To identify outliers, we first calculate the interquartile range (IQR) of the dataset. The values in ascending order are 6.8, 7.8, 7.9, 8.0, 8.1, 8.3, and 8.4. The first quartile (Q1) is 7.9, and the third quartile (Q3) is 8.3, making the IQR = Q3 - Q1 = 8.3 - 7.9 = 0.4. Outliers are typically defined as values below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR; thus, any value below 7.5 or above 8.7 would be an outlier. In this dataset, the only potential outlier is 6.8, which falls below 7.5.
78 + 79 + 80 + 80 + 81 + 81 = 479
The variance of 73 72 67 74 78 84 79 71 76 76 79 81 75 80 78 76 78 = 16.8456
75, 76, 77, 78 and 79
Assuming you mean the arithmetic mean, it is 80.
75, 76, 77, 78 and 79
78 + 79 + 80 + 80 + 81 + 81 = 479
The variance of 73 72 67 74 78 84 79 71 76 76 79 81 75 80 78 76 78 = 16.8456
75, 76, 77, 78 and 79
Assuming you mean the arithmetic mean, it is 80.
75, 76, 77, 78 and 79
For 72 68 64 90 80 84 78 75 76 77: σ=7.4863
9.5 is the interquartile range of those numbers.
The numbers 71, 73 and 79 are prime.
54 55 56 57...78 79
has to be a 79 or 80 tailight are slightly different that a 78
There are infinitely many. Involving integers (whole numbers) there are: 0 + 80, 1 + 79, 2 + 78, .... 78 + 2, 79 + 1, 80 + 0, 81 + -1, 82 + -2, ..... When you include non-integers there are: ½ + 79½, 1½ + 78½, ... 1.1 + 78.9, 1.2 + 78.8, .... 3.1 + 76.9, 3.14 + 76.86, 3.141 + 76.859, 3.1415 + 76.8585, 3.14159 + 76.85841, ...., π + (80 - π)
Integers include 75, 76, 77, 78 and 79.