Statistics for Energy Industry


  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:
    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

    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.


    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.



    Text provided by Instructor.

    U.S. NRC Applying Statistics
    NUREG-1475, Revision 1
    Date Published: March 2011


    You can buy your books online at the NMJC Bookstore.


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    4. Keep up with readings and assignments.
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    6. Utilize your professor’s office hours and e-mail.
    7. Read the text.
    8. Adhere to the deadlines posted in the course outline.


    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:


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


    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


    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

    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



    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


    Students will be held responsible for the information on these pages.

    Academic Honesty
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    Online Learning Environment
    By participating in an online class, you undertake responsibility for your own progress and time management.

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    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, 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.



    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.)