## Is 5 number summary affected by outliers?

The five-number summary of the data set is: 5, 12, 23, 39, and 47. Data points that lie below the lower limit or above the upper limit are potential outliers.

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**How do you calculate outliers on a calculator?**

An outlier in a distribution is a number that is more than 1.5 times the length of the box away from either the lower or upper quartiles. Speciﬁcally, if a number is less than Q1 – 1.5×IQR or greater than Q3 + 1.5×IQR, then it is an outlier.

### How do you tell if there is an outlier in data?

A commonly used rule says that a data point is an outlier if it is more than 1.5 ⋅ IQR 1.5\cdot \text{IQR} 1. 5⋅IQR1, point, 5, dot, start text, I, Q, R, end text above the third quartile or below the first quartile.

**What is considered an outlier?**

Definition of outliers. An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal.

## How do you find outliers on a TI 84?

TI-84: Box Plots

- Turn on the Stat Plot. Press [2nd] [Stat Plot].
- Select a Box Plot icon. The first one will show outliers.
- Enter Data in L1 of [Stat]
- View Box Plot by going to [ZOOM] ‘Stat’ (#9).
- Press [Trace] and the arrow keys to view the values of the Min, Q1, Median, Q3, and Max.
- Go to the [2nd] [Stat].

**How do you explain outliers?**

An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal.

### Why is 1.5 used to calculate outliers?

Well, as you might have guessed, the number (here 1.5, hereinafter scale) clearly controls the sensitivity of the range and hence the decision rule. A bigger scale would make the outlier(s) to be considered as data point(s) while a smaller one would make some of the data point(s) to be perceived as outlier(s).

**How do you know if a data point is an outlier?**

A commonly used rule says that a data point is an outlier if it is more than 1.5 ⋅ IQR 1.5\cdot \text{IQR} 1. 5⋅IQR1, point, 5, dot, start text, I, Q, R, end text above the third quartile or below the first quartile. Said differently, low outliers are below Q 1 − 1.5 ⋅ IQR \text{Q}_1-1.5\cdot\text{IQR} Q1−1.