.
 In a multiplicative model, the components are multiplied, implying that the effects of trend and seasonality change proportionally with the level of the time series.

Interpretation:
 Additive models are more straightforward to interpret since the components are additive.
 Multiplicative models are more complex to interpret since the components are multiplicative, and changes in one component can affect the others.

Use Cases:
 Additive models are typically used when the magnitude of seasonality does not depend on the level of the time series.
 Multiplicative models are used when the magnitude of seasonality is proportional to the level of the time series.
4. Common Usage:
 Prevalence: Both additive and multiplicative models are commonly used in time series analysis.
 Preference: The choice between the two models depends on the nature of the data and the underlying process being modeled.
 Factors Influencing Choice:
 If the magnitude of seasonality is relatively constant over time, an additive model may be more appropriate.
 If the magnitude of seasonality changes with the level of the time series, a multiplicative model may be more suitable.
5. Example:
 Consider a time series of monthly sales data. If the seasonal effect on sales remains relatively constant regardless of the level of sales, an additive model may be used. However, if the seasonal effect on sales increases as sales increase, a multiplicative model may be more appropriate.
6. Conclusion:
 Additive and multiplicative models are two common approaches to modeling time series data, each with its own strengths and weaknesses.
 The choice between the two models depends on the nature of the data and the underlying process being modeled. Both models are widely used in practice, and the selection of the appropriate model should be based on careful consideration of the characteristics of the data.
Descriptive vs. Inferential Statistics 1. Descriptive Statistics: Definition: Descriptive statistics summarize and describe the main features of a dataset. They provide simple summaries about the sample and the observations that have been made. Purpose: Descriptive statistics are used to describe anRead more
Descriptive vs. Inferential Statistics
1. Descriptive Statistics:
2. Inferential Statistics:
3. Differences:
Absolute vs. Relative Measures of Dispersion
1. Absolute Measures of Dispersion:
2. Relative Measures of Dispersion:
3. Differences:
4. Example:
5. Conclusion: