ECONOMICS
COMPOUND INTEREST
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[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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starting amount
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principal
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Detailed explanation-1: -PCA is a tool for identifying the main axes of variance within a data set and allows for easy data exploration to understand the key variables in the data and spot outliers. Properly applied, it is one of the most powerful tools in the data analysis tool kit.
Detailed explanation-2: -PCA is more useful when dealing with 3 or higher-dimensional data. It is always performed on a symmetric correlation or covariance matrix. This means the matrix should be numeric and have standardized data.
Detailed explanation-3: -The principal components in PCA are linear combinations of the initial variables that maximize the variance explained by the data. Principal components are calculated using the correlation matrix.
Detailed explanation-4: -When uses PCA? PCA helps your to find latent features among all your data, can reduce your dimensionality for 1/10, making easier to visualize data and faster training because uses less hardware to run.