Statistics Recovery Wozgen

Front

What is the interquartile range (IQR)?

Back

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 of the middle 50% of the data.

Tags

CCSS.6.SP.B.5C

Front

What does Q3 represent in a data set?

Back

Q3, or the third quartile, represents the value below which 75% of the data falls. It is a measure of the upper middle of the data set.

Tags

CCSS.6.SP.B.5C

Front

What is an outlier in a data set?

Back

An outlier is a data point that differs significantly from other observations in a data set. It can skew the results of statistical analyses.

Front

How does removing an outlier affect the mean of a data set?

Back

Removing an outlier typically decreases the mean of a data set if the outlier is significantly higher than the other values.

Front

What are the components of the 5-Number Summary?

Back

The 5-Number Summary includes the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of a data set.

Tags

CCSS.6.SP.B.5B

Front

What is the median of a data set?

Back

The median is the middle value of a data set when it is ordered from least to greatest. If there is an even number of observations, the median is the average of the two middle numbers.

Tags

CCSS.6.SP.B.5C

Front

What does the term 'statistical dispersion' refer to?

Back

Statistical dispersion refers to the extent to which a distribution is stretched or squeezed. Common measures include range, variance, and standard deviation.

Tags

CCSS.6.SP.A.3

Front

What is the difference between Q1 and Q3?

Back

The difference between Q1 and Q3 is known as the interquartile range (IQR), which measures the spread of the middle 50% of the data.

Tags

CCSS.6.SP.B.5C

Front

What is the mode of a data set?

Back

The mode is the value that appears most frequently in a data set. A data set may have one mode, more than one mode, or no mode at all.

Tags

CCSS.6.SP.B.5C

Front

How do you identify an outlier using the IQR?

Back

An outlier can be identified using the IQR by calculating Q1 - 1.5*IQR and Q3 + 1.5*IQR. Any data point outside this range is considered an outlier.