What is the predicted response value?

MATLAB: An Introduction with Applications
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### Regression Analysis on Bivariate Data

This section of the educational website will walk you through performing a regression analysis on a given set of bivariate data, with the response variable denoted as \( y \).

#### Data Set

The table below displays the bivariate data with variables \( x \) and \( y \):

| \( x \)  | \( y \)   |
|---------|---------|
| 67.7    | -25.7   |
| 72.5    | -5      |
| 55.8    | 21.3    |
| 64.1    | 11      |
| 70.9    | 64.9    |
| 61.6    | -25     |
| 65.5    | 28.7    |
| 73.6    | 84.6    |
| 68.1    | 15      |
| 70.1    | 75.3    |
| 72.4    | 84.4    |
| 56.1    | 92      |
| 55      | -5.4    |
| 72.5    | 16.5    |
| 72      | 38.7    |
| 64      | 38.7    |
| 70.8    | -15.7   |

#### Instructions

The task is to predict the average value for the response variable \( y \) given an explanatory variable \( x \) value of 63.4. We will use a significance level of \( \alpha = 0.05 \) to assess the strength of the linear correlation between \( x \) and \( y \).

##### Steps to Follow:

1. **Data Plotting and Visualization:**
   - Plot the dataset on a scatter plot.
   - Analyze the trend and linearity of the data points.

2. **Calculate the Regression Equation:**
   - Determine the slope (\( b \)) and intercept (\( a \)) of the regression line using formulas:

\[ 
b = \frac{n\sum{(xy)} - (\sum{x})(\sum{y})}{n\sum{x^2} - (\sum{x})^2} 
\]

\[ 
a = \frac{\sum{y} - b(\sum{x})}{n} 
\]

3. **Correlation Coefficient
Transcribed Image Text:### Regression Analysis on Bivariate Data This section of the educational website will walk you through performing a regression analysis on a given set of bivariate data, with the response variable denoted as \( y \). #### Data Set The table below displays the bivariate data with variables \( x \) and \( y \): | \( x \) | \( y \) | |---------|---------| | 67.7 | -25.7 | | 72.5 | -5 | | 55.8 | 21.3 | | 64.1 | 11 | | 70.9 | 64.9 | | 61.6 | -25 | | 65.5 | 28.7 | | 73.6 | 84.6 | | 68.1 | 15 | | 70.1 | 75.3 | | 72.4 | 84.4 | | 56.1 | 92 | | 55 | -5.4 | | 72.5 | 16.5 | | 72 | 38.7 | | 64 | 38.7 | | 70.8 | -15.7 | #### Instructions The task is to predict the average value for the response variable \( y \) given an explanatory variable \( x \) value of 63.4. We will use a significance level of \( \alpha = 0.05 \) to assess the strength of the linear correlation between \( x \) and \( y \). ##### Steps to Follow: 1. **Data Plotting and Visualization:** - Plot the dataset on a scatter plot. - Analyze the trend and linearity of the data points. 2. **Calculate the Regression Equation:** - Determine the slope (\( b \)) and intercept (\( a \)) of the regression line using formulas: \[ b = \frac{n\sum{(xy)} - (\sum{x})(\sum{y})}{n\sum{x^2} - (\sum{x})^2} \] \[ a = \frac{\sum{y} - b(\sum{x})}{n} \] 3. **Correlation Coefficient
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