Unit Outlines

Students will learn how to combine and multiply complex numbers. Students will also use the operations of addition, subtraction, multiplication, and scalar multiplication to combine matrices. Students represent vectors with magnitude and direction and combine using addition and subtraction. Assessment date estimate - September 4th

Students will use functions seen in Math 1 - 3 (linear, exponential, quadratic, polynomial, square root, rational, radical, logarithmic) to build piecewise functions and to model real word problems with piecewise functions. Students will extend the knowledge of evaluating functions into composition of functions. Students will use regression to model bivariate data by analyzing a scatter plot, hypothesizing about the trend, determining the best model and finally justification of this model. Students will use residuals and residual plots as measures of goodness-of-fit to all functions. Assessment date estimate - October 2nd

Students will use rules of exponents to derive the properties of logs, then to apply these properties to solve equations, including real-world problems. Students will understand and interpret key features of log functions (intercepts, end behavior, domain, range and intervals of increasing / decreasing). Students will continue the use of regressions seen in the Functions Unit to model bivariate data for the best fit logarithmic function. Assessment date estimate - October 27th

Students will expand upon the concepts of right triangle trig ratios taught in Math 2 and 3 to develop the reciprocal identities and Pythagorean identities. Students will extend their thinking of right triangle trig to investigate and establish the Law of Sines and Cosines. Students will build upon knowledge of sine and cosine functions seen in Math 3 (amplitude, period, vertical shifts, midline, domain, range) to include phase shift. Students will continue the use of regressions seen in the Functions Unit to model bivariate data for the best fit sinusoidal function. Approximate test estimate - January 14th*

*Some topics from this unit will be included in the midterm exam, scheduled for December 15th

Students will construct statistical questions to guide explorations of data in context by adhering to best practices in research. Students will determine appropriate methods of data collection and use of technology to analyze data, as well as organize large datasets previously collected. Students will recognize that variability in data exists and to describe the data in a variety of methods, such as numerically, graphically and non-standard data visualizations from media sources or scientific papers. Assessment date estimate - February 12th

Students will construct a simulation to create a sampling distribution to make informal inferences on a population using proportional data. Students will recognize the normal distribution of data when the sample size is significantly large. Students will construct confidence intervals of population proportions. Students will use significance tests to evaluate the evidence provided by the data about some claim concerning the population parameter. Assessment date estimate - March 29th

Students will construct a discrete probability distribution using simulation models recognizing that all probabilities are less than or equal to one and that the sum of the probabilities is equal to one. Students will recognize that the expected value of a discrete probability distribution is the mean of what would happen over a long series of trials. Students will recognize that a continuous probability distribution is modeled best by the normal distribution curve and that the total area under the curve is equal to one. Assessment date estimate - May 7th