Question

name: dariana medeiros date: 6/13 block: a lesson 10: outliers (continued) do now given the following data: 164, 175, 126, 115, 120, 141, 85 a) what effect will the outlier have on the median of the data if the outlier is excluded? the median increases from 143 to 151. b) what effect will the outlier have on the mean of the data if the outlier is excluded? increase from 136.71 to 150.83 or the mean. 1. a scientist is recording the temperature in a controlled lab. the sensor reports the following readings every minute: 24.8, 24.9, 25.0, 25.0, 25.1, 100.0 should this outlier be kept or removed? why? the outlier should be removed as it is likely a measurement error. 2. a retail company tracks how much customers spend in a day. the values in dollars are: 53, 63, 85, 120, 190, 230, 10,000 does this set of data have an outlier? if so, what is it? should it be removed? why? yes, the outlier is 10000. whether to remove it depends on the analysis context.

Explanation:

Step1: Identify outliers in data - 1

The data set for temperature readings is [24.8, 24.9, 25.0, 25.0, 25.1, 100.0]. The value 100.0 is much larger than the other values, so it is an outlier. Since in a controlled - lab temperature measurement, such a large deviation is likely a measurement error, it should be removed.

Step2: Identify outliers in data - 2

The data set for customer spending is [55, 65, 85, 120, 190, 230, 10000]. The value 10000 is much larger than the other values, so it is an outlier. Whether to remove it depends on the context of the analysis. If we are interested in typical customer spending, it may skew the results and should be removed. But if we are analyzing high - end customers or extreme cases, it may be relevant and should be kept.

Answer:

1. The outlier 100.0 in the temperature data set should be removed as it is likely a measurement error.
2. The outlier in the customer - spending data set is 10000. Whether to remove it depends on the analysis context.