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Math
Oklahoma Math
Statistics & Probability (S): Data Analysis (DA)
Summarize and represent the distribution for univariate quantitative data by describing and analyzing the shape of the distribution, the measures of center for the distribution, the patterns in variability for the distribution, and any outliers, gaps, or other unusual features in the distribution.
- Calculating mean and median from data displays
- Choosing the "best" measure of center
- Estimating mean and median in data displays
- Example: Comparing distributions
- Identifying outliers
- Judging outliers in a dataset
- Mean and standard deviation versus median and IQR
- Median in a histogram
- Qualitative sense of normal distributions
- Sample standard deviation and bias
- Sample variance
Select and create an appropriate display (e.g., dot plots, histograms, box plots) for univariate data.
Use statistics appropriate to the shape of the data distribution to compare center and variability of two or more different data sets.
Describe and analyze the distribution of univariate categorical data.
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Use calculators, computers, or tables to estimate areas under the normal curve. Recognize that there are data sets for which such a procedure is not appropriate.
- Basic normal calculations
- Calculating z-scores
- Empirical rule
- Finding z-score for a percentile
- Normal distribution problems: Empirical rule
- Normal distribution: Area above or below a point
- Normal distribution: Area between two points
- Standard normal table for proportion above
- Standard normal table for proportion below
- Standard normal table for proportion between values
- Threshold for low percentile
- Z-score introduction
Construct appropriate parallel graphical displays of distributions.
Use numerical attributes of distributions to make comparisons between distributions.
Create two-way tables for bivariate categorical data and analyze for possible associations between the two categories using marginal, joint, and conditional frequencies.
Make predictions and draw conclusions from regression models (linear, exponential, quadratic) from two-variable quantitative data.
Analyze scatter plots for patterns, linearity, outliers, and influential points.
Using technology, compute and interpret the correlation coefficient.
Understand the implications of extrapolating data to make predictions.
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Construct and interpret confidence intervals for the mean of a normally distributed population and for a population proportion.
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Explain how a sample statistic and a confidence level are used in the construction of a confidence interval.
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Explain how changes in the sample size, confidence level, and standard error affect the margin of error of a confidence interval.
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Construct a confidence interval for the mean of a normally distributed population (with a known standard deviation) and for a population proportion. Use confidence intervals to evaluate claims.
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Use confidence intervals to evaluate claims for a single population parameter.
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