**INSTRUCTOR**: Ebenge Usip, Ph.D

**OFFICE**:
307 DeBartolo Hall; * Hrs*. TTh 12-2;Fri. 12-1pm;

(1) Study Guide by the authors.

(2) Learning Economics and Business Statistics with SPSS/win (4

(3) Web Tutorials by Dr. Usip; available on the Web at

**GRADING WEIGHT**:

Exam # 1 (Date to be announced)
20% of 500 points

Exam # 2 (Date to be announced)
20% of 500 points

Exam #
3 (Date to
be announced)
20% of 500 points

Homework
10% of 500 points

Final Exam. (Dec. 8, 1800 to 2000; Monday) 30% of 500 points

**APPROXIMATE SCALE**:
Will be discussed in class

Last day to withdraw with a "W" is Oct. 25 (by noon).

__OUTLINE__

**I. Meaning,
Branches & Role of Statistics in Decision Making:**
ch. 1

** Descriptive Statistics:
Graphical and Numerical Methods **

Tabular and Graphical Methods ch. 2

Numerical
Methods
ch. 3

**II. Probability & Probability
Distributions **

Probability: Concepts, Theorems, & Rules ch. 4

*MIDTERM #1, 4 ^{th}
WEEK: DATE TO BE ANNOUNCED chs. 1, 2,
3, & 4 *

Discrete Probability Distribution - Binomial only ch. 5

Continuous Probability Distributions - Uniform & Normal only chs. 6

Sampling and Sampling Distributions ch. 7

Inference: Interval Estimation ch. 8

Inference: Hypothesis Testing about the Mean & Proportion of Single Populations ch. 9

Inference: Chi-Square Tests of Goodness-of-fit & Independence* ch. 12

Inference: One-way Analysis of Variance (ANOVA) or the

Multiple Regression Analysis and Correlations Analysis ch. 15

** NOTES**:

administered. Late homework/project will not be graded.

lectures. Class participation is encouraged and rewarded

Both the SPSS/win primer and a Unix account (for Internet access) number will be given in class.

* If time permits

The main objective of the course is to survey important statistical techniques and their applications in business decision-making. Emphasis will be placed on problem formulation through the use of real-world business cases and databases and on interpreting results obtained from a PC-based statistical software. Using the computer for number crunching will permit the introduction of new material that solidifies the concepts and the applications of statistics in managerial/executive decision-making. This implies that you do not need to know how to derive the formulas (e.g., the least estimators in regression). However, you need to know how to interpret the results derived from them so that you can make appropriate decisions in complex situations using statistical graphics, numerical summaries, inferential approaches (Interval Estimation and Testing of Hypotheses), and statistical summaries of causal relationships (Regression Analysis). Thus, we will emphasize the

The course is designed to be totally computer oriented -- in keeping with today's use
of the computer to perform many of the tedious and time-consuming calculations involved in
statistical analysis. All computations will be done with the aid of the computer.
The statistical program that I have elected to use is the SPSS/win. My reasons for
electing this program include the ease with which it can be used in Windows computing
environment to perform sophisticated statistical analyses as well as the fact that it is
popularly used in industries world-wide. Also, it is available to students of business and
economics on the YSU Network. I have composed an **SPSS/win Primer** that
explains in detail the **Data entry formats and
the Command Sequence **convention that I will adopt throughout this and other
statistics courses. A copy of the primer and this syllabus is also available at my Website
(see the address above).

Data: The Primary Input of Applied Statistics

The modern business manager - whether trained in engineering, science, or
administration - constantly makes decisions based on a variety of factors. Typically, the
decision making process consists of both subjective and objective elements. **Objective**
decision making process is usually based on hard or **factual data**, whereas
**subjective** decision making is based on __personal experience__ and
therefore vary among the executives. In objective decision making, the manager is
interested in quantifying the competing alternatives, which must be clearly identified in
order to make a good decision (i.e., one that produces the best outcome). The final
objective decision you make often derives from quantifying the stated alternatives. The
quantification process requires **data** on the decision variables of
interest and proper use of __mathematical and statistical methods__. It is therefore no
coincidence that in applied statistics much emphasis is placed on data related concepts,
namely: (1) the meaning of data, (2) types of data, (3) type of variable in relation to a
specific data set, (4) the measurement scales (Nominal, Ordinal, Interval, and Ratio)
according to Steven's classification system, (5) data collection methods, and (6)
transformation methods for rendering a specific data set in a useable form as dictated in
part by the research study at hand. These are the conceptual issues that an executive must
be familiar with considering the informational significance of data. I will elaborate on
these concepts in class; and also highlight further the importance of data (whether in raw
form or organized into a frequency distribution) as the primary input in Applied
Statistics. The next section presents a synopsis of the data collection aspect and related
concepts.

**Data Collection and Related Concepts of a Population and
Random Sample
**Data collection is the starting point in any empirical study. Designing a
plan for data collection might be called

Statistics is particularly useful when you have a large group of people, firms, or
other items in the target **population**
that you would like to know about but cannot reasonably afford to investigate completely.
Instead, to achieve a useful but imperfect understanding of this population, you select a
smaller group called a **sample**
consisting of some - but not all - of the items of the population, using a **random
sampling technique** that is appropriate for your research situation. The **random sample** is one of the best
ways to select a practical sample, to be studied, from a population that is too large to
be examined in its entirety. By selecting randomly, two goals are accomplished:

**1**. You are guaranteed that the selection process is fair and proceeds
without bias; that is, all items have an **equal chance** of being selected.
This assures you that, on average, samples will be representative of the population
(although each particular random sample is usually only approximately, and not perfectly,
representative).

**2**. The randomness, introduced in a controlled way during the design
phase of the project, will help ensure validity of the statistical inferences drawn later.

In class, I will discuss the various random/probability sampling techniques as well as the graphical tools/devices for exploring a body of data to uncover hidden patterns. I will also examine possible preliminary transformations that may be necessary prior to using a data set in a research project.

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Overview or SPSS/win Primer or Course Information or Home Page or send me your Comments via E-mail.**