Yes.
An example: the data set {1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 5} has median = Q1 = Q3 = 2.
The median is Q2, if it is on the right side of the box, then then it is close to Q3 than it is to Q1. If the right line ( whisker) is longer than the left, it mean the biggest outlier is farther from Q3 than the smallest outlier is from Q1. All of this means the population from which the data was sampled was skewed to the right.
The quartile deviation(QD) is half the difference between the highest and lower quartile in a distribution.
It stands for the Inter-Quartile Range. Given a set of observations, put them in ascending order. The lower quartile (Q1) is the observation such that a quarter of the observations are smaller (and three quarters are at least as large). The upper quartile (Q3) is the observation such that a quarter are larger. [The middle one (Q2) is the median.] Then IQR = Q3 - Q1
The box represents your Q1, Q2 (median) and Q3, so it is your interquartile range. The Q1 is the first box line, the Q2 is the middle one and the Q3 is the closing line. Your interquartile range basically tells you where 50% of the people are.
For the numbers: 23 25 14 25 36 27 42 12 8 7 23 29 26 28 11 20 31 8 and 36 Q1=12 Q3=29 so IQR=29-12=17 If the second and third numbers are 2 and 5 and it is not 25 then it is 9.5 and28.5. Sadly this site still does not support commas in the questions so one cannot tell for sure. The two easy ways to find interquartile range or IQR are to either use a calculator like the TI 83, or by hand. By hand you find Q2 which is the median. That divides the data into two halves. Now Q1 is the median of the first half and Q3 is the median of the second half. Subtract Q1 from Q3 and you have IQR.
Arrange the data in increasing order and count the number of data points = N. Find the integer K = N/2 or (N+1)/2. The Kth number in the ordered set is the median. Now consider only the numbers from the smallest to the median and find the median of this subset. This is the lower quartile = Q1. Then consider only the numbers from the original median to the largest. Find the median of this subset. It is the upper quartile = Q3. Then IQR = Q3 - Q1
In order to find Q1, you must first find Q2. Q2 is the median, or middle, for the entire set of given data. If the data set is 1, 2, 2, 3, 3, 4, 4 ,4, 5, 5, 6, 7, 7, then Q2 would be 4. Therefore, the first half of the data set is 1, 2, 2, 3, 3, 4. Q1 is the median for the first half of data. Since there are an even number of entries for the first half, the two middle numbers are averaged. Thus, 2+3=5, and 5/2=2.5. Q1 equals 2.5.
To find the inner quartiles (Q1 and Q3), first arrange your data in ascending order. Q1 is the median of the lower half of the data, and Q3 is the median of the upper half. The inner quartiles divide the data into four equal parts. The outer quartiles also known as the minimum and maximum values, are the smallest and largest values in the data set.
find the median of the set of data. and then find the quartiles. Q1 would be the 25th and Q3 would be the 75th
The median of the lower half of a set of data is called the first quartile, often denoted as Q1. It represents the value below which 25% of the data lies and effectively divides the lowest 50% of the dataset into two equal parts. This measure is useful in understanding the distribution and spread of the lower portion of the data.
To find Q1 (the first quartile) of a data set, first, arrange the data in ascending order. Then, identify the position of Q1 using the formula ( Q1 = \frac{(n + 1)}{4} ), where ( n ) is the number of data points. If the position is a whole number, Q1 is the value at that position; if it's not, Q1 is the average of the values at the closest whole numbers surrounding that position.
To find the lower quartile (Q1) on a dot plot, first, arrange the data points in ascending order. Then, identify the median of the lower half of the data, which includes all values below the overall median. Q1 is the median of this lower half, representing the 25th percentile. If there is an even number of values in the lower half, average the two middle values to determine Q1.
The median is between Q1 and Q3 and is the same as Q2. These are the same as 25%, 50% and 75% so the median is in the middle of the box in a box and whisker plot.
You need to find the median first. That will divide the data into two halves. The median is also known as Q2 or second quartile. Now take the first half of you data and find the median of that half. This is known as Q1. Do the same with the second half and that is Q3. You box has Q1 on the left, Q3 on the right and Q2 in the middle. The whiskers will be the range of your data, that is to say the upper and lower extremes. You will graph the quartiles and the extremes with the scale underneath them. A link with pictures is provided. The 5 numbers, Q1, Q2, Q3 and the extremes(max and min values) are known as the 5 number summary.
In a standard distribution, the first quartile (Q1) represents the 25th percentile of the data. This means that 25% of the data falls below Q1, and consequently, 75% of the data falls above Q1. Therefore, 75% of the data is above Q1.
Outliers are observations that are unusually large or unusually small. There is no universally agreed definition but values smaller than Q1 - 1.5*IQR or larger than Q3 + 1.5IQR are normally considered outliers. Q1 and Q3 are the lower and upper quartiles and Q3-Q1 is the inter quartile range, IQR. Outliers distort the mean but cannot affect the median. If it distorts the median, then most of the data are rubbish and the data set should be examined thoroughly. Outliers will distort measures of dispersion, and higher moments, such as the variance, standard deviation, skewness, kurtosis etc but again, will not affect the IQR except in very extreme conditions.
It is no ossible to answer the question because all the digits have been run together to form a single large number.