Questions: The table contains price-supply data and price-demand data for com. Find a linear regression model for the price-supply data where x is supply (in billions of bushels) and y is price (in dollars). Do the same for the price-demand data. Find the equilibrium price for com. Price ( / bu) Supply (billion bu) Price ( / bu ) Demand (billion bu) 2.12 6.43 2.07 9.88 2.29 7.45 2.14 9.28 2.32 7.73 2.29 8.36 2.49 7.92 2.33 8.21 2.42 8.16 2.33 7.72 2.53 8.37 2.48 6.86 Find a linear regression model for the price-supply data where x is supply (in billions of bushels) and y is price (in dollars). y= x+ (Round to two decimal places as needed.)

Solution Steps

To find a linear regression model for the price-supply data, we need to fit a line to the given data points where the supply is the independent variable (x) and the price is the dependent variable (y). We can use Python's numpy library to perform this linear regression.

1. Extract the supply and price data from the table.
2. Use numpy to perform linear regression and find the slope and intercept of the best-fit line.
3. Round the slope and intercept to two decimal places.

We start by extracting the supply and price data from the table. The supply data is given in billions of bushels, and the price data is given in dollars.

We perform linear regression to find the best-fit line for the price-supply data. The linear regression model is of the form \( y = mx + b \), where \( m \) is the slope and \( b \) is the intercept.

Using the data provided:

• Supply: \( [6.43, 7.45, 7.73, 7.92, 8.16, 8.37] \)
• Price: \( [2.12, 2.29, 2.32, 2.49, 2.42, 2.53] \)

The slope (\( m \)) and intercept (\( b \)) are calculated as follows:

• \( m = 0.21 \)
• \( b = 0.78 \)

Final Answer