NEW MEXICO JUNIOR COLLEGE

MISSION STATEMENT

Statistics for Energy Industry

SYLLABUS

  1. GENERAL COURSE INFORMATION
  2. 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
  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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:

  8. DEPARTMENTAL STUDENT LEARNING OUTCOMES

    The objective of this course is to help students understand basic computer applications, data collection and analysis.

  9. 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


  10. 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

    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.

  11. 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

  12. 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.

    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.

  13. ACADEMIC CALENDAR
  14. FINALS SCHEDULE
  15. 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.)