BSAD 702 – Statistics Analysis Foundation

2 Credits

Instructors

• Bryan Lilly, Ph.D. CPIM

•

Description

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.

1. Determine whether a business problem should be addressed with a statistical tool
2. Be able to select an the right statistical tool for a given problem
3. Use the tool, thus conducting statistical analysis using Excel
4. Interpret output from statistical tools

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

       Assignment-1

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.

Assignment-2

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

Assignment-3

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

Assignment-4

Midterm Exam

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

Assignment-5

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

Assignment-6

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

Assignment-7

Lesson 8: Wrap up fundamentals

Ranked data

Software packages that go beyond capabilities of Excel

Producing and interpreting regressions that involve multiple variables

Using statistics to conduct Balanced Scorecard analysis

Assignment-8

Final Exam