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use quadratic regression to find the equation of a quadratic fuction that fils the given point- xo 2 3 yle 4/6·1/71.0 125.9 89.4. A. y = 8.52x²-16.77x + 23.47 B. Y = -19.25x² + 94.32x + 4.08 Cy2,5- 10,5x12 D. Y = -25.4~² + 106.66x + 2.06

use quadratic regression to find the equation of a quadratic fuction that fils the given point- xo 2 3 yle 4/6·1/71.0 125.9 89.4. A. y = 8.52x²-16.77x + 23.47 B. Y = -19.25x² + 94.32x + 4.08 Cy2,5- 10,5x12 D. Y = -25.4~² + 106.66x + 2.06

Algebra and Trigonometry (6th Edition)
Algebra and Trigonometry (6th Edition)
6th Edition
ISBN:9780134463216
Author:Robert F. Blitzer
Publisher:Robert F. Blitzer
ChapterP: Prerequisites: Fundamental Concepts Of Algebra
Section: Chapter Questions
Problem 1MCCP: In Exercises 1-25, simplify the given expression or perform the indicated operation (and simplify,...
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**Title: Quadratic Regression Analysis** **Objective:** Use quadratic regression to find the equation of a quadratic function that fits the given data points. **Data Table:** | \(x\) | 0 | 1 | 2 | 3 | |-------|----|----|-----|-----| | \(y\) | 6.1| 7.2| 15.9| 39.4| **Quadratic Equation Options:** A. \( y = 8.52x^2 - 16.7x + 23.47 \) B. \( y = -15.25x^2 + 94.32x + 4.08 \) C. \( y = 2.5x^2 - 10.5x + 2 \) D. \( y = -25.4x^2 + 106.66x + 2.06 \) **Explanation:** The task is to determine which of these quadratic equations best fits the given data points using quadratic regression techniques. Quadratic regression involves finding the equation of the form \( y = ax^2 + bx + c \) that minimizes the differences between the actual data points and those predicted by the model.
Transcribed Image Text:**Title: Quadratic Regression Analysis** **Objective:** Use quadratic regression to find the equation of a quadratic function that fits the given data points. **Data Table:** | \(x\) | 0 | 1 | 2 | 3 | |-------|----|----|-----|-----| | \(y\) | 6.1| 7.2| 15.9| 39.4| **Quadratic Equation Options:** A. \( y = 8.52x^2 - 16.7x + 23.47 \) B. \( y = -15.25x^2 + 94.32x + 4.08 \) C. \( y = 2.5x^2 - 10.5x + 2 \) D. \( y = -25.4x^2 + 106.66x + 2.06 \) **Explanation:** The task is to determine which of these quadratic equations best fits the given data points using quadratic regression techniques. Quadratic regression involves finding the equation of the form \( y = ax^2 + bx + c \) that minimizes the differences between the actual data points and those predicted by the model.
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