With 1000 rolls of a die, and each number having a probability of 1/6, I would not expect any peaks.
No. A distribution may be non-skewed and bimodal or skewed and bimodal. Bimodal means that the distribution has two modes, or two local maxima on the curve. Visually, one can see two peaks on the distribution curve. Mixture problems (combination of two random variables with different modes) can produce bimodal curves. See: http://en.wikipedia.org/wiki/Bimodal_distribution A distribution is skewed when the mean and median are different values. A distribution is negatively skewed when the mean is less than the median and positively skewed if the mean is greater than the median. See: http://en.wikipedia.org/wiki/Skewness
I suspect you are referring to a sample frequency distribution.Providing that the sample size is sufficiently large there are various kinds of information that can be gleaned from one:the approximate range of values in the populationthe location of the population as measured by the value that appears most often in the frequency distribution-known as its modethe likely shape of the population's distribution, in particular whether it is symmetric or skewedobviously how values of the population variable are distributedwhether there are any curious peaks or valleys, even when the sample size is largethe amount of variation around the central value
Well, honey, if you've got two classes strutting their stuff with the same high frequency in your grouped data, then guess what? You've got yourself a bimodal distribution, sweetie! That means you've got not one, but two modes to deal with - so just go ahead and flaunt those two modes proudly, darling.
a histogram has intervals a bar graph does notAnswerA histogram has all the bars smooshed together while a bar graph has them apart. A bit more detail:A histogram looks sort of like mountains, valleys, and peaks. It goes up and down, depending on how the numbers change, with no break in between. A bar graph has columns which are straight up and down, with breaks (spaces) in between the columns.
When you are graphing some quantity, in many cases you will wind up with a curve that is shaped like a bell, in other words, the graph gradually rises, then rises more sharply, then flattens out and declines in a symmetrical pattern, the same way that it rose. This reflects the fact that a great many things have a normal range, and so the quantity of that item peaks in the normal range and then declines as you get either more or less than the peak, and furthermore, the pattern does tend to form that distinctive bell shape. You could, for example, do a chart of the annual income of American families. Some have very high income, some have very low income, and most fall in the intermediate range. But you could do the same thing with endless numbers of other topics. If you were to capture pigeons and make accurate measurements of the lengths of their beaks, and then graph the results, you would get a bell curve. You could graph the weight of pet cats, or the cost of sweaters, or the age of houses, and so forth. Most things will produce some kind of bell curve.
The maximum peaks that investigators would expect from y-str would be three to eight peaks
It can have multiple peaks, but most symmetrical distributions do not.
In statistics, a distribution curve that has two peaks is referred to as bimodal.
Bimodal Distribution
what type of plants would you expect to find growing at the top of the Sierra Nevada's peaks
Yes. The distribution can be compact (centred tightly around the mean) or spread out. It can have a peak in the centre or two peaks at each end, or other variations.
Ice cubes may form peaks when freezing in the freezer due to uneven temperature distribution during the freezing process. This can cause certain parts of the ice cube to freeze faster than others, creating peaks. Additionally, water may expand as it freezes, pushing the ice up and forming peaks.
To interpret HPLC results effectively, analyze the chromatogram peaks, retention times, and peak shapes. Compare results to standards or previous data, consider sample preparation and column conditions, and look for any unexpected peaks or changes. Consult with experts or reference materials for further interpretation if needed.
The particle moves much slower at the extremes than at the middle and therefore it spends more time there. Peeking randomly we can certainly expect to see it at extremes because of that. More rigorously, the derivative of the sine is the cosine. This means that the slope of the sine is +1 or -1 at the axis crossing, 0 at the peak, and between +1 and -1 everywhere in between. If you were to accumulate a distribution curve as a function of time, pigeonholing the results based on some delta y, you would see more observations at the peaks than anywhere else because, as previously stated, the particle moves slower at the peaks.
A uniform distribution is not considered unimodal because it has a constant probability density across its range, meaning there are no peaks or modes. In a unimodal distribution, there is one clear peak where the values cluster, while in a uniform distribution, all values within the specified range are equally likely. Therefore, it lacks a single mode.
The Appalachian Mountains in North America are known for their rounded peaks and wide valleys that have been shaped by millions of years of erosion, giving them a more subdued appearance compared to younger, more rugged mountain ranges like the Rockies.
They are the same because they both have water on some parts of the land and they both are a part of land on this earth. I hope this gave you a couple answers you are welcome. ~Alanna Lynn Harwick 4/18/13