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The probability is 0 since it did not happen.

The probability is 0 since it did not happen.

The probability is 0 since it did not happen.

The probability is 0 since it did not happen.

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10y ago
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10y ago

The probability is 0 since it did not happen.

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Q: What is the probability of a horse winning the triple crown 2013?
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What is probability a horse will win a race if it has odds of 5 to 1?

5 to 1 to win means the probability of actually winning is 1 to 5, or 0.2. The larger the payout, the longer (or poorer) the odds. Actually, 5 to 1 means 1 chance in 6 or 16.7%. Of course, race track odds may not be the true probablity of winning.


What does 30 f in the gn mean?

The Answer is really an English answer. In the UK our National Horse race is The Grand National and it contains 30 fences.Therefore the answer is 30 Fences in the Grand National


What are four types of articulation errors?

A child can make the following articulation errors when producing speech sounds: Substitutions, Omissions, Distortions, and/or Additions. An easy way to remember these is to use the acronym SODA!Definition: Replace one sound with another sound.Examples: "wed" for "red," "thoap" for "soap," "dut," for "duck"Definition: Omit a sound in a word.Note: This error affects intelligibility the most, making speech more difficult for the listener(s) to understand.Examples: "p ay the piano" for "play the piano", "g een nake" for "green snake"Definition: Produce a sound in an unfamiliar manner.Examples: "pencil" (nasalized-sounds more like an "m") for "pencil," "sun" (lisped-sounds "slushy") for "sun"Definition: Insert an extra sound within a word.Examples: "buhlack horse" for "black horse," "doguh," for "dog"


What are some examples of ratios or proportions that can be useful in our daily life?

Examples are:a ratio can also show a part compared to the whole lot. For example there are 5 pups, 2 are boys and 3 are girls, then the ratio of boys to girls 2/3, the ratio of boys to total is 2/5, the ratio of girls to total is 3/5.a ratio can also be used in drawings. For example to draw a horse with a scale 1/10 from its normal size. Another example is that the height to width ratio of the Indian Flag is 2:3 or 2/3Another examples are:1/2, 3/4, 78/89, ... etcx/(x+1), y/(y-2), ... etc.


What is the appropriate measure of variability for ordinal data?

This is a surprisingly difficult question, partly perhaps because of the ambiguous term 'ordinal'. For instance, horse-race finishes are ordinal--horses usually finish first, second, etc., with no ties; pure order data gives no information about gaps between horses. For such data--the purest form of ordinal data--talk about variability is meaningless. You need data with 'ties' or repeated 'values'--more cases than ordered categories--to talk about variability meaningfully. If you do have repeated values, one option is to fall back and use nominal variability measures--the Index of Qualitative Variation is one; information statistics also work; and there's always the frequency/percentage table. They don't 'measure' concentration along the categoric order, obviously. Disappointingly many websites recommend using the range or interquartile range, presumably calculated by assigning numbers to the ordered categories and subtracting. These indices are very dangerous if you assume only qualitative order among categories. This is obviously flawed--if you don't know how far categories are separated, subtracting numbers is flat invalid. For instance, rank states in the US by size--Alaska is 1, RI is 50--and consider the fact that a group from AK, TX, and CA has a range of 2 and a group from NJ and MA has range of 3 [47 - 44]. First, those numbers are really meaningless; second, they sure misrepresent relations among state size differences. Unless you trust that your 'ordinal' categories are pretty close to equal intervals apart--what we call 'quasi-interval'--you simply cannot use range validly to measure ordinal variability. The same reasoning applies to inter-quartile range. You might as well use variance, since describing 'skew' and 'outliers' for ordinal data is very dangerous, itself. More valid ordinal measures do exist--I cannot recall them. But when you choose an index, take care to examine how it is treating the numbers or other ordering symbols it trades on. Invalidity is rife.