The answer depends on what the trial is and what the variable X represents.
The probability that a coin will result in heads in any one toss is 1/2. If you toss the coin three times, the probability that the coin will turn up heads each time is 1/2 x 1/2 x 1/2 or 1/8, which is 12.5%.
The probability that all will be graduates will be 5/20 x 4/19 x 3/18. This equals 0.0087719, or 0.877% The probability of at least one being a graduate is 1 minus the probability of none being graduates. That's 1-(15/20 x 14/19 x 13/18) = 1 - 0.39912280701754385964912280701754 = 0.600877 =60% Thus the probability of having at least one graduate is 60%
You carry out the experiment a large number of times. Count the number of times it was carried out (n). Count the number of times in which the particular outcome occurred (x). Then, the experimental probability for that even is x/n.
For a continuous distribution - which is what we are considering here - the probability of something being EXACTLY a specific value is zero. This is basically because there are infinite many possible values. You will only ever consider ranges of numbers, such as the probability of something being 1 and 2.
You obtain an estimate of the probability that will usually be different from previous result(s).You obtain an estimate of the probability that will usually be different from previous result(s).You obtain an estimate of the probability that will usually be different from previous result(s).You obtain an estimate of the probability that will usually be different from previous result(s).
For a continuous variable it is 0.5 but for a discrete variable the answer depends on the probability of the variable taking the mean value. It is half of the rest of the probability. If the discrete variable X has mean m and Prob(X = m) is p then Prob(X > m) = (1 - p)/2.
Let X and Y be two random variables.Case (1) - Discrete CaseIf P(X = x) denotes the probability that the random variable X takes the value x, then the joint probability of X and Y is P(X = x and Y = y).Case (2) - Continuous CaseIf P(a < X < b) is the probability of the random variable X taking a value in the real interval (a, b), then the joint probability of X and Y is P(a < X< b and c < Y < d).Basically joint probability is the probability of two events happening (or not).
The probability of the first card being red is 16 in 32. The probability of the second card being red is 15 in 31. The third is 14 in 30. The fourth is 13 in 29. Multiply these probabilities together and you get 16 x 15 x 14 x 13 in 32 x 31 x 30 x 29, which is equal to 43680 in 863040 or about 0.0506.
The power of a statistical test is defined as being a probability that a test will product a result that is significantly different. It can be defined as equaling the probability of rejecting the null hypothesis.
in what?
The probability of a single point being chosen is 0.The probability of a single point being chosen is 0.The probability of a single point being chosen is 0.The probability of a single point being chosen is 0.
IF probability of rain is X percent then probability of no rain is 100- X percent. For example if prob of rain is 80% prob of no rain is 20%