mth231 | undergraduate
Statistics For The Life Sciences
This undergraduate-level course is 5 To enroll, speak with an Enrollment Representative.
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Introduction to Statistics, Graphing, and Measures of Center
- Classify variables as categorical or qualitative.
- Distinguish between a sample and a population.
- Identify potential sources of bias in a sample.
- Analyze a set of data with respect to characteristics such as Center, Distribution, Outliers, and Time.
- Create a frequency distribution and histogram.
- Create a graphical display of a sample using technology.
- Use a graphical display of a sample to interpret characteristics such as Center, Distribution, Outliers, and Time.
- 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 empirical vs. theoretical probability.
- Calculate probabilities from statements involving AND and OR.
- Use combinations to calculate the total number of possible events and calculate probability.
- 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 five steps in hypothesis testing with one- and two-tailed "Z" tests.
- Perform the five 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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