Statistics 211

are the people or objects included in the study.

is a characteristic of the individual to be measured or observed

has a value or numerical measurement for which operations such as addition or averaging make sense.

describes an individual by placing the individual into a category or group, such as male or female.

the data from every individual of interest

the data from only some of the individuals of interst

a numerical measure that describes an aspect of a population

a numerical measure that describes an aspect of a sample

is the study of how to collect, organize, analyze, and interrupt numerical information from data.

applies to data that consist of names, labels, or categories. There is no implied criteria.

applies data that can be arranged in order. However, differences between data values either cannot be determined or are meaning less

applies to data that can be arranged in order. Data at ratio can have a true zero.

applies to data that can be arranged in order. In addition, differences between data values are meaningful.

involves methods or organizing, picturing, and summarizing information from samples or populations.

involves methods of using information from a sample to draw conclusions regarding the population.

ratio

interval

nominal

ordinal

ratio

ratio

of n measurements from a population is a subset of the population selected in such a manner that every sample size n from the population has an equal chance of being selected. Use simple random sample from the entire population.

Divide the entire population into distinct subgroups called strata. Groups are based on characteristics like age, education level, and income.

Number all members of the population sequentially. Then, from a starting point selected at random, include every kth member of the population in sample.

create a sample by using data from a population that is readily available

divide the entire population into pre-existing segments or clusters. The clusters are often geographic. Include every member of each selected cluster cluster in the sample.

a difference between measurements from a sample and corresponding measurements from the respective population.

is the result of poor sample design.

Stratified Sample example

Cluster Sample

Convenience Sample

systematic sample

Simple random simple

partitions data into classes or intervals and shows how many data values are in each class. The classes or intervals are constructed so that each data value falls into exactly one class

the lowest data value that can fit in a class.

the highest data value that can fit in a class

the difference between the lower class limit and other lower class limit of the next class

largest data value- smallest data value/ desired # of classes

lower class limit + upper class limit/ 2

add .5 to the upper class limits

subtract .5 from the lower class limits

class frequency/ total # of frequencies

in a data set are the data values that are very different from the other measures in the data set.

is a bar graph which the bar height represents frequency of an event. left to right in order from decreasing height

wedges in a circle visually display proportional parts of the total population that share a common characteristic.

data plotted in order of occurrence at regular intervals over a period of time

method of exploratory data analysis that is used to rank-order and arrange data into groups.

both sides are the same, graph is folded vertically down the middle.

these terms refer to a histogram in which every class has equal frequency.

direction of skewness is to the left or right

histogram in which the two classes with the largest frequencies are separated by at least one class.

Class boundaries are values halfway between the upper class limit of one class and the lower class limit of the next. Class limits specify the span of data values that fall within a class. Class boundaries are possible data values. Class boundaries are not possible data values. Class limits are possible data values.

Class limits are possible data values, class boundaries are not possible data values, class limits specify the span of data values, class boundaries are values halfway between the upper class limit of one upper class limit and the lower class limit next.