Suppose that we don't include an intercept term in our model. That is. our model is now simply if = we. where \"T is the single parameter for our model that we need to optimize. (In this equation. a: is a scalar. corresponding to a single observation.) As usual. we are looking to nd the value c} that minimizes the average squared loss (\"empirical risk\") across our observed data {[er..y.-)}.i = 1.. . . .n. Riv} = xin- van-i2 i=1 The normal equations derived in lecture no longer hold. In this problem. we'll derive a solution to this simpler model. We'll see that the least squares estimate of the slope in this model differs from the simple linear regression model. and will also explore whether or not our properties from the previous problem still hold. . (4 points) Use calculus to nd the minimizing c}. That is. prove that =z;miyi Es? Note: This is the slope of our regression line. analogous to 51 from our simple linear regression model