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Includes descriptive statistics, including numerical and graphical presentation of data, measures of central tendency, dispersion and elementary probabilities. Introduction to several discrete and continuous probability distributions . Introduction to inferential statistics through selected topics such as sampling, confidence limits of the mean, hypotheses testing, simple linear regression and the chi-squared test for independence.
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Upon successful completion of this course, the student will be able to:
Discuss the value of statistical information in a variety of business disciplines and work environments.
Assess statistical information portrayed in the media, work and educational environments.
Organize and analyze quantitative data.
Calculate descriptive statistics measures such as the mean, weighted mean, median, mode, standard deviation, coefficient of variation and variance.
Use graphical techniques to present data in such a manner that it is understood by and meaningful to colleagues and clients.
Analyze data presented in frequency distributions, histograms and boxplots.
Apply the common rules of probability to evaluate business alternatives.
Analyze and calculate probabilities for Binomial, Poisson, Uniform and Exponential probability distributions.
Calculate expected values and standard deviations for random variables.
Construct and interpret a confidence interval estimate for a single population mean using both the standard normal and t distributions.
Establish and interpret a confidence interval estimate for a single population proportion.
Determine the required sample size for estimating a single population mean or proportion.
Carry out an appropriate hypothesis test on a single population mean or proportion.
Interpret the p-value of the test statistic.
Carry out a hypothesis test comparing two population means or proportions.
Construct a contingency table and perform a chi squared test of independence.
Analyze simple two-variable problems using linear regression and correlation.
Interpret the results of a computer generated regression model and ANOVA table.
Effective as of Winter 2020
OPMT 1130 is offered as a part of the following programs:
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