Use the linear regression feature on a graphing calculator to find an equation of the line of best fit and the correlation coefficient for the data. Round all values to the nearest hundredth.

The equation of the line of best fit for the data is [tex] y = 0.42x + 1.44 [/tex].

The correlation coefficient is [tex] r = 0.61 [/tex].



Answer :

Let's go through the steps to find the equation of the line of best fit and the correlation coefficient for the given data points using the linear regression method.

### Given Data:
- Data points: (2, 144.42) and (4, 146.26)

### Step 1: Calculate the Mean of x and y
We start by finding the means of the x-values and y-values.

[tex]\[ x_1 = 2, \, x_2 = 4 \][/tex]
[tex]\[ y_1 = 144.42, \, y_2 = 146.26 \][/tex]

[tex]\[ \text{Mean of x} = \overline{x} = \frac{2+4}{2} = 3 \][/tex]
[tex]\[ \text{Mean of y} = \overline{y} = \frac{144.42+146.26}{2} = 145.34 \][/tex]

### Step 2: Calculate the Terms Needed for the Slope (m)
We use the formula for the slope [tex]\( m \)[/tex]:

[tex]\[ m = \frac{\sum{(x_i - \overline{x})(y_i - \overline{y})}}{\sum{(x_i - \overline{x})^2}} \][/tex]

For the numerator:

[tex]\[ \sum{(x_i - \overline{x})(y_i - \overline{y})} = (2 - 3)(144.42 - 145.34) + (4 - 3)(146.26 - 145.34) \][/tex]
[tex]\[ = (-1)(-0.92) + (1)(0.92) \][/tex]
[tex]\[ = 0.92 + 0.92 \][/tex]
[tex]\[ = 1.84 \][/tex]

For the denominator:

[tex]\[ \sum{(x_i - \overline{x})^2} = (2 - 3)^2 + (4 - 3)^2 \][/tex]
[tex]\[ = (-1)^2 + (1)^2 \][/tex]
[tex]\[ = 1 + 1 \][/tex]
[tex]\[ = 2 \][/tex]

Therefore, the slope [tex]\( m \)[/tex] is:

[tex]\[ m = \frac{1.84}{2} = 0.92 \][/tex]

### Step 3: Calculate the Intercept (b)
The intercept [tex]\( b \)[/tex] can be found using the formula:

[tex]\[ b = \overline{y} - m\overline{x} \][/tex]

[tex]\[ b = 145.34 - 0.92 \times 3 \][/tex]
[tex]\[ b = 145.34 - 2.76 \][/tex]
[tex]\[ b = 142.58 \][/tex]

### Step 4: Write the Equation of the Line of Best Fit
Combining our slope [tex]\( m = 0.92 \)[/tex] and intercept [tex]\( b = 142.58 \)[/tex], we have the equation of the line of best fit:

[tex]\[ y = 0.92x + 142.58 \][/tex]

### Step 5: Calculate the Correlation Coefficient (r)
The correlation coefficient [tex]\( r \)[/tex] is calculated using the formula:

[tex]\[ r = \frac{\sum{(x_i - \overline{x})(y_i - \overline{y})}}{\sqrt{\sum{(x_i - \overline{x})^2} \cdot \sum{(y_i - \overline{y})^2}}} \][/tex]

We've already calculated:

[tex]\[ \sum{(x_i - \overline{x})(y_i - \overline{y})} = 1.84 \][/tex]
[tex]\[ \sum{(x_i - \overline{x})^2} = 2 \][/tex]

For:

[tex]\[ \sum{(y_i - \overline{y})^2} = (144.42 - 145.34)^2 + (146.26 - 145.34)^2 \][/tex]
[tex]\[ = (-0.92)^2 + (0.92)^2 \][/tex]
[tex]\[ = 0.8464 + 0.8464 \][/tex]
[tex]\[ = 1.6928 \][/tex]

Thus:

[tex]\[ r = \frac{1.84}{\sqrt{2 \cdot 1.6928}} \][/tex]
[tex]\[ r = \frac{1.84}{\sqrt{3.3856}} \][/tex]
[tex]\[ r = \frac{1.84}{1.84} \][/tex]
[tex]\[ r = 1.0 \][/tex]

### Summary
The equation of the line of best fit is:

[tex]\[ y = 0.92x + 142.58 \][/tex]

The correlation coefficient is:

[tex]\[ r = 1.0 \][/tex]