Course Description
Extending their knowledge of linear, exponential, and quadratic functions to polynomial, rational, and radical functions, students in Algebra 2 model situations and solve equations, discovering how the rules they learned in arithmetic continue to apply as they work with polynomials. Students focus on the properties and factors of polynomials, learning to find the zeros of a polynomial and graph it as a function. Students use complex numbers to solve quadratic equations and exponential expressions, and learn how to rewrite rational expressions in different forms and solve simple rational and radical equations. The trigonometric concepts students learned previously are expanded as they focus on the unit circle and apply these concepts to models of periodic phenomena. Students then extend their knowledge of function families to model functions defined as square roots or cube roots, as well as piecewise-defined functions. A detailed look at exponential and logarithmic functions is applied to showing intercepts and end behavior. Students collect data through sample surveys, experiments, and simulations, and learn about the role of randomness in this process. Quantitative reasoning is emphasized as students compare the differences between sample surveys, experiments, and observations, and explain how randomization relates to each one
Course Breakdown
Rational functions
Exponential and logarithmic relationships
Piecewise, absolute value, and step functions
Inverse and composition functions
Conic sections Sequences and series
The unit circle
Trigonometric functions
Graphs of sine and cosine
Harmonic motion
Mutually exclusive events
Binomial Theorem
Course Goals
Compare and contrast exponential and logarithmic functions.
Reveal information and find solutions of polynomial functions.
Solve problems using conic sections. Apply the unit circle to a variety of problems.
Interpret and construct the graphs of trigonometric functions.
Distinguish between geometric, theoretical, and experimental probability.
Gain an understanding of normally distributed data.