**Demand = (Price * Price Coefficient) + Slope**

## Step 1: Create the Parameter

## Step 2: Create a Measure that Calculates the New Demand with the Parameter

` ````
```New_Forecast = SUMX(Sheet1, IF( Sheet1[Slope] + ( (1 + 'Price Parameter'[Price Parameter Value]) * Sheet1[Price] * Sheet1[Price Coefficient]) > 0, Sheet1[Slope] + ( (1 + 'Price Parameter'[Price Parameter Value]) * Sheet1[Price] * Sheet1[Price Coefficient]), 0) )

## Step 3: Build Graphs to Visualize What-If Analysis

When we increase the price by 20% across the board, we see the overall demand decrease. It looks like the apple demand dropped the most, which would make sense because it had the largest coefficient.

When we increase the price by 150%, we see the demand drop even more. For pumpkins and apples, we see there is a demand of 0 for the first two weeks. This is a result from flooring the demand to 0 when the output is negative from that what-if analysis.

Now let’s see what happens when we decrease the price by 20%.

As price decreases, demand is increasing, suggesting that customers would buy more at a lower price.

When we drop the price by 150%, we can see the impact even more. While the demand significantly increase for apples and pumpkins, squash only increased by a little. This makes sense because the price coefficient for squash is the smallest of the three products.