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An introductory level course in statistics. Includes descriptive statistics; measures of central tendency, variation and skewness; probability laws and distributions; inferences from one and two samples; correlation and regression; estimation of sample size; hypothesis tests from large and small samples; estimation of parameters from various sampling designs. These methods are applied to examples chosen from the Renewable Resources field.
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Upon successful completion of this course, the student will be able to:
Demonstrate the application of statistical methods to problems of estimation and hypothesis testing in the Renewable Resources field.
Present an organized summary of the basic statistical properties of a data set, and draw appropriate conclusions from it regarding a related population.
Explain the meaning and importance of the underlying assumptions behind the statistical procedures commonly used in the Renewable Resources field.
To accomplish the above, the student will study and apply:
Various statistical charts including the stem and leaf plot, histogram, pareto chart, pie chart, box and whisker plot, and scatterplot.
Frequency distributions and grouped data; understand class limits, boundaries and marks.
Statistical measures including those of:
Central tendency: mean, median, mode, mid-range, weighted mean;
Position: quartiles, percentiles;
Variation: range, inter-quartile range, standard deviation, coefficient of variation;
Probability (independence, conditional probability, addition and multiplication rules, sample spaces).
Discrete probability distributions such as the binomial and Poisson distributions.
Sampling, randomness, the difference between a sample and a population, use random numbers to choose a sample.
Continuous probability distributions, such as the normal, t, and Chi-Squared distributions and use the applicable statistical tables, the normal approximation to the binomial distribution, central limit theorem.
Sampling methods, including:
Computation of sampling error and confidence limits, use of the finite population correction,
Estimation of sample size with and without the finite population correction.
Hypothesis testing for one- and two-sided alternatives. The cases studied are:
One and two sample tests for the mean and proportion,
Chi-Squared tests for independence and goodness-of-fit, contingency tables.
Linear regression and correlation techniques, compute and interpret linear regression and correlation coefficients from a set of data.
Statistical functions on an electronic calculator including means, standard deviations, sums and sums of squares.
Use spreadsheet and statistical software to do statistical data analysis.
Effective as of Fall 2014
MATH 2453 is offered as a part of the following programs:
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