Select the correct answer Raw scores on a certain standardized test one year were normally distributed, with a mean of 156 and a standard deviation of 23. If 48 592 students took the test, about how many of the students scored less than 96? Toble shows values to the LEFT of the score 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 2.5 0.99379 0.99396 0.994130.99430 0.99446 0.99461 0.99477 0.99492 2.6 0.99534 0.99547 0.99560 0.99573 0.99585 0.99598 0.99609 0.99621 2.7 0.99653 0.99664 0.99674 0.996830.99693 0.99702 2.8 0.99744 0.99752 0.99760 0.99767 0.99774 0.99781 0.99768 0.99795 2.9 0.998130.99819 0.99825 0.99831 0.99836 0.99841 0.99846 0.99851 0.001870.00181 0.00175 0.00169 0.00164 0.00159 0.00154 0.00149 -2.8 0.00256 0.00248 0.00240 0.00233 0.00226 -2.7 0.00347 0.00336 0.00326 -2.6 0.00466 0.00453 0.00440 0.00427 0..00415 0.00402 0.00391 0.00379 -2.5 0.006210.00604 0.00587 0.005700.00554 0.00539 0.005230.00508 0.99711 0.99720 -2.9 0.00212 0.00205 0.00317 0.00307 0.002980.00269 0.00280 0.00219 OR19312 OC 220 OD1862
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
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