1. Many of the continuous random variables that occur in real life are approximately normally distributed. How do you think this helps in studying real life variables? 2. Do you realize and understand the advantage of being able to convert values of an arbitrary normally distributed random variable to the corresponding values of the standard normal variable? If yes, list all the possible advantages of being able to do this. If not what do you plan to do to understand why the standard normal random variable is widely used for computations involving normal distributions?

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Ted and meg have each drawn a line on the scatter plot shown below: the graph shows numbers from 0 to 10 on x and y axes at increments of 1. dots are made at the ordered pairs 1, 8 and 2, 7.5 and 3, 6 and 4, 5.5 and 4.5, 4.8 and 5, 4 and 6, 3.5 and 7, 3 and 8, 1.9 and 9, 1.2 and 10, 0. a straight line labeled line p joins the ordered pairs 0, 8.5 and 10.1, 0. a straight line labeled line r joins the ordered pairs 0, 8.5 and 7.8, 0. which line best represents the line of best fit? line p, because it is closest to most data points line p, because it shows a positive association line r, because it is closest to most data points line r, because it shows a negative association