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Predictive analytics primarily focuses on:

• Describing historical data

• Forecasting future outcomes

• Managing data storage

• Database design

In linear regression, the coefficient represents:

• Data variability

• Sampling error

• Random noise

• Change in dependent variable per unit change in independent variable

The Boston Housing dataset is typically used for:

• Regression analysis and predictive modeling

• Classification problems

• Clustering techniques

• Time series forecasting

R-squared (R²) metric measures:

• Prediction error

• Data complexity

• Proportion of variance explained by the model

• Statistical significance

Logistic regression is most appropriate for:

• Continuous prediction

• Binary classification problems

• Time series forecasting

• Clustering analysis

The Chen Model is primarily associated with:

• Machine learning algorithms

• Regression techniques

• Neural network design

• Time series forecasting methods

Time series decomposition typically identifies:

• Trend, seasonality, and residual components

• Machine learning parameters

• Regression coefficients

• 6. Classification boundaries

ARIMA stands for:

• Advanced Regression Interpolation Method

• Automated Regression Integration

• Autoregressive Integrated Moving Average

• Analytical Regional Information Model

Seasonality in time series represents:

• Long-term movement

• Periodic fluctuations

• Constant baseline

• Unexplained variations

The Air Passenger dataset is typically used for:

• Regression analysis

• Classification problems

• Clustering techniques

• Transportation demand forecasting