Junior: Freshman: Normal: Mean=23.792, SD=4.4951 Frequency 12! 12 Normal: Mean-23.12, SD-3.8004 Frequency 10 10- 8 To 15 20 25 30 35 МАТH 15 20 30 33 ΜΑΤΗ Senior: Sophomore: Normal: Mean-22.847, SD-3.9558 Frequency 16 16 15 10 20 25 МАТH 30 15
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!
Which of these graphs show a
![Junior:
Freshman:
Normal: Mean=23.792, SD=4.4951
Frequency
12
12
Normal: Mean=23.12, SD=3.8004
Frequency
10
10-
6
8
7
7
5.
6.
5
3
To
15
20
25
30
35
MАTH
15
20
30
ΜΑΤΗ
Senior:
Sophomore:
Normal: Meun=21.222, SD-3.2608
Frequeacy
Normal: Mean=22.847, SD=3.9558
Frequency
16
16
15
10
2'5
MATH
15
20
25
30
35
МАTH](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F17733f7e-34f8-47f0-bdc3-883ee9235e3e%2Fededfd6e-1314-4961-a779-073c0a73b17f%2Fxv5tz8d_processed.png&w=3840&q=75)
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