The list below of mathematics topics, IF LACED WITH GOOD APPLICATIONS, addresses all of the issues raised in Part II at least minimally. Although the topics are displayed here according to subject matter components of traditional mathematics curricula, one should NOT infer that this is the best organization with which to address quantitative literacy . In fact, this kind of layer-cake organization may inhibit the very essence of quantitative literacy : encouraging multiple perspectives; informally developing intuition; and searching for connections. It may actually deepen the pitfall of just preparing for the next course. Nevertheless, the list suggests a common ground from which to begin. How much time will need to be spent on topics that the students have studied before will depend on circumstances, but deadly reviews of more or less familiar material should be avoided. (* means "less essential")

ARITHMETIC

• estimation

• percentage change

• use of calculator: rounding and truncation errors; order of operations.

GEOMETRY

• measurement: units and conversion of systems; length; area; volume.

• "familiar'' shapes: rectangles, triangles, circles, cubes, cones, cylinders, spheres, the Pythagorean relationship.

• angles: slopes of lines; parallel and perpendicular lines; right angles; similarity.

• complex shapes: approximation by "familiar" shapes; solution region for a system of linear inequalities in the plane.

ALGEBRA

• linear equations: equations in one unknown; systems in two unknowns; methods of solution.

• proportionality

• graphs and tables: constructing; reading, interpreting; extrapolating from; the notions of direct and indirect variation.

• simple exponents: roots and powers; products and quotients with a common base.

• concept of function: constructing discrete and continuous functions; graphical representation of functions; zeros of functions.

STATISTICS

• experimental probability: counting; mutually exclusive and independent events.

• graphical displays of data: pie and bar charts; frequency polygons; visual impact of scale changes.

• central tendency and spread: comparison of data sets using mean, median, mode and standard deviation, quartile deviation, range; percentile rank.

• the idea of correlation: measuring and evaluating the relationship between two variables.

• common sources of error: sampling error; misinterpreting averages or probabilities; invalid comparison distributions; statistical significance; statistical "proof''.

• random sampling: the count-recount technique; polls; lotteries; fair representation.

• linear fit: comparison of fit of two lines to a data set.

• quality control: the binomial distribution.
• simulation

• confidence intervals*: interval estimates; the standard error of the mean.

OTHER

• exponential change

• rates: comparison of average rates of change.

• models

• algorithms: sequential thinking; construction; relationship to formulas.

• optimization: the notions of maxima and minima of functions with or without constraints; graphical and computational methods for finding them; simple analytic methods, such as completing the square for quadratic polynomials.

• linear programming*: systems of equations in two variables with a linear objective function.

• scheduling*

• networks*

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