Gizmos for West Virginia - Mathematics: Statistics (College- and Career-Readiness Standards adopted 2023)

This correlation lists the recommended Gizmos for this state's curriculum standards. Click any Gizmo title below for more information.

DS: : Descriptive Statistics

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

DS.M.PS.1: : Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each.

DS.M.PS.2: : Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling.

DS.M.PS.3: : Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.

DS.M.PS.4: : Evaluate reports based on data. Write a function that describes a relationship between two quantities.

DS.M.PS.5: : Represent data with plots on the real number line (dots plots, histograms, and box plots).

DS.M.PS.6: : Use statistics appropriate to the shape of the data distributions to compare center and spread of two or more different data sets. Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).

P: : Probability

2.1: : Understand independence and conditional probability and use them to interpret data.

P.M.PS.10: : Understand that two events A and B are independent if the probability of A and B occurring together is the product of their probabilities and use this characterization to determine if they are independent.

P.M.PS.11: : Recognize the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B.

P.M.PS.13: : Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations. For example, compare the chance of having lung cancer if you are a smoker with the chance of being a smoker if you have lung cancer.

P.M.PS.14: : Find the conditional probability of A given B as the fraction of B's outcomes that also belong to A and interpret the answer in terms of the model.

P.M.PS.16: : Apply the general Multiplication Rule in a uniform probability model, P(A and B) = P(A)P(B|A) = P(B)P(A|B) and interpret the answer in terms of the model.

P.M.PS.17: : Use permutations and combinations to compute probabilities of compound events and solve problems.

PD: : Probability Distributions

3.1: : Calculate expected values and use them to solve problems.

PD.M.PS.18: : Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions.

PD.M.PS.19: : Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.

PD.M.PS.20: : Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated to find the expected value.

PD.M.PS.21: : Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically to find the expected value.

PD.M.PS.22: : Weight the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values (e.g., find the expected payoff for a game of chance).

PD.M.PS.23: : Evaluate and compare strategies on the basis of expected values.

PD.M.PS.24: : Analyze decisions and strategies using probability concepts.

CR: : Correlation and Regression

4.1: : Interpret linear models.

CR.M.PS.25: : Represent data on two quantitative variables on a scatter plot and describe how the variables are related.

CR.M.PS.25.a: : Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear and exponential models.

CR.M.PS.25.b: : Informally assess the fit of a function by plotting and analyzing residuals. Focus should be on situations for which linear models are appropriate.

CR.M.PS.25.c: : Fit a linear function for scatter plots that suggest a linear association.

CR.M.PS.26: : Interpret the rate of change and the constant term of a linear model in the context of the data. Use technology to compute and interpret the correlation coefficient of a linear fit.

CR.M.PS.27: : Distinguish between correlation and causation.

CI: : Confidence Intervals

5.1: : Determine and interpret confidence intervals.

CI.M.PS.28: : Find the point estimate and margin of error in a given scenario.

CI.M.PS.29: : Construct and interpret confidence intervals for the population mean.

SI: : Statistical Inference

7.1: : Determine and use correlation.

SI.M.PS.43: : Find a correlation coefficient.

SI.M.PS.46: : Distinguish between correlation and causation.

7.2: : Use linear regression to predict and interpret.

SI.M.PS.47: : Find the equation of a regression line; predict y-values using a regression line.

Correlation last revised: 10/13/2023

West Virginia - Mathematics: Statistics

College- and Career-Readiness Standards | Adopted: 2023