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The model features a downward-sloping demand curve (AD) and a horizontal inflation adjustment line (IA). The point where the two lines cross is equal to potential GDP. A shift in either curve will explain the impact on real GDP and inflation in the short run.
The AD (aggregate demand) curve in the static AD–AS model is downward sloping, reflecting a negative correlation between output and the price level on the demand side. It shows the combinations of the price level and level of the output at which the goods and assets markets are simultaneously in equilibrium.
The aggregate demand curve is plotted with real output on the horizontal axis and the price level on the vertical axis. While it is theorized to be downward sloping, the Sonnenschein–Mantel–Debreu results show that the slope of the curve cannot be mathematically derived from assumptions about individual rational behavior.
In the standard aggregate supply–aggregate demand model, real output (Y) is plotted on the horizontal axis and the price level (P) on the vertical axis. The levels of output and the price level are determined by the intersection of the aggregate supply curve with the downward-sloping aggregate demand curve.
The usual method is to collect data on past prices, quantities, and variables such as consumer income and product quality that affect demand and apply statistical methods, variants on multiple regression. The issue with this approach, as outlined by Baumol, is that only one point on a demand curve can ever be observed at a specific time.
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
This idea can be envisioned graphically by the intersection of an upward-sloping marginal cost curve and a downward-sloping marginal revenue curve . In classical theory, any change in the marginal cost structure or the marginal revenue structure will be immediately reflected in a new price and/or quantity sold of the item.