Run the follwoing codes to get the output figure. Then change to codes to generate a histogram to show age data distribution around 50 with the number of 100 bins. After that, save the histogram to be 'png'. import matplotlib. pyplot as plt
import numpy as np

np. random. seed(5)
ages = [np. random. normal(loc=40,scale=10) for x in range(100)]
#ages distributed around 40
distributed around 40
plt. hist(ages, bins=45) #bins is the number of bars
plt. title("Ages per Population")
plt. xlabel("ages")
plt. ylabel("# of people")
plt. show()

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answeroperating system;

the benefits of raising bc’s minimum wage to $15 through a

series of pre-announced staged increases far outweigh the likely

costs. claims that such an increase will lead to massive job losses

in low-wage sectors of the economy are not credible.

• an increase to $15 would result in some job losses. however,

the likely impact is less than a 1 per cent reduction in the overall

provincial employment rate, and a 7.6 per cent reduction in the

employment rate for those currently making between $10.25

and $15 per hour.

• the large job loss impacts predicted by some opponents of

minimum wages misrepresent the existing economic research. in

reading and using that research, it is important to recognize that

estimates showing job loss effects of minimum wage increases

apply only to teenagers. estimated effects for young adult and

adult workers range from insignificant to non-existent.

• focusing on teenage workers plays up an inaccurate stereotype

about who earns very low wages in bc and leads to exaggerated

claims about job losses. only 26 per cent of those currently making

between $10.25 and $12 are teenagers, and only 5 per cent

of those making between $12 and $15 are teenagers.