Catalog:

Probability distributions; introduction to statistical methods, including hypothesis testing and confidence intervals, one way ANOVA, simple linear regression, quality control basics; applications, and the use of statistical software. Prereq.: MATH 222 or equivalent. 3 Cr. S.

Text:

Jay Devore and Nicholas Farnum,

Applied Statistics for Engineers and Scientists, 1999, Brooks / Cole Publishing, Pacific Grove, CA, ISBN 0-534-3560-X

Objectives:

The STAT 353 course, Statistics for Engineers, provides education and training to equip the students to:

1.      understand data variability and dispersion,

2.      understand probability distributions,

3.      apply statistical tools for engineering data,

4.      be able to use linear regression on measurement data.

Outcomes:

After completing this course, the students will have:

1.      the ability to summarize data distribution using statistical parameters,

2.      the ability to identify probability distributions,

3.      the ability to design and process data from simple experiments,

4.      the ability to correlate data using linear regressions.

Contents:

Day

Topic

Text

Lect. No.

January

1

13

Definition of statistics, population, sample, parameter, statistic.

1.1

2

15

Graphical representation of data: Dot plot, Stem and leaf plot, Histogram

1.2

3

20

Continuing Histogram, Describing distributions: Continuous distributions: Normal distribution, Idea of other continuous distributions like uniform, Weibul, and Beta, etc.

1.2, 1.3, 1.4, 1.5

4

22

Discrete distributions, Binomial distribution, Poisson distribution, and idea of other discrete distributions like hyper geometric, negative binomial etc.

1.3, 1.6

5

27

Measures of central value of a data set: sample mean, sample median, mean of discrete variable (e.g. Binomial or Poisson distribution variates)

2.1

6

29

Mean of any continuous random variable (e.g. Normal, Uniform variates)

2.1

February

7

03

Measures of variability: sample variance, variance of discrete distributions (examples of Binomial, Poisson distribution)

2.2

8

05

Variance of continuous distributions: examples of normal and uniform distributions

2.2

9

09

Quartiles, Box plots, outliers etc. (Quantile plots – do yourself)

2.3, 2.4

10

12

Bivariate data: Scatter plot, correlation

3.1, 3.2

11

17

Exam I

12

19

Fitting bivariate data, and its interpretation, coefficient of determination

3.3

13

24

Fitting a polynomial function, Using more than one predictor (Matrix algebra may be required)

3.4, 3.5

14

26

Distributions for two variables, correlation and idea of bivariate normal distribution. Independent joint variables.

3.6

March

15

02

Operating data: Idea of sampling techniques such as SRS, Stratified sampling, cluster sampling

4.1

16

04

Systematic sampling etc. Types of data: Primary data: experiments, surveys; Secondary data or benchmarks etc.

4.2, 4.3

17

09

Measurement systems: Accuracy and precision; Repeatability and reproducibility and interlab comparisons.

4.4

18

11

Probability: Experiment, Event, Additive law, Complementary probability, Conditional probability, Multiplicative law

5.1-5.3

16

SPRING BREAK

18

19

23

Mean and variance of a random variable (do yourself), Sampling distributions, Central limit theorem – A backbone of theory in statistics,

5.4, 5.5, 5.6

20

25

Sampling distribution of a sample proportion, Quality Control: R-Chart, X-Bar Chart, Process Mean and Variation estimations, Control charts

6.3, 6.4

21

30

Hypothesis testing: Null Hypothesis, alternative hypothesis, type-1, and type-2 errors etc. General steps for testing a hypothesis.

 6.5

April

22

01

Large sample test for single mean and difference between two means, and Interval estimates. (test for proportions – do yourself)

7.2, 7.3

23

06

Exam II

24

08

Small sample test for single mean and difference between two means, and interval estimates.

7.4, 7.5

25

13

Paired t-test, One-way ANOVA

7.5, 9.2

26

15

One-way ANOVA (Tukey’s test, Dunnett’s test etc)

9.2

27

20

Two-way ANOVA

9.4

28

22

Idea of factorial designs

10.2

29

27

Multiple regression revising to test regression coefficients etc.

11.2

30

29

Idea of Likelihood estimation techniques and on last day (Discussion and questions)

7.6

Schedule is tentative and the material from one lecture to another may be shifted if required, some topics may be added or dropped.

Grading

Quizzes

Homework

Final Exam

2 quizzes at 15% each

5 assignments at 5% each

30%

25%

45%

Scale

A

90 – 100%

A-

85 – 89%

B+

80 – 84%

B

75 – 79%

B-

70 – 74%

C+

65 – 69%

C

60 – 64%

C-

55 – 59%

D+

50 – 54%

D

45 – 59%

F

<45%

Remarks

( 1 ) All examinations will be ‘closed book’. Calculators are allowed, but no formulae sheets.

( 2 ) 20% marks will be deducted from late assignments.

( 3 ) Date and time for the final exam will be announced in the class. Experience shows that the date(s) for the midterm exams generally changes, so be regular with the class activities/announcements.

( 4 ) Assignments will be given in the class. A few students feel that assignments dead lines should be a bit flexible, and other feel not. Both kind of experience will be tried.

( 5 ) Any other change in the schedule will be announced in the class.

( 6 ) Please make sure that you are registered with one of these sections, as otherwise your final grade may not be submitted.