Exam 1 Flashcards
Statistics
Is the science of conducting studies to collect, organize, analyze, and draw conclusions from data.
Data
Values (measurements/observations) that the variables can assume.
Data value/datum
What each value in the data set is called.
Random variable
Variables with their values determined by chance.
Population
Consists of all subjects (human or otherwise) that are being studied.
Sample
A group of subjects selected from a population.
Descriptive Statistics
Consists of the collection, organization, summarization, and presentation of data.
(Describes, data can be shown in graphs, tables, etc.)
Inferential Statistics
Consists of generalizing from samples to populations, performing estimations & hypothesis tests, determining relationships among variables, and making predictions.
(Statistician tries to make inferences)
Qualitative Variables
Variables that have distinct categories according to some characteristic or attribute (Ex. hair color, drink brand, Jersey #, gender, religion, geographic location)
Quantitative Variables
Variables that can be counted or measured (Ex. age, height, weight, body temp, # of frogs)
Discrete Variables
Assume variables that can be counted and assigned values like 0,1,2,3, etc. (Ex. # of frogs in a contest, # of children in a family, calls received in a month)
Continuous Variables
Can assume an infinite # of values between any 2 specific values. They are obtained by measuring and often contain fractions and decimals. (Ex. distance a frog jumps, temp of a frog)
Nominal Level of Measurement
Classifies data into mutually exclusive (nonoverlapping) categories in which no order or ranking can be placed on the data (Ex. classifying people by zip codes, political party, religion, or marital status)
Ordinal Level of Measurement
Classifies data into categories that can be ranked; however, precise differences between the ranks doesn’t exist (Ex. T-shirt size, placings, letter grades)
Interval Level of Measurement
Ranks data and precise differences between units of measure do exist; however, there is no meaningful zero (Ex. IQ score, temperature)
Ratio Level of Measurement
Possess all the characteristics of interval measurement and there is a true zero. Also, true ratio exists when the same variable is measured on two different members of the population (Ex. scales used to measure weight, height, and area) (Ration Ex. one person can lift 200lbs. one person can lift 100lbs. this would be a 2:1 ratio between them)
Random sample
Sample where all members of the population have an equal chance of being selected.
Systematic sample
Sample is obtained by selecting every kth member of the population (Ex. picking every 5th person in line)
Stratified Sample
Sample obtained by dividing the population into subgroups/strata according to some characteristic relevant to the study (there can be several subgroups) subjects are then selected at random from each subgroup.
Cluster Sample
Obtained by dividing the population into sections/Clusters and then selecting one or more clusters at random and using all the members of the cluster(s) as the sample. (Used when the population is too large or involves multiple locations)
Convenience Sample
Researcher uses subjects that are convenient (Ex. interviewing people who walk into the mall)
Observational Study
Researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations.
Experimental Study
Researcher manipulates one of the variables and tries to determine how the manipulation influences other variables
Independent Variable
The variable that is being manipulated by the researcher; independent variable is AKA the explanatory variable. This is the x-axis.
Dependent Variable
Resultant variable or the outcome variable. This is the y-axis
Treatment Group
Group that received a specific instruction
Control Group
Group that doesn’t receive specific instruction
Categorial Frequency Distribution
Used for data that can be placed in specific categories (nominal or ordinal-level data) (Ex. political affiliation, religious affiliation, field of study)
Grouped Frequency Distribution
Range of data must be grouped into classes that are more than 1 unit in width.
Ungrouped Frequency Distribution
Using single data values for each class
Bell-shaped Distribution
Has a single peak and tapers off at either end. (Approx. symmetric.)
Uniform
Flat or rectangular
J-shaped
Has a few data values on the left side and increases as one moves right
Reverse J-shaped
Opposite of the j-shaped distribution
Positively or right-skewed
The peak of the distribution is to the left and the data values taper off to the right.
Negatively or left-skewed
Data values are clustered to the right and taper off to the left
Unimodal
Distributions with 1 peak
Bimodal
Distribution has 2 peaks of the same height
U-Shaped
Distribution is shaped like a “U”
Histogram
A graph that displays the data by using contiguous vertical bars (unless the frequency of a class is 0) of various heights to represent the frequencies of the class
Frequency Polygon
Graph that displays data by using lines that connect points plotted for the frequencies at the midpoints of the class. The frequencies are represented by the heights of the points
Ogive
A graph that represents the cumulative frequencies for the classes in a frequency distribution
