Business
MA217: Statistical Methods for Business (5 ECTS)
This module demontrates methods in statistical inference with applications in Business, Finance, Marketing and Ecomomics. This is a first course in statistical inference covering sampling distributions, contruction of confidence intervals and hypothesis testing, and communication of results of analysis applied to a range of business problems. Students must have completed an introductory course in descriptive statistics and probability similar to the content of MA109 Statistics for Business.
Taught in Semester(s) I. Examined in Semester(s) I.
Workload: 34 hours (24 Lecture hours, 10 Lab hours).
Module Learning Outcomes.
On successful completion of this module the learner should be able to:
- Define and identify in applications, basic terms: experimental unit, population, sample, variables and their types, parameter, statistic, descriptive statistics and inferential statistics.
- Define the term standard error and define, discuss and identify common sampling distributions and the define and apply the Central Limit Theorem in the context of the sampling distribution of the mean and the sampling distribution of the proportion of successes. Discuss the sampling distribution of the mean for large and small samples and discuss and check any assumptions that apply in those cases.
- Construct and interpret a confidence interval for a population mean for large and small samples. Discuss and check any assumptions that apply in doing so. Construct confidence intervals at varying levels of confidence and discuss the implications of changes in the confidence level and the sample size on the resulting interval.
- Carry out a hypothesis test for a population mean for large and small samples. Discuss and check any assumptions that apply in carrying out the analysis. Define type I and type II error, the significance level, the test statistic, the power of the test and the p-value and interpret each of these terns in application. Complete the hypothesis test by either determing a rejection region for the test statistic, a rejection region for the sample estimate of the parameter, or a p-value. Identify and complete one and two tailed testing procedures.
- Expand application of basic skills learned in contructing confidence intervals and carrying out hypothesis tests for infering the value of a single population mean to other problems such as:
- inference for comparing means of two populations (large and small samples), independent samples.
- inference for comparing means between two populations (large and small samples), paired samples.
- inference for comparing means of more than two populations using ANOVA.
- inference for a single population proportion of successes(large samples only) in a binomial experiment.
- inference for population proportions in a multinomial experiment, the $\chi^2$ goodness of fit test.
- inference for comparing proportions of successes between two populations (large samples only), independent samples.
- inference for testing for an association between two qualitative variables in a population, the $\chi^2$ association/independence test.
- inference for testing for a linear relationship between two quantitative variables in a population, via simple regression analysis, including; estimation of the line of best fit, tesing for s significant population relationship by carrying out inference for the population slope parameter, and using the fitted line for estimation via a confidence interval or prediction interval as appropriate.
Indicative Content
Module Resources
SPSS software
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