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Anuta_ua [19.1K]
3 years ago
8

Two number cubes whose sides are numbered 1 through 6 are rolled on a table. The two numbers showing are added. If you repeat th

is process 300 times, how many times would you expect the two cubes to add to exactly 7? ASAP​
Mathematics
2 answers:
ankoles [38]3 years ago
8 0

Answer:

50 times.

Step-by-step explanation:

When the 2 number cubes are rolled once:

The possible outcomes for a total of 7 are:

1 and 6

6 and 1

2 and 5

5 and 2

3 and 4

4 and 3

= 6 outcomes.

The total of all possible outcomes = 6 * 6 = 36.

So Prob( total of 7 in one throw of teh 2 cubes) =  6/36 = 1/6.

For 300 throws you would expect the total of 7 to occur

1/6 * 300 = 50.

spin [16.1K]3 years ago
4 0
50 times would be your answer. Hope this helps.
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2 years ago
Simon had 2 cups of jelly he put the jelly in to 1/3 cup jars how many jars of jelly did he make
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4 0
3 years ago
s) An e-mail lter is planned to separate valid e-mails from spam. The word free occurs in 50% of the spam messages and only 3% o
balu736 [363]

Answer:

A) P(F) = 0.124

B) P(S|F) = 0.8065

C) P(V|F^(c)) = 0.886

Step-by-step explanation:

Let us denote as follows;

F = Message contains word free

S = message is spam

V = message is valid

From the question, we are given that;

The probability that word free occurs in spam messages;P(F|S) = 50% = 0.5

The probability of the valid messages that contain free; P(F|V) = 3% = 0.03

Spam messages; P(S) = 20% = 0.2

Valid messages; P(V) = 1 - 0.2 = 0.8

A) From rule of total probability ;

probability that the message contains the word free is given as;

P(F) = P(F|S)•P(S) + P(F|V)•P(V)

P(F) = (0.5 x 0.2) + (0.03 x 0.8)

P(F) = 0.124

B) From Baye's theorem;

probability that the message is spam given that it contains free is given as;

P(S|F) = P(F|S)•P(S)/P(F)

P(S|F) = (0.5 x 0.2)/0.124

P(S|F) = 0.8065

C) From combination of complement rule and Baye's theorem;

probability that the message is valid given that it does not contain free is given as;

P(V|F^(c)) = P(F^(c)|V)•P(V)/P(F^(c))

Thus,

P(V|F^(c)) = [(1 - P(F|V))•P(V)]/(1 - P(F))

P(V|F^(c)) = ((1 - 0.03)•0.8)/(1 - 0.124)

P(V|F^(c)) = 0.776/0.876

P(V|F^(c)) = 0.886

5 0
4 years ago
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