Prob/Stats - Unit 3 - Quantitative Data

Front

What is the median in a data set?

Back

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

Front

What is stratified sampling?

Back

Stratified sampling is a method of sampling that involves dividing a population into smaller groups, known as strata, based on shared characteristics, and then randomly selecting samples from each stratum.

Front

What is the five-number summary?

Back

The five-number summary consists of five values: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. It provides a quick overview of the distribution of a data set.

Front

What does IQR stand for and what does it measure?

Back

IQR stands for Interquartile Range. It measures the range between the first quartile (Q1) and the third quartile (Q3) and is used to identify the middle 50% of the data.

Front

What is an outlier?

Back

An outlier is a data point that is significantly different from other observations in a data set. It can be much higher or lower than the rest of the data.

Front

What is the mode in 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.

Front

What is the difference between quantitative and categorical data?

Back

Quantitative data is numerical and can be measured, while categorical data represents characteristics or qualities and can be divided into groups.

Front

How do you calculate the mean of a data set?

Back

To calculate the mean, sum all the values in the data set and then divide by the number of values.

Front

What is the purpose of a stemplot?

Back

A stemplot (or stem-and-leaf plot) is used to display quantitative data in a graphical format, allowing for easy visualization of the distribution while retaining the original data values.

Front

What is the significance of the lower and upper ends of data in identifying outliers?

Back

Outliers can be identified by examining the lower and upper ends of the data. If a data point is below Q1 - 1.5*IQR or above Q3 + 1.5*IQR, it is considered an outlier.