Experimental probability = 1/5
Theoretical probability = 1/4
note: 1/5 = 0.2 and 1/4 = 0.25
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How I got those values:
We have 12 hearts out of 60 cards total in our simulation or experiment. So 12/60 = (12*1)/(12*5) = 1/5 is the experimental probability. In the simulation, 1 in 5 cards were a heart.
Theoretically it should be 1 in 4, or 1/4, since we have 13 hearts out of 52 total leading to 13/52 = (13*1)/(13*4) = 1/4. This makes sense because there are four suits and each suit is equally likely.
The experimental probability and theoretical probability values are not likely to line up perfectly. However they should be fairly close assuming that you're working with a fair standard deck. The more simulations you perform, the closer the experimental probability is likely to approach the theoretical one.
For example, let's say you flip a coin 20 times and get 8 heads. We see that 8/20 = 0.40 is close to 0.50 which is the theoretical probability of getting heads. If you flip that same coin 100 times and get 46 heads, then 46/100 = 0.46 is the experimental probability which is close to 0.50, and that probability is likely to get closer if you flipped it say 1000 times or 10000 times.
In short, the experimental probability is what you observe when you do the experiment (or simulation). So it's actually pulling the cards out and writing down your results. Contrast with a theoretical probability is where you guess beforehand what the result might be based on assumptions. One such assumption being each card is equally likely.
Answer:
b=-1
Step-by-step explanation:
-7+b=-8
Add 7 to each side to isolate b
-7+7+b=-8+7
b = -1
|a| = -a if a < 0
The absolute value and the opposite of rational number is equal when a number is negative.
Examples:
|-7| = - (-7) = 7
|-0.5| = - (-0.5) = 0.5
Answer:
s = 
Step-by-step explanation:
s = ut + 
at² ( substitute the given values into the equation )
s = ( 3 × ) + ( × - 12 × ( )² )
= 1 + (- 6 × 
)
= 1 + ( - 
)
= 1 -
=
Answer: 0.01708
Step-by-step explanation:
Given : If records indicate that 15 houses out of 1000 are expected to be damaged by fire in any year.
i.e. the probability that house damaged buy fire in a year : 
The formula for binomial distribution is given by :-
Now, the probability that a woman who owns 14 houses will have fire damage in 2 of them in a year (put n=14 and x=2), we get
Hence, the required probability = 0.01708
