This course surveys descriptive and inferential statistics with emphasis on practical applications of statistical analysis. The principles of collecting, analyzing, and interpreting data are covered. It examines the role of statistical analysis, statistical terminology, the appropriate use of statistical techniques, and interpretation of statistical findings through applications and functions of statistical methods.
Introduction to Statistics, Graphing, and Measures of Center
Distinguish between a sample and a population.
Determine whether a sample is random or biased.
Analyze a set of data with respect to characteristics such as Center, Distribution, Outliers, and Time.
Create a frequency distribution.
Create a graphical display using technology.
Interpret data presented in a graph.
Calculate mean, median, and mode for ungrouped data.
Standard Deviation, Probability, and Normal Distribution
Calculate standard deviation and variance for ungrouped data.
Use the empirical rule to determine if a data value is usual or unusual.
Calculate probabilities using the classical and relative frequency approaches.
Calculate probabilities using the addition and multiplication rules.
Use combinations to calculate the total number of possible events.
Calculate a probability from a Z-score and vice versa.
Central Limit Theorem and Estimates
Identify biased and unbiased estimators.
Use the Central Limit Theorem to calculate probabilities and z-scores.
Calculate a probability from a t-score and vice versa.
Calculate and interpret confidence intervals for proportions and means.
Calculate the required sample size to estimate a population proportion and mean.
Perform the steps in hypothesis testing with one- and two-tailed z-tests.
Perform the steps in hypothesis testing with one- and two-tailed t-tests.
Correlation and Regression
Interpret the correlation of data from a graph.
Calculate the correlation coefficient.
Perform a hypothesis test on a correlation coefficient.
Calculate the equation of the regression line for a set of paired data.
Use the results of the regression line equation for predictions.
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