MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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### Regression Analysis with Dependent and Independent Variables

Consider the following data for a dependent variable \( y \) and two independent variables, \( x_1 \) and \( x_2 \).

\[
\begin{array}{ccc}
x_1 & x_2 & y \\
\hline
29 & 13 & 95 \\
47 & 10 & 109 \\
25 & 18 & 113 \\
51 & 16 & 178 \\
40 & 6 & 94 \\
52 & 20 & 175 \\
75 & 7 & 170 \\
37 & 12 & 117 \\
59 & 14 & 142 \\
77 & 16 & 211 \\
\end{array}
\]

Round all your answers to two decimal places. Enter negative values as negative numbers, if necessary.

#### a. Develop an estimated regression equation relating \( y \) to \( x_1 \).

\[
\hat{y} = \_\_\_\_\_\_\_\_\_\_ + \_\_\_\_\_\_\_\_\_\_ x_1
\]

**Predict \( y \) if \( x_1 = 35 \).**

\[
\hat{y} = \_\_\_\_\_\_\_
\]

#### b. Develop an estimated regression equation relating \( y \) to \( x_2 \).

\[
\hat{y} = \_\_\_\_\_\_\_\_\_\_ + \_\_\_\_\_\_\_\_\_\_ x_2
\]

**Predict \( y \) if \( x_2 = 15 \).**

\[
\hat{y} = \_\_\_\_\_\_\_
\]

#### c. Develop an estimated regression equation relating \( y \) to \( x_1 \) and \( x_2 \).

\[
\hat{y} = \_\_\_\_\_\_\_\_\_\_ + \_\_\_\_\_\_\_\_\_\_ x_1 + \_\_\_\_\_\_\_\_\_\_ x_2
\]

**Predict \( y \) if \( x_1 = 35 \) and \( x_2 = 15 \).**

\[
\hat{y} = \_\_\_\_\_\_\_
\]
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Transcribed Image Text:### Regression Analysis with Dependent and Independent Variables Consider the following data for a dependent variable \( y \) and two independent variables, \( x_1 \) and \( x_2 \). \[ \begin{array}{ccc} x_1 & x_2 & y \\ \hline 29 & 13 & 95 \\ 47 & 10 & 109 \\ 25 & 18 & 113 \\ 51 & 16 & 178 \\ 40 & 6 & 94 \\ 52 & 20 & 175 \\ 75 & 7 & 170 \\ 37 & 12 & 117 \\ 59 & 14 & 142 \\ 77 & 16 & 211 \\ \end{array} \] Round all your answers to two decimal places. Enter negative values as negative numbers, if necessary. #### a. Develop an estimated regression equation relating \( y \) to \( x_1 \). \[ \hat{y} = \_\_\_\_\_\_\_\_\_\_ + \_\_\_\_\_\_\_\_\_\_ x_1 \] **Predict \( y \) if \( x_1 = 35 \).** \[ \hat{y} = \_\_\_\_\_\_\_ \] #### b. Develop an estimated regression equation relating \( y \) to \( x_2 \). \[ \hat{y} = \_\_\_\_\_\_\_\_\_\_ + \_\_\_\_\_\_\_\_\_\_ x_2 \] **Predict \( y \) if \( x_2 = 15 \).** \[ \hat{y} = \_\_\_\_\_\_\_ \] #### c. Develop an estimated regression equation relating \( y \) to \( x_1 \) and \( x_2 \). \[ \hat{y} = \_\_\_\_\_\_\_\_\_\_ + \_\_\_\_\_\_\_\_\_\_ x_1 + \_\_\_\_\_\_\_\_\_\_ x_2 \] **Predict \( y \) if \( x_1 = 35 \) and \( x_2 = 15 \).** \[ \hat{y} = \_\_\_\_\_\_\_ \]
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