Search the California Content Standards

Cluster:
Construct and compare linear, quadratic, and exponential models and solve problems.

Standard:
For exponential models, express as a logarithm the solution to ab^ct = d where a, c, and d are numbers and the base b is 2, 10, or e; evaluate the logarithm using technology. * [Logarithms as solutions for exponentials]

Cluster:
Construct and compare linear, quadratic, and exponential models and solve problems.

Standard:
Prove simple laws of logarithms. CA *

Cluster:
Construct and compare linear, quadratic, and exponential models and solve problems.

Standard:
Use the definition of logarithms to translate between logarithms in any base. CA *

Cluster:
Construct and compare linear, quadratic, and exponential models and solve problems.

Standard:
Understand and use the properties of logarithms to simplify logarithmic numeric expressions and to identify their approximate values. CA *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve. *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
Represent data with plots on the real number line (dot plots, histograms, and box plots). *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets. *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers). *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve. *

Cluster:
Summarize, represent, and interpret data on two categorical and quantitative variables.

Standard:
Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data. *