OPIM-554 Applied Business Statistics
MBA Core

Topics Covered in this Class:

0) Brief review of Essential Statistical Concepts

1) Statistical Inference: Estimation and Testing Hypotheses
Statistical Inference is the art of making statements about the general (the Population) based on the specific (the Sample). Any such attempt has two sobering properties:
• Our answer is almost certainly incorrect
• We never know by how much we are wrong.
Nevertheless, most human endeavor proceeds by making such inferences and it is therefore, important to conduct the process as effectively as possible. The two general categories of statistical inference, estimation and hypothesis testing, can be distinguished by different purposes: estimation is concerned with estimating the value of an unknown population parameter; hypothesis testing is concerned with making a decision about a hypothesized value of an unknown population parameter.

In chapter 5 “Inferences Based on a Single Sample: Estimation with Confidence Intervals”, we estimate population means and proportions based on a single sample selected from the population of interest and, more importantly, show how to attach a measure of uncertainty to that point estimate. In chapter 6 “Inferences Based on a Single Sample: Tests of Hypothesis,” we show how to utilize the sample information to test whether a population parameter conforms to a pre-specified value.

2) Applied Regression Analysis:
Regression analysis is a quantitative tool for modeling the relationship between a set of explanatory or independent variables and a dependent variable. The explanatory variables may be controlled (e.g. advertising expenditures) or observational (e.g. responses to a survey). The value of such models lies both in direct prediction and in “what if” analyses.

Chapter 10 “Simple Linear Regression,” covers the simplest situation, the linear relationship between a single explanatory variable and a dependent variable. Chapter 11 “Multiple Regression and Model Building” extends the basic concepts developed in chapter 10, modeling the mean value of dependent variable as a function of two or more explanatory variables. The emphasis in the course is the development of model-building skills and the ability to interpret the results of a regression analysis.
Credits: 1.75
Prerequisites: MBA 1st year students only.
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