BSAD 702 – Statistics Analysis Foundation
The Statistics Foundation course is designed for students entering the MBA program who lack a basic knowledge of how to use statistics for business decision making.
At the end of the course you will be able to determine whether a business problem should be addressed with a statistical tool, and if so: 1) select the appropriate tool, 2) implement the tool using Excel and 3) interpret results in a manner that help you address the underlying business problem. The statistical tools include frequencies, means/medians, standard deviation, crosstabulation, comparison of mean across groups, correlation, regression and p-value.
College algebra and basic competency in using MS Excel. Not for MBA credit.
Lesson 1: Introduction
Entering and formatting data into Excel
Use basic Excel functions and turn on the Data Analysis toolpak
Sort data in a manner that keeps rows of data intact
Identify basic charts and graphs that can be created in Excel
Lesson 2: Variables
Recognize types of variables
Calculate frequencies for non-metric variables, both in raw numbers (counts) and in percents.
Calculate central tendencies including mean and median.
Determine low and high end values for metric variables.
At a beginning level, use analysis results to make recommendations on business issues.
Lesson 3: Basics of Statistics
Describe what is meant by a statistic
Articulate why sample statistics are used, given interests in a population
Explain the importance of having goals that motivate data collection
Be able to produce crosstabulation statistics
Be able to produce comparison of means statistics
Be able to produce regression coefficient statistics
Lesson 4: Continuation of bivariate (two variable) statistics and p-values
Explain what it means to say two variables are related to each other
Identify appropriate instances for using a p-value
Produce and interpret a standard deviation
Produce and interpret a correlation
Produce and interpret a p-value
Lesson 5: Regression and variable coding
Explain how and why data analysis may warrant conclusions that stretch beyond the data assessed.
Produce and interpret a regression constant.
Explain how coding a variable impacts analysis results.
Explain how interpreting analysis results should depend on how variables are coded.
Use results from univariate analysis and bivariate regression to make decisions
Lesson 6: Statistical models
Recognize how variables can be recoded to help examine data
Explain what is meant by a statistical model, and how to use variables to create a model
How statistics can be used for forecasting
Producing multiple regression results
Producing and interpreting R-square
Lesson 7: Data collection and non-linear regression
Data collection processes and how data collection may impact data analysis conclusions
Statistics used for pricing
Producing regression analysis to assess relationships that are non-linear
Lesson 8: Wrap up fundamentals
Software packages that go beyond capabilities of Excel
Producing and interpreting regressions that involve multiple variables
Using statistics to conduct Balanced Scorecard analysis