INSTRUCTOR: Ebenge Usip, Ph.D
OFFICE: 307 DeBartolo Hall; Hrs. TTh 12-2;Fri. 12-1pm; Ph. 742 1682; Email: firstname.lastname@example.org
TEXT: Statistics for Business and Economics (7th.ed.) - by Anderson, Sweeney & Williams
(1) Study Guide by the authors.
(2) Learning Economics and Business Statistics with SPSS/win (4th edition) by Dr. Usip.
(3) Web Tutorials by Dr. Usip; available on the Web at http://www.cc.ysu.edu/~eeusip/
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).
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, 4th 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
III. Sampling, Sampling Distributions & Statistical Inference
Sampling and Sampling Distributions ch. 7
Inference: Interval Estimation ch. 8
MIDTERM #2, 8th WEEK: DATE TO BE ANNOUNCED chs. 5, 6, 7, & 8
Inference: Hypothesis Testing about the Mean & Proportion of Single Populations ch. 9
Inference: Hypothesis Testing about the Mean & Proportion of Two Populations* ch. 10
Inference: Chi-Square Tests of Goodness-of-fit & Independence* ch. 12
Inference: One-way Analysis of Variance (ANOVA) or the F-tests ch. 13
MIDTERM #3, 12th WEEK: DATE TO BE ANNOUNCED chs. 9, 10, 12, & 13
IV. Regression and Correlation Analysis
Simple Regression and Correlation and Analysis ch. 14
Multiple Regression Analysis and Correlations Analysis ch. 15
V. Time Series & Index Numbers
Time Series Analysis & Forecasting ch. 18
FINAL EXAM, 16th WEEK chs. 14, 15, & 18
1. Prerequisite: This is a pre-MBA course in statistics
2. Class attendance is optional but strongly encouraged. No make-up examination will be
administered. Late homework/project will not be graded.
3. It is your responsibility to be familiar with the assigned chapters and all materials covered during
lectures. Class participation is encouraged and rewarded
4. Computer application using SPSS/win and use of the Internet are required part of the course.
Both the SPSS/win primer and a Unix account (for Internet access) number will be given in class.
* If time permits
COURSE OVERVIEW: Goals and Objectives
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 key ideas that underlie Applied Statistics, namely: collecting data, organizing data, analyzing data, and interpreting the results derived from the data. Applications, examples, exercises and cases will examine problems in such fields as finance, accounting, market research, R&D, consumer research, economics, psychology, and education, and medicine. Exposure to a variety of applications will hopefully help you develop a pattern of thought that will persist in your executive career.
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
Data collection is the starting point in any empirical study. Designing a plan for data collection might be called Sample Survey in a marketing study or Experimental Design in a chemical manufacturing process optimization study. This phase of designing the study involves planning the details of data gathering. A careful design can avoid the costs and disappointment of finding out - too late - that the data collected are not adequate to answer the important questions. A good design will also collect just the right amount of data: enough to be useful but not so much as to be wasteful. Thus, by planning ahead, you can help ensure that the analysis phase will go smoothly and hold the cost of the project.
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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