1. Unit 7: Statistics Key Terms
Lessons 1 & 2
Quantitative data-
Examples:
Mean-
Median-
Range-
data measured numerically
age, salary, height, weight
average of a set of data
middle value in a data set when data is arranged in order
difference between the highest and lowest values in the data set
Quartiles-
1st Quartile-
3rd Quartile-
5 Number Summary-
Boxplot-
dividing a data set into 4 equal sets
median of the lower half of data
median of the upper half of data
minimum, 1st Quartile, median, 3rd Quartile, Maximum
Graph of the 5 number summary

2. Lessons 3 & 4
Census-
Survey-
Population-
Sample-
Outlier-
Interquartile range (IQR)-
Standard deviation-
Sample Standard Deviation (s)-
Population Standard Deviation (σ) -
study that includes every member of the intended population
study that includes only a sample or portion of the intended population
target group of the study
portion of the population actually included in the study
value in the data set that falls far away from the rest of the data
difference between 3rd quartile and 1st quartile
average distance a data point is away from the mean
used when study involves using a sample
used when every member of the population is included in the study

3. Notes
The affect of outliers on a data set:
What do outliers do to the mean of a set of data?
They pull the mean in the direction of the outlier.
Therefore, if a data set has outliers, use the median!!
Also, use IQR instead of range when there are outliers.

4. Lesson 5
Categorical Data-
Two-Way Frequency Table-
Joint Relative Frequency-
Marginal Relative Frequency-
Conditional Relative Frequency-
Lessons
Bivariate Data-
Correlation-
Causal Relationship-
Residual-
data that falls into a specific category (hair color, favorite movie)
a frequency table showing totals for 2 data sets
entries in the body of the table
entries in the “total” rows and columns
same as joint relative frequency
data collected on two variables to determine a relationship
statistical measurement of the relationship between 2 variables
when change in 1 variable “causes” change in the other variable
the difference between the predicted y-value and the observed y-value