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In a normal distribution if the mean and the mode are both 25 what would the median be?
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How do you find the median with a Evan set of numbers?
You would answer it like a normal problem if you were doing both even and odd
Why the normal distribution curve have extremes is opened?
Because the domain of the normal distribution is infinite - in both directions.
How does the standard normal distribution differ from the t-distribution?
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
Comparision between binomial plus normal plus hypergeometric distribution?
The binomial distribution is a discrete probability distribution which describes the number of successes in a sequence of draws from a finite population, with replacement. The hypergeometric distribution is similar except that it deals with draws without replacement. For sufficiently large populations the Normal distribution is a good approximation for both.
Which jobs use the median?
It is very hard to generalize in this manner. There are times when a set of data is best described by the median. Frequently, both the mean and the median are calculated, and presented. It is not the profession but the characteristics of the data which determines the choice. A tightly controlled experiment, where random effects are small, such as in chemistry or physics would most likely report the mean rather than the median, although both may reported. Remember that median is a robust measure of the center, in that a few numbers which are numerically distant from the normal range will not affect results as much as the numerical average. Frequently, the median home price are stated to avoid unrepresentative (for most of us!) extremely expensive homes. Stating the median salary for a particular group (profession, years of experience, education) would diminish the affects of extreme values. The median income for different countries will not be affected by a very small percentage of the wealthest people in the country. The statistician would report the median when appropriate. For the examples provided, a market analyst involved in housing prices would normally use the median. An economist or financial analyst involved in analyzing salaries or incomes, would also use the median. The list of professions where the data would be more appropriately represented by the median is long. It includes epidemiologists, engineers, astronomers, social workers, and accountants. Even a fisherman, who wants to report how many fish he catches on a "normal day" would use the median.
How the values of mean and median relate to shape?
The question is how do the mean and median affect the distribution shape. In a normal curve, the mean and median are both in the same point. ( as is the mode) If a distribution is skewed, its tail is either on the right or the left. If a distribution is skewed the median may be a better value to use than the mean since it has less effect on the shape. Also is there are large outliers, the median has less effect and is better to use. So the mean has a bigger effect on the shape many times than the median.
When you calculate mean is the answer same when the same data is calculated by median?
No. The mean and median are not necessarily the same. They will be the same if the distribution is symmetric but the converse is not necessarily true. That is to say, a distribution does not have to be symmetric for the mean and median to be the same. For example, the mean and median of {1, 1, 5, 6, 12} are both 5 but the distribution is NOT symmetric.
How do you find the median with a Evan set of numbers?
You would answer it like a normal problem if you were doing both even and odd
Why the normal distribution curve have extremes is opened?
Because the domain of the normal distribution is infinite - in both directions.
A lopsided distribution of scores in which the mean is much larger than both the mode and median is said to be?
skewed.
Why calculate for the mean and median in relation to a sample?
Both the mean and median represent the center of a distribution. Calculating the mean is easier, but may be more affected by outliers or extreme values. The median is more robust.
How does the standard normal distribution differ from the t-distribution?
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
What is the important similarity between the uniform and normal probability distribution?
They are both continuous, symmetric distribution functions.
What is asymmetric lighting?
It is a type of light distribution. Symmetric distribution would be like normal down lighting with distribution to cover all surfaces. Asymmeteric distribution would be a down light that has distribution both down and to one side to cover a specific surface or object. Like a spot light or a wall wash fixture.
Is Mean is more sensitive to skewness than the median?
If the distribution is positively skewed , then the mean will always be the highest estimate of central tendency and the mode will always be the lowest estimate of central tendency (If it is a uni-modal distribution). If the distribution is negatively skewed then mean will always be the lowest estimate of central tendency and the mode will be the highest estimate of central tendency. In both positive and negative skewed distribution the median will always be between the mean and the mode. If a distribution is less symmetrical and more skewed, you are better of using the median over the mean.
Why two tails of the normal probability distribution extend indefinitely and never touch the horizontal axis?
This is because the normal distribution has a domain that extends to infinity in both directions.
What are some applications of normal distribution in your daily life?
Following are some applications:- 1)Computing grades from test scores by using the bell curve to find the average. 2)Same applies to any other normally distrubuted quantity like height,weight etc. • The normal distribution is a distribution that is centered around an average value with an even spread in both directions (standard deviation). • This makes the distribution symmetrical! • This symmetry causes the mean, median, and mode to be the exact same value. • Symmetry will come in handy when calculating probabilities.
