When minimizing the sum of squared errors J(w) = minw Σ1 (ƒ(x¡; w) — yi) for Least Mean Squares we want to update our weights using which of the following: O the integral O the postive gradient O the negative gradient O the closed form equation

Randomly generate 15 numbers rounded to 2 decimal places using the Numpy random function “normal()” with a mean of 20 and standard deviation of 5. Use seed of 1. Create a Pandas series of these 15 randomly generated numbers. Suppose that these numbers represent hourly wages of fifteen workers. Use a Pandas functions determine the average wage rate and the corresponding sample standard deviation Use a Boolean mask and Pandas function determine how many workers make less than $15 per hour Use a Boolean mask and Pandas function generate summary descriptive statistics for those earning less than $20 an hour Suppose that 5% raise for all individuals is approved. Create a new Pandas series for the new wage rate rounded to 2 decimal places