Statistics
Course description:
Statistics is an advanced mathematics course that uses meaningful
problems and appropriate technologies to use statistical concepts developed
in previous courses to develop more advanced means of statistical analyses,
interpretations, and predictions.
Standard
1.0 Experimental Design
Students
will design and conduct statistical experiments.
Learning
Expectations:
The student will:
- 1.1 design studies
that can be addressed with data;
- 1.2 collect
data based on an appropriate sample.
Student Performance Indicators:
- formulate
questions that can be addressed with data;
- describe
the role of randomization in surveys and experiments;
- select
and use a method such as a survey or an experiment to collect data;
- demonstrate
understanding of bias in sampling;
- demonstrate
an understanding of the Law of Large Numbers;
- demonstrate
an understanding of the probability of independent events and conditional
probability;
- using
appropriate probability models, design a method for simulating data from
a particular situation, and use the generated data to analyze the situation;
- design
and conduct a statistical experiment to study a problem, and interpret
and communicate the outcomes;
- test
hypotheses using appropriate statistics.
Standard 2.0: Data Analysis
The student will select and use appropriate statistical methods
to analyze data and to develop and evaluate inferences and predictions based
on the data.
Learning Expectations:
The student will:
- 2.1 select and
use appropriate displays to represent and summarize the data collected in
statistical studies or experiments;
- 2.2 select and
use appropriate statistical methods to analyze data;
- 2.3 develop
and evaluate inferences and predictions based on data.
Student Performance Indicators:
- construct
and interpret charts, tables, and graphs that display univariate and bivariate
data;
- calculate
and apply measures of central tendency and dispersion in order to make
inferences about a data set;
- analyze
the effects of data transformations on measures of central tendency and
variability;
- calculate
and apply the correlation between data sets.
- apply
the properties of a normal distribution or a Chi-square distribution in
appropriate situations in order to make inferences about a data set;
- demonstrate
an understanding of the Central Limit Theorem;
- use
curve-fitting with appropriate technology to make regression equations
in order to represent a data set algebraically and to make inferences;
- demonstrate
an understanding of confidence intervals.
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