Statistics For The Life Sciences – mth231 (3 credits)
This course will examine the concepts of statistics leading to the application of these concepts to the life sciences. Topics will include populations and samples, random sampling, probabilities, distributions, and confidence intervals.
Explain the use of the F distribution to compare variation in two samples.
Interpret the correlation coefficient between two samples.
Differentiate among the traditional method, the p-value method, and the confidence interval method of testing a claim about a proportion.
Apply inferential statistics to test a claim about population mean and population standard deviation.
Collecting, Organizing, and Summarizing Data
Explain how random sampling is important in collecting statistical data.
Differentiate among scatterplots, histograms, and frequency distributions.
Calculate the mean, median, mode, standard deviation, variance, and z scores for a data set.
Differentiate between populations and samples.
Probabilities and Discrete Probability Distributions
Calculate probability using the addition rule and the multiplication rule.
Distinguish between independent and dependent events.
Explain absolute risk reduction, relative risk, number needed to treat, odds ratios, and rates.
Determine whether a procedure results in a binomial distribution or a Poisson distribution.
Normal Probability Distributions
Describe normal distribution, standard normal distribution, and uniform distribution.
Calculate the probability for a range of values in a normal distribution and in a standard normal distribution.
Explain the central limit theorem.
Describe point estimates, confidence intervals, confidence levels, critical values, and margins of error.
Confidence Intervals, Inferences, and Hypothesis Testing
Interpret a confidence interval for an estimate of population mean and population variance.
Differentiate among the z distribution, the t distribution, and the chi-square distribution.
Describe hypotheses, hypothesis tests, null hypotheses, alternative hypotheses, test statistics, and significance levels.
Differentiate between Type I and Type II errors.
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