Step 1: Organize the data

To find a linear regression model for the price-supply data, we will first organize the data. The table provides two sets of data### Step 1: Organize the data

To find a linear regression model for the price-supply data, we will first organize the data. The table provides two sets of

• Supply (x): in billions of bushels
• Price (y): in dollars per bushel

Here’s the price-supply data that we have:

Price (y)	Supply (x)
2.19	6.35
2.23	7.39
2.34	7.64
2.43	7.77
2.42	8.23
2.39	7.74
2.59	8.24
2.42	6.82

Step 2: Linear regression formula

The general formula for the linear regression line is: $y = mx + b$ Where:

• $m$ is the slope
• $b$ is the y-intercept

To find the slope ($m$) and intercept ($b$), we need to apply the following formulas:

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

Where $n$ is the number of data points.

Step 3: Compute values

Let's compute the necessary sums:

1. $\sum{x}$ (sum of supply values)
2. $\sum{y}$ (sum of price values)
3. $\sum{xy}$ (sum of products of price and supply)
4. $\sum{x^2}$ (sum of squared supply values)

I will now calculate the values.The linear regression model for the price-supply data is:

$y = 0.1334x + 1.3727$

Where:

• $x$ is the supply in billions of bushels
• $y$ is the price in dollars per bushel

Next, we will repeat the same process to find the linear regression model for the price-demand data. Would you like me to proceed with that part as well?