Using and Interpreting Concepts Finding Quartiles, Interquartile Range, and Outliers In Exercises 11 and 12, (c) identify any outliers.
56 63 51 60 57 60 60 54 63 59 80 63 60 62 65
Verified step by step guidance
1
Step 1: Arrange the data in ascending order. The given data is: 51, 54, 56, 57, 59, 60, 60, 60, 60, 62, 63, 63, 63, 65, 80.
Step 2: Find the first quartile (Q1) and third quartile (Q3). Q1 is the median of the lower half of the data (excluding the overall median), and Q3 is the median of the upper half of the data (excluding the overall median).
Step 3: Calculate the interquartile range (IQR) using the formula: IQR = Q3 - Q1.
Step 4: Determine the lower and upper bounds for outliers using the formulas: Lower Bound = Q1 - 1.5 * IQR and Upper Bound = Q3 + 1.5 * IQR.
Step 5: Identify any outliers by checking which data points fall below the lower bound or above the upper bound.
Verified video answer for a similar problem:
This video solution was recommended by our tutors as helpful for the problem above
Video duration:
5m
Play a video:
Was this helpful?
Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Quartiles
Quartiles are values that divide a data set into four equal parts, each containing 25% of the data. The first quartile (Q1) is the median of the lower half of the data, the second quartile (Q2) is the overall median, and the third quartile (Q3) is the median of the upper half. These values help in understanding the distribution and spread of the data.
The interquartile range (IQR) is a measure of statistical dispersion, calculated as the difference between the third quartile (Q3) and the first quartile (Q1). It represents the range within which the central 50% of the data lies, providing insight into the variability of the data while being resistant to outliers.
Find 5-Number Summary - TI-84 Calculator Example 1
Outliers
Outliers are data points that significantly differ from the other observations in a dataset. They can be identified using the IQR method, where any value below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR is considered an outlier. Recognizing outliers is crucial as they can skew the results and affect statistical analyses.