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B020529 - INDUSTRIAL STATISTICS
Main information
Teaching Language
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Further information
Type of Assessment
Course program
Academic Year 2015-16
Course year
Second year - First Semester
Belonging Department
Industrial Engineering (DIEF)
Course Type
Single education field course
Scientific Area
SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH
Credits
6
Teaching Hours
54
Teaching Term
21/09/2015 ⇒ 18/12/2015
Attendance required
No
Type of Evaluation
Final Grade
Course program
show
Lectureship
Teaching Language
Ityalian
Suggested readings (Search our library's catalogue)
NAVIDI, William
Probabilità e statistica per l'ingegneria e le scienze
McGraw-Hill 2006
Montgomery et al. Statistica per ingegneria, Egea 2004
Probabilità e statistica per l'ingegneria e le scienze
McGraw-Hill 2006
Montgomery et al. Statistica per ingegneria, Egea 2004
Learning Objectives
Acquisition of the main statistical methods with specific reference to their usefulness in solving problems applied to engineering
Prerequisites
The basic elements of the "Calculation" for one and more variables (Analysis 1 and 2) and linear algebra (geometry)
Teaching Methods
Lectures and classroom exercises
Further information
'this course is in the platform moodle University where will be inserted including any supplementary material
Type of Assessment
Written examination of applications
Course program
A. Descriptive Statistics
1. Populations and samples. Data
2. Summary of numerical data: average, median, mode; standard deviation, interquartile range; skewness, kurtosis.
2. Graphical representations: histograms, frequency polygons, exploratory data analysis. Stem-and-leaf plots, box plots-mustache (box plot)).
B. Fundamentals of Probability
1. The logic uncertain. Events, Probability, Events dependent and independent, counting techniques.
2. Probability distributions, moments. Probabilistic models:
Discrete: Binomial, hypergeometric, geometric, negative binomial, Poisson:
Continue: normal, log-normal (Gamma, Beta, Weibull).
C. Statistical inference
1. Point and interval estimation of parameters comparisons between different samples.
2. Hypothesis testing. Fit testing [nods].
3. Analysis of correlation.
4. Simple linear regression: least squares parameter estimation and model verification.
1. Populations and samples. Data
2. Summary of numerical data: average, median, mode; standard deviation, interquartile range; skewness, kurtosis.
2. Graphical representations: histograms, frequency polygons, exploratory data analysis. Stem-and-leaf plots, box plots-mustache (box plot)).
B. Fundamentals of Probability
1. The logic uncertain. Events, Probability, Events dependent and independent, counting techniques.
2. Probability distributions, moments. Probabilistic models:
Discrete: Binomial, hypergeometric, geometric, negative binomial, Poisson:
Continue: normal, log-normal (Gamma, Beta, Weibull).
C. Statistical inference
1. Point and interval estimation of parameters comparisons between different samples.
2. Hypothesis testing. Fit testing [nods].
3. Analysis of correlation.
4. Simple linear regression: least squares parameter estimation and model verification.