1.
If the mean = 0 and the standard deviation = 1, this is a standard,
normal curve
2.
Use with z scores (standard x , where +z are scores are above the
scores), z =
z are scores below the mean.
mean and
3.
To compare two observations from different circu
A.
Analyzing two variable quantitative data when a linear relationship is
suggested
Linear correlation coefficient (r) measures the strength of the
1.
linear relationship 1 r 1
r = 0 indicates no relationship (the ellipse is a perfect circle)
r indicates
voluntary response sample participants choose
themselves, usually those with strong opinions
choose to respond
e.g. online surveys, callin opinion questions
convenience sample investigators choose to sample
those people who are easy to reach
e.g. marketin
A.
The geometric distribution conditions are the same as for
the binomial except there is not a fixed number of
observations because the task is to find out how many
times it takes before a success occurs. This is sometimes
called a waiting time distribut
A.
Summary statistics for one variable data (use calculator with 1-variable stats)
1.
Measures of central tendency (center)
mean ( x, )
median (middle) mode
(most)
2.
Measures of dispersion (spread)
range (max min)
quartile (25% = Q 75% = Q )
1,
3
interqu
1.
Residual (y y ) vertical distance from the actual data
point to the regression line. y y = observed y value
predicted y value where residuals sum to zero
Residual plot scatterplot of (observed x values,
predicted y values) or (x, plot residuals on y a
A.
Observational study observes individuals in a population or
sample, measures variables of interest, but does not in any
way assign treatments or influence responses
B.
Experiment deliberately imposes some treatment on
individuals (experimental units or
Random Variables (Chapter 7)
1.
Graphs, whether of continuous or discrete variables, must have area
under a curve = 1. Histograms
discrete smooth curves continuous. The graphs need not be
symmetric.
2.
To get the expected value or mean of a discrete rand
Exploring Data (Chapter 1)
Categorical Data nominal scale, names
e.g. male/female or eye color or breeds of dogs
Quantitative Data rational scale (can +, , , with numbers
describing data)
e.g. weights of hamsters or amounts of chemicals in
beverages
A.
Gr
A.
Cautions in analyzing data
1.
Correlation does not imply causation. Only a
welldesigned, controlled experiment may establish
causation
2.
Lurking variables (variables not identified or considered)
may explain a relationship (correlation) between the
ex