Which Quadratic Function Best Fits This Data?$\[ \begin{tabular}{|c|c|} \hline $x$ & $y$ \\ \hline 1 & 250 \\ \hline 2 & 289 \\ \hline 3 & 316 \\ \hline 4 & 335 \\ \hline 6 & 320 \\ \hline \end{tabular} \\]A. $y = 9.16x^2 + 73.04x +

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Introduction

In mathematics, particularly in algebra and statistics, it is often necessary to find the best-fitting function to a given dataset. This can be achieved by using various methods, including linear regression, polynomial regression, and quadratic regression. In this article, we will focus on quadratic regression, which is a type of polynomial regression that involves finding the best-fitting quadratic function to a given dataset.

What is Quadratic Regression?

Quadratic regression is a type of regression analysis that involves finding the best-fitting quadratic function to a given dataset. A quadratic function is a polynomial function of degree two, which means it has the form y=ax2+bx+cy = ax^2 + bx + c, where aa, bb, and cc are constants. Quadratic regression is used to model the relationship between a dependent variable yy and an independent variable xx.

The Quadratic Function

The quadratic function that we will be using to fit the data is of the form y=ax2+bx+cy = ax^2 + bx + c. To find the best-fitting quadratic function, we need to determine the values of aa, bb, and cc that minimize the sum of the squared errors between the observed values of yy and the predicted values of yy.

The Data

The data that we will be using to fit the quadratic function is given in the table below:

xx yy
1 250
2 289
3 316
4 335
6 320

Fitting the Quadratic Function

To fit the quadratic function to the data, we need to determine the values of aa, bb, and cc that minimize the sum of the squared errors between the observed values of yy and the predicted values of yy. This can be done using various methods, including the method of least squares.

Method of Least Squares

The method of least squares is a statistical technique that involves finding the best-fitting line or curve to a given dataset. The method of least squares is based on the principle of minimizing the sum of the squared errors between the observed values of yy and the predicted values of yy.

Calculating the Coefficients

To calculate the coefficients aa, bb, and cc of the quadratic function, we need to use the following formulas:

a=∑i=1nxi2∑i=1nyi−∑i=1nxi∑i=1nxiyin∑i=1nxi2−(∑i=1nxi)2a = \frac{\sum_{i=1}^{n} x_i^2 \sum_{i=1}^{n} y_i - \sum_{i=1}^{n} x_i \sum_{i=1}^{n} x_i y_i}{n \sum_{i=1}^{n} x_i^2 - \left(\sum_{i=1}^{n} x_i\right)^2}

b=n∑i=1nxiyi−∑i=1nxi∑i=1nyin∑i=1nxi2−(∑i=1nxi)2b = \frac{n \sum_{i=1}^{n} x_i y_i - \sum_{i=1}^{n} x_i \sum_{i=1}^{n} y_i}{n \sum_{i=1}^{n} x_i^2 - \left(\sum_{i=1}^{n} x_i\right)^2}

c=∑i=1nyi−a∑i=1nxi2−b∑i=1nxinc = \frac{\sum_{i=1}^{n} y_i - a \sum_{i=1}^{n} x_i^2 - b \sum_{i=1}^{n} x_i}{n}

Calculating the Coefficients for the Given Data

To calculate the coefficients aa, bb, and cc for the given data, we need to use the formulas above. The calculations are as follows:

a=∑i=1nxi2∑i=1nyi−∑i=1nxi∑i=1nxiyin∑i=1nxi2−(∑i=1nxi)2a = \frac{\sum_{i=1}^{n} x_i^2 \sum_{i=1}^{n} y_i - \sum_{i=1}^{n} x_i \sum_{i=1}^{n} x_i y_i}{n \sum_{i=1}^{n} x_i^2 - \left(\sum_{i=1}^{n} x_i\right)^2}

=(12+22+32+42+62)(250+289+316+335+320)−(1+2+3+4+6)(1⋅250+2⋅289+3⋅316+4⋅335+6⋅320)5(12+22+32+42+62)−(1+2+3+4+6)2= \frac{(1^2 + 2^2 + 3^2 + 4^2 + 6^2)(250 + 289 + 316 + 335 + 320) - (1 + 2 + 3 + 4 + 6)(1 \cdot 250 + 2 \cdot 289 + 3 \cdot 316 + 4 \cdot 335 + 6 \cdot 320)}{5(1^2 + 2^2 + 3^2 + 4^2 + 6^2) - (1 + 2 + 3 + 4 + 6)^2}

=55⋅1510−16⋅15105⋅55−202= \frac{55 \cdot 1510 - 16 \cdot 1510}{5 \cdot 55 - 20^2}

=82650−24160275−400= \frac{82650 - 24160}{275 - 400}

=58490−125= \frac{58490}{-125}

=−467.04= -467.04

b=n∑i=1nxiyi−∑i=1nxi∑i=1nyin∑i=1nxi2−(∑i=1nxi)2b = \frac{n \sum_{i=1}^{n} x_i y_i - \sum_{i=1}^{n} x_i \sum_{i=1}^{n} y_i}{n \sum_{i=1}^{n} x_i^2 - \left(\sum_{i=1}^{n} x_i\right)^2}

=5⋅(1⋅250+2⋅289+3⋅316+4⋅335+6⋅320)−(1+2+3+4+6)(250+289+316+335+320)5⋅55−202= \frac{5 \cdot (1 \cdot 250 + 2 \cdot 289 + 3 \cdot 316 + 4 \cdot 335 + 6 \cdot 320) - (1 + 2 + 3 + 4 + 6)(250 + 289 + 316 + 335 + 320)}{5 \cdot 55 - 20^2}

=5⋅1510−16⋅1510275−400= \frac{5 \cdot 1510 - 16 \cdot 1510}{275 - 400}

=7550−24160−125= \frac{7550 - 24160}{-125}

=−144.48= -144.48

c=∑i=1nyi−a∑i=1nxi2−b∑i=1nxinc = \frac{\sum_{i=1}^{n} y_i - a \sum_{i=1}^{n} x_i^2 - b \sum_{i=1}^{n} x_i}{n}

=250+289+316+335+320−(−467.04)⋅55−(−144.48)⋅205= \frac{250 + 289 + 316 + 335 + 320 - (-467.04) \cdot 55 - (-144.48) \cdot 20}{5}

=1510+25678.2+2889.65= \frac{1510 + 25678.2 + 2889.6}{5}

=29478.85= \frac{29478.8}{5}

=5895.76= 5895.76

The Best-Fitting Quadratic Function

The best-fitting quadratic function to the given data is:

y=−467.04x2−144.48x+5895.76y = -467.04x^2 - 144.48x + 5895.76

Conclusion

In this article, we have discussed the method of quadratic regression, which involves finding the best-fitting quadratic function to a given dataset. We have also calculated the coefficients of the quadratic function using the method of least squares. The best-fitting quadratic function to the given data is y=−467.04x2−144.48x+5895.76y = -467.04x^2 - 144.48x + 5895.76. This function can be used to model the relationship between the dependent variable yy and the independent variable xx.

References

  • Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied Linear Statistical Models. McGraw-Hill.
  • Draper, N. R., & Smith, H. (1998). Applied Regression Analysis. John Wiley & Sons.
  • Weisberg, S. (2005). Applied Linear Regression. John Wiley & Sons.
    Quadratic Regression Q&A ==========================

Introduction

In our previous article, we discussed the method of quadratic regression, which involves finding the best-fitting quadratic function to a given dataset. We also calculated the coefficients of the quadratic function using the method of least squares. In this article, we will answer some frequently asked questions about quadratic regression.

Q: What is quadratic regression?

A: Quadratic regression is a type of regression analysis that involves finding the best-fitting quadratic function to a given dataset. A quadratic function is a polynomial function of degree two, which means it has the form y=ax2+bx+cy = ax^2 + bx + c, where aa, bb, and cc are constants.

Q: What are the advantages of quadratic regression?

A: The advantages of quadratic regression include:

  • It can be used to model non-linear relationships between variables.
  • It can be used to identify the presence of a quadratic relationship between variables.
  • It can be used to predict the value of a dependent variable based on the value of an independent variable.

Q: What are the disadvantages of quadratic regression?

A: The disadvantages of quadratic regression include:

  • It can be sensitive to outliers in the data.
  • It can be affected by the presence of multicollinearity in the data.
  • It can be difficult to interpret the results of quadratic regression.

Q: How do I choose the best-fitting quadratic function?

A: To choose the best-fitting quadratic function, you need to use a statistical method such as the method of least squares. This method involves minimizing the sum of the squared errors between the observed values of yy and the predicted values of yy.

Q: What are the common applications of quadratic regression?

A: The common applications of quadratic regression include:

  • Modeling the relationship between a dependent variable and an independent variable.
  • Identifying the presence of a quadratic relationship between variables.
  • Predicting the value of a dependent variable based on the value of an independent variable.

Q: How do I interpret the results of quadratic regression?

A: To interpret the results of quadratic regression, you need to examine the coefficients of the quadratic function. The coefficient of the x2x^2 term represents the rate of change of the dependent variable with respect to the independent variable. The coefficient of the xx term represents the rate of change of the dependent variable with respect to the independent variable at the point where the quadratic function is at its minimum or maximum.

Q: What are the common mistakes to avoid in quadratic regression?

A: The common mistakes to avoid in quadratic regression include:

  • Failing to check for multicollinearity in the data.
  • Failing to check for outliers in the data.
  • Failing to interpret the results of quadratic regression correctly.

Q: How do I perform quadratic regression in a programming language?

A: To perform quadratic regression in a programming language, you need to use a statistical library or package that supports quadratic regression. For example, in R, you can use the lm() function to perform linear regression, and then use the poly() function to add a quadratic term to the model.

Conclusion

In this article, we have answered some frequently asked questions about quadratic regression. Quadratic regression is a powerful tool for modeling non-linear relationships between variables, and it has many applications in statistics and data analysis. However, it can be sensitive to outliers in the data and affected by the presence of multicollinearity in the data. Therefore, it is essential to check for these issues before performing quadratic regression.

References

  • Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied Linear Statistical Models. McGraw-Hill.
  • Draper, N. R., & Smith, H. (1998). Applied Regression Analysis. John Wiley & Sons.
  • Weisberg, S. (2005). Applied Linear Regression. John Wiley & Sons.