Chalkboard

Introduction
Ordering Info
Copyright

© 2006 by Elkhorn Public Schools and Scantron Corporation.
All Rights Reserved.

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Elkhorn Public Schools
Math
Mathematics - Statistics

Statistics
1a
The learner will be able to distinguish between a population and a sample.
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Sampling Techniques Master Elkhorn Public Schools(a) 12.5.1
  
1b
The learner will be able to distinguish between a parameter and a statistic.
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Sampling Techniques Master Elkhorn Public Schools(a)
  
1c
The learner will be able to distinguish between qualitative and quantitative data.
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Sampling Techniques Master Elkhorn Public Schools(a) 12.5.1
  
1d
The learner will be able to classify data with respect to the four levels of measurement-nominal, ordinal, interval and ratio.
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Sampling Techniques Master Elkhorn Public Schools(a)
  
1e
The learner will be able to collect data by taking a census, using sampling, a simulation, or performing an experiment.
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Sampling Techniques Master Elkhorn Public Schools(a) 12.5.1
  
1f
The learner will be able to create a sample using random sampling, statified sampling, cluster sampling, and systematic sampling.
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Sampling Techniques Master Elkhorn Public Schools(a) 12.5.6
  
1g
The learner will be able to identify a biased sample.
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Sampling Techniques Master Elkhorn Public Schools(a) 12.5.1
  
2a
The learner will be able to find the range and standard deviation.
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Central tendency Master Elkhorn Public Schools(a)-12.5.5
  
2b
The learner will be able to find the empirical rule.
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Central tendency Master Elkhorn Public Schools(a) 12.5.5
  
2c
The learner will be able to find and apply Chebychev's theorem to interpret standard deviation.
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Central tendency Master Elkhorn Public Schools(a) 12.5.4
  
2d
The learner will be able to find and apply the first , second , and third quartiles.
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Central tendency Master Elkhorn Public Schools(a) 12.5.5
  
2e
The learner will be able to find the interquartile range.
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Central tendency Master Elkhorn Public Schools(a) 12.5.4
  
2f
The learner will be able to represent the data graphically using a box and whisker plot.
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Central tendency Master Elkhorn Public Schools(a) 12.5.5
  
2g
The learner will be able to find and interpret the z-score.
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Central tendency Master Elkhorn Public Schools(a)
  
2h
The learner will be able to construct a frequency distribution including limits, boundaries, midpoints, relative frequencies, and cumulative frequencies.
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Central tendency Master Elkhorn Public Schools(a)
  
2i
The learner will be able to construct a frequency histogram.
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Central tendency Master Elkhorn Public Schools(a) 12.5.5
  
2j
The learner will be able to construct a relative frequency histogram and ogives chart.
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Central tendency Master Elkhorn Public Schools(a) 12.5.5
  
2k
The learner will be able to graph and interpret the data using stem and leaf plots , scatter plots, pie charts, and pareto charts.
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Central tendency Master Elkhorn Public Schools(a)
  
2l
The learner will be able to find the mean, median, and mode of a population sample.
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Central tendency Master Elkhorn Public Schools(a) 12.5.4
  
2m
The learner will be able to find a weighted mean of a set and the mean of a frequency distribution.
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Central tendency Master Elkhorn Public Schools(a)
  
3a
The learner will be able to identify the sample space of a probability experiment and to identify simple events.
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types of probability Master Elkhorn Public Schools(a) 12.5.3
  
3b
The learner will be able to distinguish among classical probability, empirical probability, and subjective probability.
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types of probability Master Elkhorn Public Schools(a) 12.5.3
  
3c
The learner will be able to distinguish between independent and dependent events.
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types of probability Master Elkhorn Public Schools(a) 12.5.6
  
3d
The learner will be able to find the multiplication rule to find the probability of two events.
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types of probability Master Elkhorn Public Schools(a) 12.5.3
  
3e
The learner will be able to determine if two events are mutually exclusive.
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types of probability Master Elkhorn Public Schools(a) 12.5.3
  
3f
The learner will be able to the addition rule to find the probability.
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types of probability Master Elkhorn Public Schools(a) 12.5.3
  
3g
The learner will be able to use the fundamental counting principle to find the number of ways two or more events can occur.
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types of probability Master Elkhorn Public Schools(a) 12.5.3
  
3h
The learner will be able to find the number of ways a group of objects can be arranged in order and the number of ways to choose several objectives from a group without regard to order.
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types of probability Master Elkhorn Public Schools(a) 12.5.3
  
3i
The learner will be able to use the counting principles to find probabilities.
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types of probability Master Elkhorn Public Schools(a) 12.5.6
  
4a
The learner will be able to distinguish between discrete random variables and continuous variables.
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Discrete Probability Distributions Master Elkhorn Public Schools(a)
  
4b
The learner will be able to determine if a distribution is a probability distribution.
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Discrete Probability Distributions Master Elkhorn Public Schools(a)
  
4c
The learner will be able to construct a discrete probability distribution and its graph, and the mean, variance and standard deviation of a discrete probability distribution.
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Discrete Probability Distributions Master Elkhorn Public Schools(a)
  
4d
The learner will be able to find the expected value of a discrete probability distribution.
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Discrete Probability Distributions Master Elkhorn Public Schools(a)
  
4e
The learner will be able to determine if a probability experiment is a binomial experiment.
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Discrete Probability Distributions Master Elkhorn Public Schools(a)
  
4f
The learner will be able to find binomial probabilities using the binomial probability formula , a binomial probability table and technology.
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Discrete Probability Distributions Master Elkhorn Public Schools(a)
  
4i
The learner will be able to find the probabilities using the poisson distribution.
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Discrete Probability Distributions Master Elkhorn Public Schools(a)
  
5a
The learner will be able to interpret graphs of normal probability distribution.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5b
The learner will be able to estimate areas under a normal curve and use them to estimate probabilities for random variables with normal distribution.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5c
The learner will be able to find and interpret z scores.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5d
The learner will be able to find probabilities for normally distributed variables.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5e
The learner will be able to find a z score given the area under the normal curve.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5f
The learner will be able to transform a z score to an x value.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5g
The learner will be able to find a specific data value of a normal distribution given the probability.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5g
The learner will be able to find a specific data value of a normal distribution given the probability.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5h
The learner will be able to find sampling distribution and verify their properties.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5i
The learner will be able to interpret the central limit theorem.
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Normal Probability Distribution Master Elkhorn Public Schools(a)
  
5j
The learner will be able to use scatter diagrams to decide whether there is a linear correlation between two variables.
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Normal Probability Distribution Master Elkhorn Public Schools(a) 12.5.2
  
5k
The learner will be able to the learner will calculate mathematical models and will use them to predict other values when given data.
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Normal Probability Distribution Master Elkhorn Public Schools(a) 12.5.2
  
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