Download Algebra I Unit 3 - Livingston County School District

Livingston County Schools
Unit 3
Descriptive Statistics
Unit Overview
Students extend prior knowledge of descriptive statistics to determine how a model fits data. Students use graphical representation
and knowledge to make judgments of the appropriateness of context.
Length of unit: 6 Weeks
KY Core Academic Standard
Learning Target
K
S.ID.1 Represent data with
plots on the real number
line (dot plots, histograms,
and box plots).
I can represent data with plots
using a variety of display types.
X
S.ID.2 Use statistics
appropriate to the shape of
the data distribution
to compare center (median,
mean) and spread
(interquartile range,
standard deviation) of two
or more different data sets.
I can choose the appropriate
measure for center and spread
based on the shape of a data
distribution.
X
S.ID.3 Interpret differences
in shape, center, and
spread in the context
of the data sets, accounting
for possible effects of
extreme data points
(outliers).
I can define "the context of data
sets."
I can use appropriate statistics to
compare two or more data sets.
I can interpret differences in the
context of data sets.
I can describe the effects of outliers
on the data sets.
R
X
X
S
P
Critical
Vocabulary
dot plots
histograms
box plots
number line
mean
median
spread
interquartile
range
standard
deviation
data
distribution
center
context of
data sets
X
outliers
X
Texts/Resources/Activities
S.ID.5 Summarize
categorical data for two
categories in two-way
frequency tables. Interpret
relative frequencies in the
context of the
data (including joint,
marginal, and conditional
relative frequencies).
Recognize possible
associations and trends in
the data.
I can recognize the different types
of relative frequencies.
S.ID.6a Represent data on
two quantitative variables on
a scatter plot, and describe
how the variables are
related.
a. Fit a function to the data;
use functions fitted to data
to solve problems in the
context of the data. Use
given functions or choose a
function suggested by the
context. Emphasize linear
and exponential models.
b. Informally assess the fit of
a function by plotting and
analyzing residuals.
c. Fit a linear function for a
scatter plot that suggests a
linear association.
I can represent data on a
scatterplot.
I can calculate relative frequencies.
I can summarize two-way
frequency tables.
X
joint, marginal
and
conditional
relative
frequencies
X
X
I can interpret relative frequencies.
X
I can recognize associations and
trends in data.
X
I can fit a given function class to
data.
I can describe how two quantitative
variables are related given
scatterplot data.
I can determine which function
best models scatterplot data and
describe how two quantitative
variables are related.
I can use functions fitted to data to
solve problems.
X
frequency
table
two-way
frequency
table
scatterplot
function class
line of best fit
X
X
X
X
S.ID.6b Represent data on
two quantitative variables
on a scatter plot, and
describe how the variables
are related.
b. Informally assess the fit
of a function by plotting
and analyzing residuals.
(Statistics and Probability is
a Modeling Conceptual
Category.)
I can represent the residuals from a
function and the data set it models
numerically and graphically.
X
I can informally assess the fit of a
function by analyzing residuals
from the residual plot.
X
S.ID.6c Represent data on
two quantitative variables
on a scatter plot, and
describe how the variables
are related.
c. Fit a linear function for a
scatter plot that suggests a
linear association.
(Statistics and Probability is
a Modeling Conceptual
Category.)
I can fit a linear function for a
scatterplot that suggests a linear
association.
X
line of best fit
linear
association
S.ID.7 Interpret the slope
(rate of change) and the
intercept (constant
term) of a linear model in
the context of the data.
I can interpret the slope and the
intercept of a linear model.
X
slope
intercept
constant term
residuals
S.ID.8 Compute (using
technology) and interpret
the correlation coefficient
of a linear fit
I can compute (using technology)
the correlation coefficient of a
linear fit.
X
I can define the correlation
coefficient.
X
X
I can interpret the correlation
coefficient of a linear fit as a
measure of how well the data fit
the relationship.
S.ID.9 Distinguish between
correlation
and causation.
I can define different types of
correlation.
I can define causation.
I can distinguish between
correlation and causation.
Spiraled Standards:
correlation
coefficient
X
X
X
HOT Questions:
correlation
positive
correlation
negative
correlation
no correlation
causation