# High School Probability and Statistics Honors (Florida Specific)

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Suggested Prerequisites

Algebra 2

Description

Begin your path to mastering data! You will learn how to display, quantify, and describe data for various characteristics of a population. You will also discover how to sample, design experiments, and gather data effectively, ensuring high-quality data collection for further analysis. By anticipating patterns using probability and simulation, and applying statistical inference, you will analyze data and draw meaningful conclusions. Your path will result in you being an educated consumer of statistics, capable of planning and conducting studies, and making informed decisions based on your findings.

Module One: Exploring Data

-Distinguish between categorical and numerical data

-Categorical data displays

-Numerical data displays

-Measures of central tendency and variation

-Describe distribution of numerical data

-Data change effect on measures of center

-Five-number summary

-Compare distributions among two or more populations

Module Two: Bivariate Data

-Display bivariate categorical data

-Two-way frequency and relative frequency

-Joint and marginal frequencies

-Interpret conditional relative frequencies

-Create tree diagrams

-Two-way frequency tables and segmented bar graphs

-Association between categorical variables

-Calculate and interpret false positives and negatives

Module Three: Exploring Relationships

-Display numerical data

-Describe and compare explanatory and response variables

-Model data using linear and nonlinear functions

-Calculate and interpret residuals

-Interpret outliers and influential points

-Determine and describe a possible association

-Calculate and interpret correlation coefficient r

-Calculate slope and intercept of a model

-Interpret slope and intercept of a model

-Solving and using regression models

Module Four: Collecting Data

-Representative samples from population of interest

-Population parameter versus sample statistic

-Deciding which method to generate data

-Determining correlation versus causation

-Recognizing and minimizing bias and sampling errors

-Design controlled experiments

-Simulate random phenomena to measure estimate variations

-Analyze data distributions

-Determine if difference in parameters is significant

-Construct and interpret confidence intervals

Module Five: Probability

-Outcomes for chance occurrences using probability rules

-Decide which probability formula/rule to apply

-Calculate conditional probabilities

-Determine if events are independent

-Theoretical versus empirical probability

-Determine if events are mutually exclusive

Module Six: Probability Distributions

-Discrete versus continuous probability distributions

-Conditions for binomial and geometric random variables

-Probabilities for binomial and geometric random variables

-Mean and standard deviation for binomial random variables

-Mean or expected value for geometric random variables

-Interpret percentiles in context

-Z-scores to compare data points and distributions

-Normal density curve and Empirical rule

-Standardize data and calculate probability

-Explain Central Limit Theorem