The annual profits for a company are given in the following table, where [tex]\( x \)[/tex] represents the number of years since 2012, and [tex]\( y \)[/tex] represents the profit in thousands of dollars.

Write the linear regression equation that represents this set of data, rounding all coefficients to the nearest tenth. Using this equation, find the projected profit (in thousands of dollars) for 2021, rounded to the nearest thousand dollars.

[tex]\[
\begin{tabular}{|c|c|}
\hline
Years since 2012 (x) & Profits (y) \\
& (in thousands of dollars) \\
\hline
0 & 127 \\
\hline
1 & 126 \\
\hline
2 & 151 \\
\hline
3 & 169 \\
\hline
\end{tabular}
\][/tex]

Regression Equation: [tex]\(\square\)[/tex]

Final Answer: [tex]\(\square\)[/tex] thousand dollars



Answer :

To solve this problem, we need to find the linear regression equation of the form [tex]\( y = mx + b \)[/tex], where:
- [tex]\( y \)[/tex] is the profit in thousands of dollars,
- [tex]\( x \)[/tex] is the number of years since 2012,
- [tex]\( m \)[/tex] is the slope of the line,
- [tex]\( b \)[/tex] is the y-intercept of the line.

Given data points:

[tex]\[ (x_i, y_i): \quad (0, 127), (1, 126), (2, 151), (3, 169) \][/tex]

### Step 1: Calculate the Means
First, we calculate the means of the [tex]\( x \)[/tex] and [tex]\( y \)[/tex] values.

[tex]\[ \bar{x} = \frac{\sum x_i}{n} = \frac{0 + 1 + 2 + 3}{4} = \frac{6}{4} = 1.5 \][/tex]

[tex]\[ \bar{y} = \frac{\sum y_i}{n} = \frac{127 + 126 + 151 + 169}{4} = \frac{573}{4} = 143.25 \][/tex]

### Step 2: Calculate the Slope (m) and Intercept (b)
The formulas for the slope [tex]\( m \)[/tex] and intercept [tex]\( b \)[/tex] are:

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

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

To find [tex]\( m \)[/tex], we first need to compute the components:

[tex]\[ \sum (x_i - \bar{x})(y_i - \bar{y}) = (0-1.5)(127-143.25) + (1-1.5)(126-143.25) + (2-1.5)(151-143.25) + (3-1.5)(169-143.25) \][/tex]

[tex]\[ \sum (x_i - \bar{x})(y_i - \bar{y}) = (-1.5)(-16.25) + (-0.5)(-17.25) + (0.5)(7.75) + (1.5)(25.75) \][/tex]

[tex]\[ \sum (x_i - \bar{x})(y_i - \bar{y}) = 24.375 + 8.625 + 3.875 + 38.625 = 75.5 \][/tex]

Next, we find [tex]\( \sum (x_i - \bar{x})^2 \)[/tex]:

[tex]\[ \sum (x_i - \bar{x})^2 = (0-1.5)^2 + (1-1.5)^2 + (2-1.5)^2 + (3-1.5)^2 \][/tex]

[tex]\[ \sum (x_i - \bar{x})^2 = 2.25 + 0.25 + 0.25 + 2.25 = 5 \][/tex]

Now, calculate the slope [tex]\( m \)[/tex]:

[tex]\[ m = \frac{75.5}{5} = 15.1 \][/tex]

Finally, calculate the intercept [tex]\( b \)[/tex]:

[tex]\[ b = \bar{y} - m\bar{x} = 143.25 - (15.1 \cdot 1.5) = 143.25 - 22.65 = 120.6 \][/tex]

### Step 3: Write the Linear Regression Equation
The linear regression equation is:

[tex]\[ y = 15.1x + 120.6 \][/tex]

### Step 4: Project the Profit for 2021
To find the profit for 2021, note that 2021 is 9 years since 2012 ([tex]\( x = 9 \)[/tex]):

[tex]\[ y = 15.1 \cdot 9 + 120.6 \][/tex]

[tex]\[ y = 135.9 + 120.6 = 256.5 \][/tex]

Rounding to the nearest thousand dollars:

[tex]\[ y \approx 257 \ \text{thousand dollars} \][/tex]

### Final Answer
- Regression Equation: [tex]\( y = 15.1x + 120.6 \)[/tex]
- Projected Profit for 2021: [tex]\( 257 \ \text{thousand dollars} \)[/tex]

Please fill in the answer slots as required:

- Regression Equation: [tex]\( y = 15.1x + 120.6 \)[/tex]
- Final Answer: [tex]\( 257 \ \text{thousand dollars} \)[/tex]