Sheena Tefera
Prof. Chaires
Mat221
6-11-07
CHAPTER 2
A. Raw Data- Data that has not been manipulated or treated in any way beyond their
original collection.
B. Data Array- Lists the data in increasing or decreasing numerical order.
C. Frequency distribution- A table that divides the data values into classes and
shows the number of observed values that fall into each class.
D. Class- Each category of the frequency distribution.
E. Frequency- The number of data values falling within each class.
F. Class limits- The boundaries for each class. These determine which data values
are assigned to that class.
G. Class interval- The width of each class. This is the difference between the lower
limit of the class and the lower limit of the next higher class. When planning a
frequency distribution that will have equally wide classes, the approximate width of
each class is.
H. Mutually Exclusive- a given data value can fall into only one class.
I. Exhaustive: include all possible data values.
J. Open-end classes- classes with either no lower limit or no upper limit
K. Class mark: the midpoint of the interval; this can be calculated as the lower limit
plus half the width of the interval.
L. Relative Frequency Distribution- The proportion or percentage of the data
values that fall within each category.
M. Cumulative Frequency Distribution- List the number of observations that are
within or below each of the classes.
N. Stem-and-leaf display- A variant of the frequency distribution, uses a subset of
the original digits as class descriptors.
O. Dot plot- displays each data value as a dot and allows us to readily see the shape
of the distribution as well as the high and low values.
P. Histogram- describes a frequency distribution by using a series of adjacent
rectangles, each of which has a length proportionate to either the frequency or the
relative frequency of the class it represents.
Q. Frequency Polygon- consists of line segments connecting the points formed by
the intersections of the class marks with the class frequencies.
R. Bar Chart- represents frequencies according to the relative lengths of a set of
rectangles, but it differs in two respects from the histogram: (1) the histogram is used
in representing quantitative data, while the bar chart represents qualitative data; and
(2) adjacent rectangles in the histogram share a common side, while those in the bar
chart have a gap between them.
S. Multiple-bar chart-Each time period, company, subsidiary, or other unit is
represented by two or more bars.
T. Line Graph- capable of simultaneously showing values of two quantitative
variables; it consists of linear segments connecting points observed or measured for
each variable.
U. Pie Chart- a circular display divided into sections based on either the number of
observations within or the relative values of the segments.
Sheena Tefera
Prof. Chaires
Mat221
6-11-07
V. Pictogram- Uses symbols instead of a bar describing frequencies or other values
of interest.
W. Sketch- A drawing or pictorial representation of some symbol relevant to the
data.
X. Scatter diagram or Scatterplot- Used to examine whether a relationship exists
Tabulation- Counting how many people or items are in each category or combination
of categories.
Y. Simple tabulation- Count how many people or items are in each category
Z. Cross-tabulation- shows how many people or items are in combinations of
categories.
Chapter 4
A. Exploratory Research- Helps to become familiar with the problem situation,
identify important variables, and use these variables to form hypothesis that can be
tested in subsequent research.
B. Descriptive Research- The goal of describing something.
C. Causal research- To determine whether one variable has an effect on another.
D. Predictive research- attempts to forecast some situation or value that will occur
in the future.
E. Primary data- Refers to those generated by a researcher for the specific problem
or decision at hand.
F. Secondary data- Data gathered by someone else for some other purpose.
G. Mail survey- A mailed questionnaire typically accompanied by a cover letter and
a postage-paid return envelope.
H. Personal Interview- An interviewer personally secures the respondents
cooperation and carries out what could be described as a “purposeful conversation” in
which the respondent replies to the questions asked of her.
I. Telephone interview- Similar to the personal interview, but uses the telephone
instead of personal interaction.
J. Questionnaire- The data collection instrument.
K. Multiple Choice- Several alternatives in which to choose.
L. Dichotomous- Having only two alternatives available.
M. Open-ended- The respondent is free to formulate his or her own answer and
expand on the subject of the question.
N. Sampling error- Errors due to survey research.
O. Nonsystematic: Measurements exhibiting random error are just as likely to be too
high as they are to be too low.
P. Response or non-response errors- Both of the directional, or systematic type.
Q. Experiments- Purpose is to identify cause and effect relationships between
variables.
R. Internal Validity- Whether T really made the difference in the measurements
obtained.
S. External validity- Whether the results can be generalized to other people or
settings.
T. Observation- Relies on watching or listening, then counting or measuring.
Sheena Tefera
Prof. Chaires
Mat221
6-11-07
U. Population: The set of all possible elements that could theoretically be observed
or measured; this is sometimes referred to as the universe.
V. Probability sampling- Each person or element in the population has a known
chance of being included in the sample.
W. Nonprobability sampling- Primarily used in exploratory research studies where
there is no intention of making statistical inferences from the sample to the
population.
X. Simple random sample- Every person or element in the population has an equal
chance of being included in the sample.
Y. Systematic sample- Randomly select a starting point between 1 and k, then
sample every kth element from the population.
Z. Periodicity- A phenomenon where the order in which the population appears
happened to include a cyclical variation in which the length of the cycle is the same
as the value of k that we are using in selecting the sample.
A. Stratified sample- The population is divided into layers, or strata; then a simple
random sample of members from each stratum is selected.
B. Cluster Sample- Involves dividing the population into groups, then randomly
selecting some of the groups and taking either a sample or a census of the members of
the groups selected.