Probability

P(A)=.35 P(B given A)=0.6 P(A and B)= ?

The sum should equal to 1.

The mean of a proportion, p, is X/n; where X is the number of instances & n is the sample size; and its standard deviation is sqrt[p(1-p)]

A variable that shows serious departure from the classic bell-shaped, or "Gaussian", curve is described as being not normally distributed. This departure could take the form of skew and/or kurtosis and/or multi modality.

An example might be weekly wages. If you drew a histogram of a population's earnings you would most likely see a distribution skewed significantly toward the right. That is, toward the higher incomes.

Another example is height. If you drew a histogram of a population's height you would see a bimodal distribution. One peak for males and another peak for females. The distribution of height for males and females might be normal when looked at individually, but not normal when you combine them.

The probability is one in fifty-two.

P(7 of Clubs) or P(7 of Spades) = 1/52 + 1/52 = 0.0385

or 1/26 chance

good question.

a straight, vertical line, i.e., zero variability

A random variable is a function that assigns unique numerical values to all possible outcomes of a random experiment.

A real valued function defined on a sample space of an experiment is also called random variable.

Three outcomes are possible: THH or HTH or HHT, so the probability of getting exactly 2 heads from three tosses of a fair coin is 3x0.53=0.375

In general, the number of different ways of getting k heads from n coin tosses is given by n! / ( k! x (n-k)! ), which is known as the binomial coefficient. The "!" notation is "the factorial"; for some number s, s! = s x (s-1) x (s-2) ... 2 x 1, so that 3! = 3x2x1, 5! = 5x4x3x2x1 and so on.

Thus your question can be answered by replacing n and k in the formula above, such that the number of ways is 3! / ( 2! x 1!) = 3. This is true for all experiments where you perform some experiment with two possible outcomes n times and record how many times one of the events occurs. Another typical example would be the number of times you roll a 4 after 10 rolls of a dice, and there are 10! / 6!x4! = 210 ways of doing this.

Stockbrokers and traders are human beings. They may read an event differently, have different predictions on it's consequences, or react to it differently based on emotions, media attention, or something else entirely.

Off the top of my head, a perfect F-ratio would be 1.00 which is never possible. All F-ratios will be greater than one so the numerator has to be greater than denominator.

This is called a random sample.

It is one of the informal definitions for an outlier.

Imagine you have four empty buckets in which to put any of the four letters Y, A, R, D into. At first you have four letters that can be placed in any of the four empty buckets. Once you've placed a letter in a bucket you only have three letters and three empty buckets to choose from. And so on... So there are 4x3x2x1 = 24 permutations of the word YARD.

You may be referring to the statistical term 'outlier(s)'. Also, there is a rule in statistics called the '68-95-99 Rule'. It states that in a normally distributed dataset approximately 68% of the observations will be within plus/minus one standard deviation of the mean, 95% within plus/minus two standard deviations, and 99% within plus/minus three standard deviations. So if your data follow the classic bell-shaped curve, roughly 1% of the measures should fall beyond three standard deviations of the mean.

wow great question, well for sure that i know basketball has alot to do with math, well, it has everything to do with math. the angles and the height of basketball player the strength used to throw the ball and pretty much everything else. Also, it has to do a to with angles

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when you doesnt have information about the real mean of a population and use the estimation of mean instead of the real mean , usually you use t distribution instead of normal distribution.

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Intersting but nothing to do with the question!

If a random variable X is distributed Normally with mean m and standard deviation s, then Z = (X-m)/s has a standard Normal distribution.

Z has mean 0 and standard deviation = 1 (or Variance = sd2 = 1).

The cause of skewed data distributions are extreme values, also know as outliers. For example imagine taking the weights of people you see on the street. If you have 9 cheerleaders' weights and then the weight of a sumo wrestler mixed into the averages this skews the data. This makes the mean much higher because of the one extreme value. Instead of the data being distributed normally, it is distributed with a positive skew. If there is a really small extreme value instead of a really large one, then the data has a negative skew. This could be the heights of people on the street, but one of them would be a midget. The mean is made lower by that one extreme value.

Perhaps, little person is a more politically correct term in our day.

whatis normal distribution

well, i play basket ball and to tell you the truth, it's 50 50 on that. But that's if the players have had the same amount of experience. Boys and girls are both great at sinking' hoops.

I would expect it to land on a number of 4 or higher 50% of the time or roughly 24 times.