School City of Hobart Mathematics The ISTEP+ assessment measures the academic performance of students in Mathematics. IN: Probability and Statistics The Indiana Academic Standards for Mathematics include standards for students in Probability and Statistics. |
Data Interpretation |
The Data Interpretation Unit includes Competencies/Objectives which focus on the study and use of graphical forms. Students collect and classify data, organize and display data, use logical reasoning, and problem solving. |
Data Collection
The learner will be able to collect data.
|
Probability/Statistics |
The Probability/Statistics Unit includes Competencies/Objectives which focus on data analysis and probability concepts. Students collect, analyze, and make sense of real world data (including overlapping data, inconclusive data, etc.). |
Central Tendency: Calculate/Apply
The learner will be able to calculate and apply the mean, median, mode, weighted mean, geometric mean, harmonic mean, range, quartiles, variance, and standard deviation.
|
Mutually Exclusive: Find Probability
The learner will be able to comprehend and apply the addition rule to compute probabilities for mutually exclusive and non-mutually exclusive events.
|
Distribution: Calculate/Interpret
The learner will be able to calculate and interpret the mean and variance of a probability distribution.
|
Problem Solving: Probability
The learner will be able to apply counting methods, permutations, and combinations to obtain solutions to probability problems.
|
Probability Concepts: Comprehend
The learner will be able to comprehend hypothesis tests of means and differences between means and apply them in forming conclusions.
|
Data Display: Make/Compare/Evaluate
The learner will be able to make, compare, and evaluate various graphical representations of the same data using histograms, frequency polygons, cumulative distribution functions, pie charts, scatterplots, stem-and-leaf pots, and box-and-whisker plots, creating these by hand or using a computer spreadsheet.
|
Probability: Comprehend/Conditional
The learner will be able to comprehend the ideas of conditional probabilities.
|
Data: Fit/Straight Lines
The learner will be able to fit a straight line to given data.
|
Measures of Central Tendency: Apply
The learner will be able to apply the measures of central tendency.
|
Dependent/Independent: Multiplication
The learner will be able to comprehend how to calculate the probability of dependent and independent events using the multiplication principle.
|
Bayes' Theorem: Understand
The learner will be able to understand Bayes' Theorem.
|
Dependent/Independent: Multiplication
The learner will be able to calculate the probability of dependent and independent events using the multiplication principle.
|
Complementary Events: Probability
The learner will be able to calculate the probability of complementary events.
|
Bayes' Theorem: Problem Solving
The learner will be able to apply Bayes' theorem to problems solving situations.
|
Counting Methods: Understand Procedures
The learner will be able to comprehend counting procedures.
|
Permutations: Understand/Counting Method
The learner will be able to develop an understanding of permutations as a method of counting.
|
Combinations: Understand
The learner will be able to understand combinations.
|
Probability Distributions: Solving
The learner will be able to obtain problem solutions through the use of probability distributions.
|
Probability Distribution: Applying
The learner will be able to use a variety of probability distributions, such as binomial, geometric, multinomial etc.
|
Distribution: Apply/Normal
The learner will be able to apply the normal distribution.
|
Distribution: Curve of Best Fit/Squares
The learner will be able to determine the curve of best fit for collected data by applying the principle of least squares.
|
Central Limit Theorem
The learner will be able to apply the central limit theorem in solving problems.
|
Central Limit Theorem: Understand
The learner will be able to illustrate an understanding of the Central Limit Theorem.
|
Confidence Intervals: Calculate
The learner will be able to calculate confidence intervals to formulate estimates.
|
Confidence Intervals: Apply
The learner will be able to apply confidence intervals to formulate estimates.
|
Intervals: Apply/Confidence
The learner will be able to apply confidence intervals and hypothesis tests.
|
Variate: Continuous/Problem Solving
The learner will be able to solve problems using continuous random variables.
|
Variate: Discrete/Apply
The learner will be able to apply discrete random variables in probability situations.
|
Variability: Apply
The learner will be able to apply measures of variability.
|
Correlation: Coefficient/Compute
The learner will be able to compute the correlation coefficient for collected data.
|
Correlation: Coefficient/Interpret
The learner will be able to interpret the correlation coefficient for collected data.
|
Problem Solving: Probability
The learner will be able to apply probability to obtain solutions to problems.
|
Problem Solving: Conditional Probability
The learner will be able to use conditional probability to problem solve.
|
Data Display
The learner will be able to display data.
|