Transcript Statistics for Managers Using Microsoft Excel, 4/e

STAT 5101
Foundations of Data Science
Instructor: Xinyuan Song
Office: LSB 114, 39437929, email: [email protected]
Teaching Assistant: Lai-Fan Pun
Office: LSB G28, 39438529, email: [email protected]
Assessment Scheme
Exercise
Mid-term examination
20%
30%
Final examination
50%
October 31, 2012
7:00-9:00pm
No make-up examination
December 12, 2012
7:00-9:00pm
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Course Description


This course provides comprehensive coverage of basic
concepts of statistics.
Topics include
exploratory data analysis,
statistical graphics, sampling variability,
point and confidence interval estimation,
hypothesis testing,
other selected topics.

Two computer software: R and Microsoft Excel will be
introduced to describe and analyze data.
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Learning Outcomes
After completing the course, students should
be able to
understand basic concepts in statistics;
use various statistical methods and techniques to
summarize, present, and analyze data;
read statistical reports and recognize when the quantitative
information presented is accurate or misleading ;
use computer software (R and Excel) to analyze data and
draw conclusions.

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Textbook and Reference Books
Textbook
Levine, D. M., Stephan, D., Krehbiel, T. C. and Berenson, M. L.
Statistics for Managers Using Microsoft Excel 5th Edition. Pearson Prentice
Hall, 2008.
Reference book
1. Siegel, A. F. Practical Business Statistics 5th Edition. Mc Graw Hill,
2003.
2. Agresti, A. and Franklin, C. Statistics: The Art and Science of Learning
from Data. 2nd Edition, Pearson Prentice Hall, 2009.
3. Fraenkel, J., Wallen, N. and Sawin, E. I. Visual Statistics.
4. Any other textbook for introducing basic statistics.
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Organization of Textbook
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Presenting and Describing Information
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Introduction and Data Collection (Chapter 1)
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Presenting Data in Tables and Charts (Chapter 2)
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Numerical Descriptive Measures (Chapter 3)
Drawing Conclusions About Populations Using Sample
Information
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Basic Probability (Chapter 4)
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Some Important Discrete Probability Distributions (Chapter 5)

The Normal Distribution and Other Continuous Distributions (Chapter 6)

Sampling and Sampling Distributions (Chapter 7)
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Confidence Interval Estimation (Chapter 8)

Hypothesis Testing (Chapters 9-12)

Decision Making (Chapter 17)
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Organization of Textbook
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
Making Reliable Forecasts

Simple Linear Regression (Chapter 13)

Introduction to Multiple Regression (Chapter 14)
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Multiple Regression Model Building (Chapter 15)
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Time-Series Forecasting (Chapter 16)
Improving Business Process

Statistical Applications in Quality Management (Chapter 18)
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Course Outline
Chapter I Data Collection and Data Presentation
Chapter 2 Numerical Descriptive Measures
Chapter 3 Important Discrete Probability Distributions
Chapter 4 Important Continuous Distributions
Chapter 5 Sampling and Sampling Distributions
Chapter 6 Confidence Interval Estimation
Chapter 7 Hypothesis Testing: One Sample Tests
Chapter 8 Two-Sample Tests
Chapter 9 Chi-squared Tests and Nonparametric Tests
Chapter 10* Selected topic
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Chapter 1
Data Collection and Data Presentation
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Explain key definitions:
 Population vs. Sample
 Primary vs. Secondary Data
 Parameter vs. Statistic
 Descriptive vs. Inferential Statistics
Describe key data collection methods
Describe different sampling methods
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Probability Samples vs. Nonprobability Samples
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Identify types of data and levels of measurement
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Use graphical techniques to organize and present data
 ordered array
 stem-and-leaf display
 frequency distribution, polygon, and ogive
 scatter diagrams
 histogram
 bar charts, pie charts
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Chapter 2
Numerical Descriptive Measures
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Mean, median, mode
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Range, variance, standard deviation, coefficient of
variation
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Five-number summary
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Box-and-whiskers plot
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Correlation coefficient
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Chapter 3
Important Discrete Probability Distribution
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Define mean and standard deviation
Explain covariance and its application in finance
Binomial probability distribution
Poisson probability distribution
Hypergeometric probability distribution
Negative binomial distribution, geometirc distribution,
multinomial distribution
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Chapter 4
Important Continuous Distributions
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Continuous probability distribution
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Characteristics of the normal distribution
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Using a normal distribution table
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Evaluate the normality assumption
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Uniform and exponential distributions
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Gamma and Weibull distributions
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Chapter 5
Sampling and Sampling Distributions
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Types of sampling methods
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Sampling distributions
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Sampling distribution of the mean
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Sampling distribution of the proportion
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Central Limit Theorem
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Chapter 6
Confidence Interval Estimation
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Point estimate
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Confidence interval estimate
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Confidence interval for a population mean
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Confidence interval for a population proportion
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Determine the required sample size
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Chapter 7
Hypothesis Testing: One Sample Tests
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Null and alternative hypotheses
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A decision rule for testing a hypothesis
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Hypothesis testing
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Type I and Type II errors
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Chapter 8
Two-Sample Tests
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Test the difference between two independent
population means
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Test two means from related samples
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Test the difference between two proportions
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F test for the difference between two variances
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Chapter 9
Chi-Square Tests and Nonparametric Tests
 Chi-square test for the difference between two
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proportions
Chi-square test for differences in more than two
proportions
Chi-square test for independence
The Wilcoxon rank sum test for two population
medians
The Kruskal-Wallis H-test for multiple population
medians
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