Download cuny graduate center - Computer Science

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Foundations of statistics wikipedia, lookup

History of statistics wikipedia, lookup

Statistics wikipedia, lookup

Transcript
THE CITY COLLEGE
Department of Computer Science
MIS G1010. Statistics and Decision Making
Fall 2015
Prof. Abbe Mowshowitz
Office: NAC 7/244
Phone: (212) 650-6161
Hours: M: 4-6
email: [email protected]
TEXT: Sharpe, De Veaux, and Velleman. Business Statistics (second edition). Addison-Wesley,
2012.
LECTURE NOTES: www-cs.ccny.cuny.edu/~abbe/BS
GRADING: Test #1 (20%), Test #2 (20%), assignments (20%), final exam-cumulative (40%).
The objective of this course is to help you learn to analyze data and use methods of statistical
inference in making business decisions. Central to the course is the application of fundamental
concepts covered in probability and decision making to the problem of drawing inferences from
data on observed outcomes. Topics covered during the first part of the course will include
statistical sampling and sampling distributions, point estimation and confidence intervals,
hypothesis testing, and correlations among variables. The second part of the course will focus on
multivariate analysis, with special attention paid to the inferences that may drawn with respect to
prediction and causality.
ASSIGNMENTS:
(1) Mini case study projects for class presentation and discussion. Each week a student will be
responsible for analyzing and leading a discussion of one of the mini case studies in the
textbook.
(2) Problem assignments: five exercises from the chapters covered in the text will be assigned
every two weeks and the solutions will be discussed in class.
Course Outline
Week 1. Foundations 1: Variation, Data, Surveys and Sampling (Chapters 1-3)
Overview; data in statistical analysis; surveys and sampling.
Week 2. Foundations 2: Displaying and Describing Data, Correlation (Chapters 4-6)
Frequency tables, charts and contingency tables; elementary probability theory;
quantitative data: boxplots, outliers, standardization.
Week 3. Randomness and Probability Models (Chapter 7-8)
Expected value, variance and standard deviation of a random variable; discrete probability
models; continuous, random variables.
Week 4. Normal Model (Chapter 9) Standard deviation; normal distribution; plot; sums of normal;
approximation.
1
Week 5. Sampling Distributions and the Normal Model (Chapter 10)
Disribution of sample proportions; sampling distribution for proportions; Central Limit
Theorem; sampling distribution of the mean.
** Test #1 (October 5 – Week 5) – one hour covering Weeks 1-4
Week 6. Confidence Intervals for Proportions, Confidence Intervals for Means (Chaps. 11-12)
Confidence intervals; margin of error; sample size; sampling distribution for the mean;
confidence interval for means; degrees of freedom.
Week 7. Testing Hypotheses (Chapter 13)
Hypotheses; trial as hypothesis test; P-values; alternative hypotheses; one-sample t-test;
alpha levels and significance; critical values; confidence intervals and tests; types of error.
Week 8. Comparing Two Groups (Chapter 14)
Comparing two means; two-sample t-test; confidence interval for difference between means;
Paired data; pooled t-test; paired t-test.
Week 9. Inference for Counts: Chi-Square Tests (Chapter 15)
Goodness of fit tests; interpreting Chi-square values; analyzing residuals; Chi-square tests
for homogeneity and independence.
** Test #2 (Nov. 9 – Week 9) – one hour covering Weeks 5-8
Week 10. Inference for Regression (Chapter 16)
Population and sample; standard error of slope; test for the regression slope; hypothesis
test for correlation; standard errors.
Week 11. Multiple Regression (Chapter 18)
Multiple regression model; multiple regression coefficients; assumptions and conditions;
testing the model.
Week 12. Time Series Analysis (Chapter 20)
Components of time series; smoothing methods; simple and weighted moving averages;
exponential smoothing; autoregressive models; random walks; forecasting with regression
models.
Weeks 13-14. Design and Analysis of Experiments and Observational Studies (Chapter 21)
Observational studies; experimental design; one-way analysis of variance (ANOVA);
assumptions and conditions; multifactor designs.
2