Virtual Labs

Regression and curve fitting using procedures such as Least-Square and Weighted Least-Square curve fitting.

What is the main goal of regression analysis?

a: To predict future data points Explanation

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b: To find the exact middle points of a dataset Explanation

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c: To calculate the average of all points Explanation

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d: To determine the color of data points Explanation

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Which type of regression is most appropriate when the relationship between variables appears to grow exponentially?

a: Linear regression Explanation

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b: Exponential regression Explanation

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c: Polynomial regression Explanation

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d: Logarithmic regression Explanation

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Least-Square Method works by minimizing:

a: Sum of all values Explanation

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b: Area under curve Explanation

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c: Sum of squared errors Explanation

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d: Slope of the curve Explanation

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Weighted Least-Square Method is useful when:

a: All data points have equal accuracy Explanation

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b: Some data points are more reliable than others Explanation

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c: Dataset has only two points Explanation

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d: Curve is always straight Explanation

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Regression helps find:

a: Relationship between dependent and independent variables Explanation

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b: Only the largest data value Explanation

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c: Angles in geometric shapes Explanation

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d: Random output without structure Explanation

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Curve fitting visually represents:

a: How well a model fits observed data Explanation

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b: Only the maximum value in the data Explanation

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c: Number of rows in dataset Explanation

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d: Color of the graph background Explanation

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Polynomial regression is suitable for:

a: Non-linear patterns in data Explanation

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b: Only perfectly straight-line data Explanation

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c: Purely categorical data Explanation

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d: Finding angle of a triangle Explanation

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Regression models usually aim to:

a: Minimize prediction error Explanation

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b: Increase dataset size Explanation

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c: Ignore dependent variables Explanation

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d: Remove all data variations Explanation

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In data science, regression is used for:

a: Prediction and forecasting Explanation

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b: Sorting data alphabetically Explanation

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c: Measuring file size Explanation

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d: Playing animations Explanation

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A good regression model:

a: Fits the underlying trend closely Explanation

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b: Gives a completely random output Explanation

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c: Avoids using any input data Explanation

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d: Changes with every simulation Explanation

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