NEW MEXICO JUNIOR COLLEGE

Statistics for Energy Industry

SYLLABUS

- GENERAL COURSE INFORMATION
- COURSE DESCRIPTION
This course will provide students with basic knowledge of statistics used in various energy industries. Topics will include data collection, charting, chart formulas, process calculations, graphic presentations and reliability methods. Example problems are provided and solved using spreadsheet software. This is a three credit hour course.

- COURSE RATIONALE / TRANSFERABILITY
It is important to check with the institution to which you are planning to transfer to determine transferability. All students are encouraged to keep the course syllabus, as it will help determine the transferability of this course credit to another institution.

- REQUIRED / SUGGESTED COURSE MATERIALS
**Required:**

Text provided by Instructor.

U.S. NRC Applying Statistics

NUREG-1475, Revision 1

Date Published: March 2011**Suggested:**

None.You can buy your books online at the NMJC Bookstore.

- GRADING POLICY
**Retrieving Grades from T-BirdWeb Portal**

Go to the New Mexico Junior College T-BirdWeb Portal login page. Please enter your User Identification Number (ID), which is your Banner ID, and your Personal Identification Number (PIN). When finished, click Login.**Tips for Success in Online Courses:**

1. Log in to class regularly.

2. Pay attention.

3. Take notes.

4. Keep up with readings and assignments.

5. Ask questions when you do not understand something.

6. Utilize your professor’s office hours and e-mail.

7. Read the text.

8. Adhere to the deadlines posted in the course outline. - INSTITUTIONAL STUDENT LEARNING OUTCOMES
New Mexico Junior College’s institutional student learning outcomes represent the knowledge and abilities developed by students attending New Mexico Junior College. Upon completion students should achieve the following learning outcomes along with specific curriculum outcomes for respective areas of study:

- Communication
- Comprehend information to summarize, analyze, evaluate, and apply to a specific situation.
- Communicate in an accurate, correct, and understandable manner.
- Critical Thinking and Problem Solving
- Define a problem and arrive at a logical solution.
- Use appropriate technology and information systems to collect, analyze, and organize information.
- Apply critical thinking, analysis, and problem solving to data.
- Self and Community
- Analyze and reflect on the ethical dimensions of legal, social, and / or scientific issues.
- Communicate an awareness of a variety of perspectives of ethical issues.
- Interact with individuals and within groups with integrity and awareness of others’ opinions, feelings and values.

- DEPARTMENTAL STUDENT LEARNING OUTCOMES
The objective of this course is to help students understand basic computer applications, data collection and analysis.

- SPECIFIC COURSE STUDENT LEARNING OUTCOMES
By the end of this course students should be able to use a computer to solve problems by applying the following statistical methods:

Chapter 1 Introduction to analysis

Chapter 2 Descriptive statistics

Chapter 3 Statistical graphics

Chapter 4 Basics of probability

Chapter 5 Errors

Chapter 6 Random variables

Chapter 7 Continuous distributions

Chapter 8 Discrete distributions

Chapter 9 Estimation

Chapter 10 Inference

Chapter 11 Goodness-of-fit tests

Chapter 12 Contingency Tables

Chapter 13 Tests of statistical hypotheses: One mean

Chapter 14 Tests for statistical hypotheses: Variances

Chapter 15 Tests for statistical hypotheses: Two means

Chapter 16 One-way ANOVA

Chapter 17 Two-way ANOVA

Chapter 18 Regression

Chapter 19 Simple linear correlation

Chapter 20 Bayesian probability inference

Chapter 21 Hypergeometric experiments

Chapter 22 Binomial experiments

Chapter 23 Poisson experiments

Chapter 24 Quality assurance

Chapter 25 Nonparametric statistics

Chapter 26 Outliers

Chapter 27 Simulation

- REQUIRED TECHNICAL COMPETENCIES AND EQUIPMENT
**Student Requirements**

If you have not already received login information for Canvas/T-BirdWeb Portal/E-mail, you will need to contact the Enrollment Management office at (575) 492-2546.Check first-time login page for instructions at www.nmjc.edu/distancelearning/coursescourseschedules/canvasinstructions.aspx.

**Canvas Assistance**- Review the Canvas Student Orientation.
- Call the Canvas helpdesk at (575) 399-2199 for assistance and have your course CRN (ex. 10023) and/or student ID number available.

You must have access, on a regular basis, to a computer that supports the Canvas minimum specifications and has an active connection to the Internet. See the minimum computer specification requirements at www.nmjc.edu/distancelearning/coursescourseschedules/Canvasinstructions.aspx.

- ADDITIONAL INFORMATION
__Response Time Frames____Grading with feedback__: Within one day of posting assignment__Email__: If you send me an email at address on your Home Page my response will usually be within an hour.__Instructor login__: I log into Canvas several times a day from computer or iPhone.__If you need help contact me by__:1. Canvas Mail

2. My personal email address on your Home Page

3. Text my phone number

4. Call between 9AM and 9PM (CST/CDT) any day of the week including holidays

- GENERAL/MISCELLANEOUS
**Students will be held responsible for the information on these pages.****Academic Honesty**

Each student is expected to maintain the highest standards of honesty and integrity in online academic and professional matters. The College reserves the right to take disciplinary action, up to and including dismissal, against any student who is found guilty of academic dishonesty or otherwise fails to meet these standards. Academic dishonesty includes, but is not limited to, dishonesty in quizzes, tests, or assignments; claiming credit for work not done or done by others; and nondisclosure or misrepresentation in filling out applications or other College records. Cheating or gaining illegal information for any type of graded work is considered dishonest and will be dealt with accordingly.**Americans with Disabilities Act (ADA) Information**

Any student requiring special accommodations should contact the Special Needs Student Services Coordinator at (575) 492-2576 or by e-mail at krueda@nmjc.edu.**Attendance Policy and Participation Expectations**

It is expected that you regularly log into class at least three times weekly and check your Canvas mail to ensure you have not missed any changes/updates. Students are expected to complete discussions/quizzes/tests/ assignments before deadlines expire.**Canvas Help**

If you experience difficulty with Canvas you may reach the Canvas Helpdesk at canvashelpdesk@nmjc.edu, or by calling the 24 hour helpdesk phone at (575) 399-2199.**Netiquette**

The professor is responsible for monitoring and evaluating student conduct and student behavior within the Canvas course. By registering for this class, the student is assumed to have entered into an agreement with New Mexico Junior College and the professor to log into the class regularly and to behave in an**appropriate**manner at all times. Disruptive behavior**may**result in the student being removed from the class and dropped for the semester. For comprehensive information on the common rules of netiquette and other online issues, please review the NMJC Online Student Handbook.**Online Learning Environment**

By participating in an online class, you undertake responsibility for your own progress and time management.- Pay attention to deadlines and do not forget them (keep a calendar if it helps).
- Carefully read and understand all assignments.
- Complete assignments according to posted instructions and notes. Do not just glance at an assignment before attempting to complete it (this is likely to result in a poor grade).
- Questions may be posed for clarification in the class discussion area or by sending an e-mail to your instructor.

**Plagiarism**

Offering the work of another as one’s own, without proper acknowledgment, is plagiarism; therefore, any student who fails to give credit for quotations or essentially identical expression of material taken from books, encyclopedias, magazines and other reference works, or from the themes, reports, or other writings of a fellow student, is guilty of plagiarism. Plagiarism violates the academic honesty policy and is considered cheating.**Tutoring Assistance**

Free tutoring services are available to all NMJC students through Brainfuse and the Academic Success Center located in Mansur Hall room 123 and 124.**Withdrawal Policy**

The instructor has the right to drop any student who has failed to log on to Canvas for two weeks or more, but it is not guaranteed that the instructor will drop you. If the student chooses to stop attending a class, he/she should withdraw from the class by accessing your student account in the T-Bird Web Portal at www.nmjc.edu, or submitting the required paperwork to the Registrar’s Office by**5:00 p.m. on Friday, February 24, 2017**. Failure to withdraw yourself from a course by this date may result in your receiving an “F” in the course. All students are encouraged to discuss their class status with the professor prior to withdrawing from the class. - ACADEMIC CALENDAR
- FINALS SCHEDULE
- COURSE OUTLINE
__Text provided by Instructor__U.S. NRC Applying Statistics

NUREG-1475, Revision 1

Date Published: March 2011

Syllabus quiz 500 points (you can use your syllabus for this quiz)

Weekly quizzes 50 points

Midterm 500 points

Final Exam 500 points

**WEEK 1**: 17 Jan to 23 Jan__Syllabus Quiz 500 points__Chapter 1 Introduction

1.1 What to look for in Chapter 1

1.2 What is statistics?

1.3 Probability and statistics

1.4 Data

1.5 Scales of measurement

1.6 Four basic concepts in statistics

1.7 Evaluating statistical statements

1.8 Misconceptions about statistics

1.9 Spreadsheet computation

Chapter 2 Descriptive statistics

2.1 What to look for in Chapter 2

2.2 Descriptive statistics

2.3 Measures of centrality

2.4 The weighted mean

2.5 Spreadsheet functions for measuring centrality

2.6 Measures of dispersion: range and quantiles

2.7 Variance and standard deviation

2.8 Spreadsheet functions for measuring dispersion

2.9 Descriptive statistics with a hand-held calculator

2.10 Descriptive statistics for coded data

2.11 Skewness and kurtosis

2.12 An empirical rule for a mound-shaped dataset

2.13 Estimating the standard deviation from the range

2.14 Chebyshev’s inequality

Chapter 3 Statistical graphics

3.1 What to look for in Chapter 3

3.2 What’s in a graph?

3.3 The pie chart

3.4 Suggestions for constructing pie charts

3.5 The bar chart

3.6 Suggestions for constructing bar charts

3.7 The histogram

3.8 Suggestions for constructing histograms

3.9 The box plot

3.10 Suggestions for constructing box plots

3.11 The stem-and-leaf display

3.12 Suggestions for constructing a stem-and-leaf display

__Week 1 Quiz 50 points__WEEK 2: 24 Jan to 30 Jan

Chapter 4 Basics of probability

4.1 What to look for in Chapter 4

4.2 The concept of probability

4.3 Sample spaces and events

4.4 Basic set theory

4.5 Basic rules and principles of probability

4.6 Marginal and joint probabilities

4.7 Conditional probability

4.8 Bayes’ Theorem

4.9 Probability Estimation

Chapter 5 Errors

5.1 What to look for in Chapter 5

5.2 Errors, errors, … everywhere

5.3 Characterizing errors: accuracy and precision

5.4 Uncertainty

Chapter 6 Random variables

6.1 What to look for in Chapter 6

6.2 Random variables

6.3 Distributions of discrete random variables

6.4 Distributions of continuous random variables

6.5 Distributions of discrete bivariate random variables

6.6 Distributions of continuous bivariate random variables

6.7 Expected value and variance

6.8 Linear combinations

6.9 Linear contrasts

6.10 Mean square error

Chapter 7 Continuous distributions

7.1 What to look for in Chapter 7

7.2 Uniform distribution

7.3 Standard uniform distribution

7.4 Normal distribution

7.5 Standard normal distribution

7.6 Table lookup for the normal distribution

7.7 Spreadsheet functions for normal probabilities

7.8 The Central Limit Theorem

7.9 Lognormal distribution

7.10 Chi-square distribution

7.11 Student’s t-distribution

7.12 F-distribution

7.13 Exponential distribution

7.14 Gamma distribution

7.15 Beta distribution

__Week 2 Quiz 50 points__WEEK 3: 31 Jan to 6 Feb

Chapter 8 Discrete distributions

8.1 What to look for in Chapter 8

8.2 Discrete uniform distribution

8.3 Sampling for attributes

8.4 Sampling with and without replacement

8.5 Bernoulli distribution

8.6 Hyper-geometric distribution

8.7 Binomial distribution

8.8 Geometric distribution

8.9 Negative binomial distribution

8.10 Poisson distribution

Chapter 9 Estimation

9.1 What to look for in the remainder of this course

9.2 What to look for in Chapter 9

9.3 Estimation and inference

9.4 Elements of estimation

9.5 Point estimators

9.6 Interval estimators

9.7 Confidence intervals for a mean

9.8 Two-sided 95% confidence intervals for a mean

9.9 One-sided 95% confidence intervals for a mean

9.10 Confidence intervals with an arbitrary confidence level

9.11 Confidence intervals for unknown σ

9.12 Statistical tolerance limits for a normal population

9.13 Confidence intervals for a variance

9.14 Sample size determination: σ known

9.15 Sample size determination: σ unknown

Chapter 10 Inference

10.1 What to look for in Chapter 10

10.2 Testing statistical hypotheses: setting the stage

10.3 Terminology

10.4 Null and alternative hypotheses: examples

10.5 Consequences of hypothesis testing

10.6 Guilty until found innocent

10.7 Finally

Chapter 11 Goodness-of-fit tests

11.1 What to look for in Chapter 11

11.2 Testing goodness-of-fit

11.3 Chi-square test for discrete distributions

11.4 Chi-square test: sample-size considerations

11.5 Chi-square test for normality: known parameters

11.6 Chi-square test for normality: unknown parameters

11.7 Empirical cumulative distribution function

11.8 Kolmogorov-Smirnov goodness-of-fit test

11.9 Shapiro-Wilk (W-) test for normality

11.10 D’Agostino (D’) test for normality

__Week 3 Quiz 50 points__WEEK 4: 7 Feb to 13 Feb

Chapter 12 Contingency Tables

12.1 What to look for in Chapter 12

12.2 Contingency tables

12.3 Structure of contingency tables

12.4 Independence

12.5 Testing independence

12.6 Special case: 2.~2 contingency tables

12.7 Fisher’s exact probability test

12.8 Simpson’s paradox—better watch out!

12.9 A contingency table look-alike: McNemar’s test statistic

Chapter 13 Tests of statistical hypotheses: One mean

13.1 What to look for in Chapter 13

13.2 A test of the mean: σ known

13.3 A one-sided test: A different view

13.4 Power of a test of hypothesis

13.5 Operating characteristic curve

13.6 More power to you

13.7 Testing a mean when σ is unknown

13.8 Hypotheses with two-sided alternatives

13.9 Required sample size to test a mean: σ known

13.10 Required sample size to test a mean: σ unknown

Chapter 14 Tests for statistical hypotheses: Variances

14.1 What to look for in Chapter 14

14.2 Why worry about variances?

14.3 Testing a single variance

14.4 Testing equality of two variances

14.5 Testing homoscedasticity with samples of equal size

14.6 Pooling variances

14.7 Testing equality of variances with unequal sample sizes

Chapter 15 Tests for statistical hypotheses: Two means

15.1 What to look for in Chapter 15

15.2 Hypotheses about the means of two populations

15.3 Procedure 1: Paired observations

15.4 Procedure 2: Variances known

15.5 Procedure 3: Variances unknown but assumed equal

15.6 Procedure 4: Variances unknown and unequal

15.7 An example to summarize the four procedures

15.8 Required sample size

MIDTERM EXAM (500 points)

WEEK 5: 14 Feb to 20 Feb

Chapter 16 One-way ANOVA

16.1 What to look for in Chapter 16

16.2 One-way ANOVA: Data structure

16.3 Descriptive statistics

16.4 Model and assumptions

16.5 Partition of the total sum of squares

16.6 The one-way ANOVA table

16.7 Constructing an ANOVA Table with Excel

16.8 Duncan’s multiple range test

16.9 T-test and the ANOVA equivalence

Chapter 17 Two-way ANOVA

17.1 What to look for in Chapter 17

17.2 Two-way factorial designs

17.3 Randomized complete block design

17.4 Data structure and model: No replication

17.5 ANOVA for a two-way factorial design without replication

17.6 Excel calculations for the ANOVA table: No replication

17.7 Balanced two-way factorial designs with replication

17.8 Other multi-factor designs

Chapter 18 Regression

18.1 What to look for in Chapter 18

18.2 Concepts and terms from algebra and geometry

18.3 The concept of regression

18.4 Regression models

18.5 Simple linear regression

18.6 Fitting a line to data

18.7 The method of least squares and the regression line

18.8 Geometric interpretation of regression components

18.9 Partition of the total sum of squares

18.10 Regression ANOVA for a single independent variable

18.11 Using Excel to construct the regression line

18.12 Using Excel for regression analysis

18.13 Hypothesis testing and confidence intervals for the slope

18.14 Hypothesis testing and confidence intervals for the intercept

18.15 Regression through the origin

18.16 Multiple linear regression

18.17 Prediction

__Week 5 Quiz 50 points__WEEK 6: 21 Feb to 27 Feb

Chapter 19 Simple linear correlation

19.1 What to look for in Chapter 19

19.2 Basics of Simple linear correlation

19.3 The correlation coefficient

19.4 Excel’s routines for calculating correlation

19.5 Testing the correlation coefficient

19.6 Confidence interval for the correlation coefficient

19.7 Testing equality of two correlation coefficients

19.8 Comparison of regression and correlation analyses

Chapter 20 Bayesian probability inference

20.1 What to look for in Chapter 20

20.2 Motivation for Bayesian inference

20.3 Bayesian inference

20.4 Bayesian parameter estimation

20.5 Conjugate distributions

20.6 Non-informative prior distributions

20.7 Non-conjugate prior distributions

20.8 Bayesian hypothesis testing

20.9 Bayes factors

20.10 Consistency of the prior with the observed data

Chapter 21 Hypergeometric experiments

21.1 What to look for in Chapter 21

21.2 Basics of the hypergeometric distribution

21.3 Estimates of the proportion and number of successes

21.4 Tests of hypotheses

21.5 Sample size considerations

21.6 Normal approximation to the hypergeometric distribution

Chapter 22 Binomial experiments

22.1 What to look for in Chapter 22

22.2 Prerequisites for a binomial experiment

22.3 Binomial probabilities

22.4 Examples of binomial experiments

22.5 Mean and variance of a binomial variable

22.6 Normal approximation to the binomial

22.7 Confidence interval for proportion: Normal approximation applies

22.8 Confidence interval for proportion: Normal approximation does not apply

22.9 Confidence interval for a sample with no defects

22.10 Sample size for political polling

22.11 Binomial approximation to hyper-geometric distribution

__Week 6 Quiz 50 points__WEEK 7: 28 Feb to 5 Mar

Chapter 23 Poisson experiments

23.1 What to look for in Chapter 23

23.2 Prerequisites for a Poisson experiment

23.3 Poisson probabilities

23.4 Applications

23.5 Parameter testing and confidence intervals

23.6 Approximations to the binomial distribution

23.7 The normal approximation to the Poisson distribution

Chapter 24 Quality assurance

24.1 What to look for in Chapter 24

24.2 The concept of quality assurance

24.3 Process control: Building in quality

24.4 Control charts for means

24.5 Run test for control charts

24.6 Control charts for variability

24.7 Control charts for attributes

24.8 Acceptance sampling: Verifying quality

24.9 The A/Q criterion: Rules of the game

24.10 Probability calculations for the 95/95 criterion

24.11 What’s wrong with this picture?

Chapter 25 Nonparametric statistics

25.1 What to look for in Chapter 25

25.2 Nonparametric methods

25.3 Test of randomness: The runs test

25.4 Test for location: The sign test

25.5 Test for location: Wilcoxon signed ranks test

25.6 Test of locations with two matched samples: Sign test

25.7 Test of locations with two samples: Wilcoxon matched pairs test

25.8 Test of locations; two independent samples: Wilcoxon rank sum test

25.9 Test of locations with several samples: The median test

25.10 Test of locations with several samples: The Kruskal-Wallis test

25.11 Test of location for related samples: The rank ANOVA test

25.12 Test of location for related samples: The Friedman test

25.13 Test of variances with two samples: Squared ranks test

25.14 Test of variances with several samples: The k-sample squared ranks test

25.15 Spearman’s test of independence for two populations

__Week 7 Quiz 50 points__WEEK 8: 6 Mar to 12 Mar

Chapter 26 Outliers

26.1 What to look for in Chapter 26

26.2 What is an outlier?

26.3 Box plot procedure for outlier identification

26.4 Dixon’s procedure for outlier identification

26.5 Grubbs’ tests for outliers

26.6 Considerations in outlier rejection

Chapter 27 Simulation

27.1 What to look for in Chapter 27

27.2 Introduction to simulation

27.3 Generation of random numbers

27.4 Estimation of definite integrals

27.5 Generation of normal variates

27.6 Generation of arbitrary variates

27.7 Confirmation of the central limit theorem

FINAL EXAM: 6 Mar to 12 Mar 500 points (Lockdown Browser and Monitor required.)

A. | Course Title: | Statistics for Energy Industry |

B. | Course Number: | INDT 213G - 10364 |

C. | Semester: | Spring 2017 |

D. | Days/Time: | Online |

E. | Credit Hours: | 3 |

F. | Instructor: | Abitz, Michael |

G. | Office: | none |

H. | Email Address: | mabitz@nmjc.edu |

I. | Office Phone: | none |

J. | Office Hours: | 9 AM to 9 PM CDT/CST (Call me any day via cell phone number posted on your Home Page) |

K. | Time Zone: | Mountain Time |

L. | Prerequisite(s): | None |

M. | Corequisite(s): | None |

N. | Class Location: | Virtual |