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Data Analysis and Probability
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PS1: DATA ORGANIZATION: FORMULATE
The learner will be able to
formulate studies to answer question to real-world situations.
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M.STAT.1.1 Sampling: Concepts
The learner will be able to distinguish between sample and
population, identify characteristics of representative samples to
minimize bias and error, and recognize the variability among repeated
samples taken from the same population.
| Bloom's |
Scope |
Hours |
Source |
| Application |
Master |
2.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #8 |
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M.STAT.1.2 Statistical Analysis: Estimate
The learner will be able to develop the concept of estimating
population parameters using confidence intervals produced from
comparisons of box plots, and apply the capture-recapture model to
generate a confidence interval for the populations.
| Bloom's |
Scope |
Hours |
Source |
| Application |
Master |
3.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #22 |
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M.STAT.1.3 Statistics: Models
The learner will be able to develop estimates (both point and
interval) for parameters (such as mean, standard deviation and
proportion of successes) and test hypotheses concerning these
parameters through the using appropriate statistical models.
| Bloom's |
Scope |
Hours |
Source |
| Synthesis |
Master |
2.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #24 |
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M.STAT.1.4 Sampling: Construct
The learner will be able to construct sampling distributions from
binomial populations construct student experiments, random number
tables and computer simulations.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
2.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #20 |
|
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M.STAT.1.5 Mathematical Modeling: Simulation
The learner will be able to use the eight step process to build a
model for simulating a given practical problem situation and use
manipulatives, random number generators, calculators, and/or computers
to perform the simulation to form an approximation to the problem
solution.
| Bloom's |
Scope |
Hours |
Source |
| Synthesis |
Master |
8.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #14 |
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M.STAT.1.6 Sampling: Concepts
The learner will be able to understand the concept of randomness as
applied to sample selection and identify other sampling methods
suitable to given situations.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #9 |
|
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M.STAT.1.7 Data Collection: Methods
The learner will be able to
design a survey or an opinion poll or choose other methods of data to solve problems.
| Bloom's |
Scope |
Hours |
Source |
| Synthesis |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #10 |
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PS2: DATA ORGANIZATION: CONDUCT
The learner will be able to
conduct investigations using statistical tools and display resulting data.
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M.STAT.2.1 Data: Organize/Summarize/Characterize
The learner will be able to organize, summarize, characterize, and
interpret data from practical situations using relevant data sets by
constructing of tables, graphs, and charts including frequency
distributions, histograms, line plots, stem-and-leaf plots, box plots,
and/or scatterplots for bivariate data.
| Bloom's |
Scope |
Hours |
Source |
| Synthesis |
Master |
3.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #1 |
|
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M.STAT.2.2 Sampling: Create
The learner will be able to construct and interpret 90% and 95% box
plots for various size samples, and use the box plots to summarize the
sampling distribution.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #21 |
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M.STAT.2.3 Central Limit Theorem: Use
The learner will be able to apply the Central Limit Theorem and
understand its impact on the distribution of the sample mean, including
the effects of sample size.
| Bloom's |
Scope |
Hours |
Source |
| Application |
Master |
2.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #23 |
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M.STAT.2.4 Data Collection: Experimental
The learner will be able to
collect and analyze data using experimental models and random number tables and generators.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
2.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #11 |
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M.STAT.2.5 Simulations: Perform
The learner will be able to perform simulations for problems where
the probability of success is one-half and other then one-half and
perform simulations for situations with an unknown number of key
components.
| Bloom's |
Scope |
Hours |
Source |
| Synthesis |
Master |
4.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #15 |
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PS3: DATA ANALYSIS: CENTRAL TENDENCY
The learner will be able to
analyze real-world data collected using appropriate measures of central tendency and dispersion.
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M.STAT.3.1 Variation: Analyze Source
The learner will be able to analyze sources of variation and
interpret and draw conclusions when solving applied problems. (Some may
include the difference between samples and populations, sampling
variability, the application of probability to make generalizations and
predictions about populations based on the analysis of samples, the
concept of random or chance variation, and analysis of variance).
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Develop |
4.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #28 |
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M.STAT.3.2 Data Analysis: Summarize
The learner will be able to apply the measures of central tendency
(mean, median, and mode), and measures of spread ( range, interquartile
range, and standard deviation).
| Bloom's |
Scope |
Hours |
Source |
| Comprehension |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #2 |
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M.STAT.3.3 Data Analysis: Recognize/Trends
The learner will be able to
identify trends in data represented graphically, including patterns, clusters, and outliers.
| Bloom's |
Scope |
Hours |
Source |
| Knowledge |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #3 |
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M.STAT.3.4 Statistical Analysis: Alternative
The learner will be able to apply distribution-free or
non-parametric methods as alternative to statistical analyses that make
assumptions about populations sampled. (Applications from practical
problems can be presented using such measures as the sign test, the
Mann-Whitney U test and Sperman's rank correlation test).
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Introduce |
5.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #25 |
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PS4: DATA ANALYSIS: EVALUATE
The learner will be able to
evaluate statistical studies and determine inferences that can be justified.
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M.STAT.4.1 Data: Analyze Bivariate
The learner will be able to analyze bivariate data represented
graphically and predict results by fitting a line to the data, using
methods such as mean fit and least squares and tools such as computers
and calculators.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
4.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #4 |
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M.STAT.4.2 Correlation: Compute/Investigate
The learner will be able to for a given bivariate scatter plot or
data set, characterizes the correlation, calculates the correlation
coefficient, and determine if a linear relationship exists.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
2.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #5 |
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M.STAT.4.3 Data: Linear Transformation
The learner will be able to
understand the effect of linear transformations have on the analysis and exploration of data.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #6 |
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M.STAT.4.4 Data Analysis: Interpret Out
The learner will be able to
interpret the outcome of data analysis and communicate these results.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #12 |
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M.STAT.4.5 Statistics: Proper/Improper
The learner will be able to
identify sound examples of statistics in decision making and correct the misuses of statistics.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #7 |
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M.STAT.4.6 Proof: Use/Mathematical Induction
The learner will be able to use mathematical induction the
derivation of certain formulas, the verification of appropriate
properties, proofs of equivalence, and deductive reasoning.
| Bloom's |
Scope |
Hours |
Source |
| Synthesis |
Introduce |
2.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #27 |
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PS5: PROBABILITY: COUNTING PRINCIPLES
The learner will be able to
apply counting principles in real world contexts.
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M.STAT.5.1 Experiments: Apply Results
The learner will be able to use student-generate data sets, games of
chance, manipulatives, and historic data to estimate probabilities with
the empirical approach. Apply the results obtained from active
experiments to illustrate the Law of Large Numbers and to develop the
concept of theoretical probability.
| Bloom's |
Scope |
Hours |
Source |
| Analysis |
Master |
4.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #13 |
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M.STAT.5.2 Counting Methods: Apply
The learner will be able to apply counting techniques and calculate
the probability of the union and the intersection of two events, the
probability of the complement, and conditional probability.
| Bloom's |
Scope |
Hours |
Source |
| Application |
Master |
2.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #16 |
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PS6: PROBABILITY
The learner will be able to
apply the laws of probability in real world contexts.
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M.STAT.6.1 Probability: Find/Odds/For/Associated
The learner will be able to
distinguish between odds for and probabilities and find the odds associated with given events.
| Bloom's |
Scope |
Hours |
Source |
| Application |
Master |
1.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #17 |
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M.STAT.6.2 Probability: Assign
The learner will be able to
assigns probabilities to the outcomes of a random variable and calculate expected value.
| Bloom's |
Scope |
Hours |
Source |
| Application |
Master |
1.5 |
GA: Quality Core Curriculum, December 2000, Statistics, #18 |
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M.STAT.6.3 Probability Distributions: Distinguish
The learner will be able to distinguishes between discrete and
continuous distributions and solves problems using probability
distributions, including binomial, normal, Poisson, and chi square.
| Bloom's |
Scope |
Hours |
Source |
| Application |
Master |
3.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #19 |
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M.STAT.6.4 Probability: Geometric
The learner will be able to use geometric probability to develop
problem solving skills through experiments whose outcomes can be
represented by points in a geometric region.
| Bloom's |
Scope |
Hours |
Source |
| Synthesis |
Master |
3.0 |
GA: Quality Core Curriculum, December 2000, Statistics, #26 |
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514 Glover Street | Marietta, Ga. 30060 | (770) 426-3300
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