Topics in probability and statistics: descriptive measures, distributions, one-sample estimation and hypothesis testing, correlation, simple linear regression, and categorical data. (Offered each semester.)

MATH 107, 108, 109, or 111

Douglas A. Lind, William G. Marshal, Samuel A. Wathen, Statistical Techniques in Business and Economics, 15^th edition, McGraw-Hill Irwin, 2012 (ISBN: 9780073401805)

Yvonne E. Martin, Assistant Professor of Business Administration

Students in the Management and Business Department

Students who complete this course will be able to:

1. Understand why statistics is studied.
2. Distinguish between descriptive statistics, probability, and inferential statistics.
3. Distinguish between a qualitative variable and a quantitative variable.
4. Describe how a discrete variable is different from a continuous variable.
5. Distinguish among the nominal, ordinal, interval, and ratio levels of measurement.
6. Construct an array.
7. Organize data into a frequency table.
8. Create histograms, frequency polygons, and cumulative frequency polygons.
9. Calculate the arithmetic mean, weighted mean, median, mode, range, mean deviation, variance, and standard deviation.  In addition, explain the characteristics, uses, advantages, and disadvantages of each.
10. Understand Chebyshev’s theorem and the Empirical Rule as they relate to a set of observations.
11. Develop and interpret a dot plot, stem-and-leaf display, and box plot.
12. Describe the classical, empirical, and subjective approaches to probability.
13. Understand some rules for computing probabilities.
14. Distinguish between discrete and continuous probability distributions.
15. Calculate the mean, variance, and standard deviation of discrete probability distributions.
16. Describe the characteristics of and compute probabilities using the binomial probability, hypergeometric probability and Poisson probability distributions.
17. Understand the characteristics and use the normal probability distribution to determine probability that an observation is between two points or above (or below) a point on a normal probability distribution.
18. Use the normal probability distribution to approximate the binomial probability distribution.
19. Explain why a sample is often the only feasible way to learn something about a population.
20. Describe methods to select a sample.
21. Define and construct a sampling distribution of the sample mean.
22. Understand, explain, and use the central limit theorem to find probabilities of selecting possible sample means from a specified population.
23. Define a point estimate and level of confidence.
24. Construct confidence intervals for the population mean and population proportion.
25. Determine the required sample size.
26. Define, describe and use the six-step hypothesis-testing procedure for population means and population proportions.
27. Define Type I and Type II errors.
28. Understand and interpret the terms dependent and independent variables.
29. Calculate the least squares regression line.  Calculate and interpret he coefficient of correlation, the coefficient of determination, and the standard error of estimate.   Construct and interpret confidence and prediction intervals.  Conduct hypothesis tests for regression coefficients and correlation coefficients.
30. Use a computer software package to produce a multiple regression equation.  Calculate and interpret the multiple standard error of estimate, the coefficient of multiple determination, and confidence intervals.  Conduct hypothesis tests for regression coefficients.  Know how to insert quantitative information into a regression equation and how to evaluate residuals. 
31. Compute and interpret Spearman’s coefficient of rank correlation.  Conduct a test of hypothesis to determine whether the correlation among the ranks in the population is different from zero.

1. Introduction: Types of Variables, Levels of Measurement
2. Descriptive Measures:
   • Tables, Graphs
   • Measures of Location
   • Measures of Dispersion
   • Measures of Relative Standing
   • Application
3. Descriptive Measures:
• Tables, Graphs
4. Probability
   • Mean, Variance and Standard Deviation of Probability Distributions
   • Other Probability Distributions
     • Binomial
     • Hypergeometric
     • Poisson
     • Normal
     • Approximations to Exact Techniques
     • Sampling
     • Application
5. Single-Sample
   • Estimation – Point and Interval
   • Sample Size
   • Hypothesis Testing of Means
   • Hypothesis Testing of Proportions
   • Application
6. Correlation, Spearman’s Rank Correlation
7. Simple Linear Regression
   • Method of Least Squares
   • Measuring Accuracy of Prediction
   • Statistical Inference in Regression Analysis
   • Application
8. Multiple Regression
   • Measuring Accuracy of Prediction
• Statistical Inference in Multiple Regression Analysis
• Dummy Variables
• Residuals
• Application
9. Ethics is integrated throughout the course.