What is the mean of 1, 3, 5, 7,9. 11, 13?
Answers
Answer:
Answer is 7
Step-by-step explanation:
The mean of 1, 3, 5, 7, 9, 11, 13 is 7. We can easily solve this problem by following the given steps. Mean of the given data = 1+3+5+6+9+11+13/7 ( The total number of observations here is 7. ... Mean = 7.
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Answer:
The mean of 1, 3, 5, 7, 9, 11, 13 is 7. We can easily solve this problem by following the given steps. Mean of the given data = 1+3+5+6+9+11+13/7 ( The total number of observations here is 7. ...
Step-by-step explanation:
Mean of 3,5,7,9,11,13,15
No. of data =7
∴Mean=
7
3+5+7+9+11+13+15
=
7
63
=9
Arithmetic mean (AM)
The arithmetic mean (or simply mean) of a list of numbers, is the sum of all of the numbers divided by the number of numbers. Similarly, the mean of a sample
Equality holds if all the elements of the given sample are equal.
Statistical location
Comparison of the arithmetic mean, median, and mode of two skewed (log-normal) distributions.
Geometric visualization of the mode, median and mean of an arbitrary probability density function.[3]
In descriptive statistics, the mean may be confused with the median, mode or mid-range, as any of these may incorrectly be called an "average" (more formally, a measure of central tendency). The mean of a set of observations is the arithmetic average of the values; however, for skewed distributions, the mean is not necessarily the same as the middle value (median), or the most likely value (mode). For example, mean income is typically skewed upwards by a small number of people with very large incomes, so that the majority have an income lower than the mean. By contrast, the median income is the level at which half the population is below and half is above. The mode income is the most likely income and favors the larger number of people with lower incomes. While the median and mode are often more intuitive measures for such skewed data, many skewed distributions are in fact best described by their mean, including the exponential and Poisson distributions.
Mean of a probability distribution
Main article: Expected value
See also: Population mean
The mean of a probability distribution is the long-run arithmetic average value of a random variable having that distribution. If the random variable is denoted by
f(x) is the probability density function.[4] In all cases, including those in which the distribution is neither discrete nor continuous, the mean is the Lebesgue integral of the random variable with respect to its probability measure. The mean need not exist or be finite; for some probability distributions the mean is infinite (+∞ or −∞), while for others the mean is undefined.
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