UNM Stat 145 special: Statistics for Research (S4R)
Spring 2019
Syllabus is below timetable.
Spring 2019 schedule
TR 15301645, CTLB 330, Stat 145.014, CRN 30479
Goal
Our goal is to increase the number and diversity of students exposed to meaningful and empowering data analysis experiences and to inspire the pursuit of advanced datadriven experiences and opportunities for everyone! Learn to produce beautiful (markdown) and reproducible (knitr) reports with informative plots (ggplot2) and tables (kable) by writing code (R, tidyverse, Rstudio) to answer questions using fundamental statistical methods (all one and twovariable methods), which you’ll be proud to present (poster).
News
Information about the coming week will appear here if necessary; usually there won’t be any.
Precourse todos
Did you receive a registration error for Spring 2019? Send me an email with the following answers:
1. What registration error did you get (copy/paste is best)?
2. What is your UNM ID?
3. What is your Math/Stat background (that is, do you have the prereqs)?
If you are waitlisted, as long as there are seats available I will override you into the course. Don’t worry.
Step 0: Before our first class (Tue 1/15) please read through the following actions and install the required software on your computer and complete the brief surveys. If you don’t have a computer, there are classroom computers which will be available only when the classroom is open.
 Install R and RStudio:
 R for programming
 Rstudio Desktop for better R experience
 Installers at bottom, choose Windows or Mac OSX.
 Videos that may be helpful for installation:
 Install R on Mac (2 min).
 Install R for Windows (3 min).
 Install R and RStudio on Windows (5 min).
 Install R packages (copy/paste CODE into console and press [Enter]; this may take 2030 minutes), also update all packages within RStudio.
 Install Zotero or Mendeley (recommended for your own laptop) for bibliography management.
Course content
Course book and videos
Book: Passion Driven Statistics
Passion Driven Statistics (PDS) data
I encourage you to use one of the AddHealth datasets or NESARC. Use AddHealth W1 if you want to understand adolescents when they were young and AddHealth W4 if you want to understand adult relationships. NESARC is also interesting for substance abuse issues.
Data available in the PDS package with the command: library(PDS).
 AddHealthW1 Sampling Design, Codebook, RData. Adolescents when they were young, unique ID “AID”.
 AddHealthW4 Sampling Design, Codebook, RData. Same adolescents when they were older, unique ID “aid”.
 NESARC Sampling Design, Codebook, RData. Alcohol abuse and related conditions, unique ID “IDNUM”.
OutlookOnLife Sampling Design, Codebook, RData.Interesting data, but not enough quantitative variables to use, unique ID “CASEID”.GapMinder Sampling Design, Codebook, RData.Country data, but it’s complicated to interpret the average of large and small countries, unique ID “country”. Additional data sources
Weekly structure (also see Assessment below)
 Preclass (Tuesday): Reading, Video, Quiz (due before class — two attempts, higher score used, and solutions become available Tue 3:30pm after the quiz is due)
 Inclass: Activities in class Tuesday and Thursday. Tuesday’s assignment is due by Thursday 3:30pm of the same week, submitted to UNM Learn (evaluated by TA within 1 week). Often finished in class.
 Postclass (Thursday): Thursday’s assignment will be left to complete as Homework (due following Thursday by 3:30pm). Occasionally, finished in class, usually not.
 UNM Learn for quizzes and submitting inclass and homework assignments.
Office hours
 Erik: M/T 13:0014:00, and by appointment in SMLC 312
 Kelli: M 1112, W 1012 in SMLC 306
 Leah: M/W 34 in SMLC 319
Timetable
WkDate  Cl  Topic  Reading, Video, Quiz Week’s Preparation 
Inclass Worksheet, Homework, Data 

0001/15  00  Install software, survey  Step 0 – software install
Complete the Learning Studio Opinio presemester survey required for classroom assessment. Dierker Presurvey (sent by email end of first week) 

0101/15  01  Intro, RStudio and RMarkdown, poster 


0101/17  02  Rmd, codebook 


0201/22  03  Datasets, Codebooks 

PDS Data WS Codebook ADA1_HW_01_PersonalCodebook.Rmd 01 Personal codebook Rmd html Choose from PDS datasets 
0201/24  04  Personal codebook 

WS Citations Ch 04 ZoteroRMarkdown\References.Rmd Ch 04 ZoteroRMDexample\KCV.Rmd and KCV.bib Inclass: Rmd html Turn in one citation to a research question.02 Literature review Rmd html bib (While we won’t be doing a research proposal as part of this class, if we were covering more on research methods, then we might continue with a short research proposal (Rmd html).) 
0301/29  05  Literature Review 

ADA1_WS_03_ResearchQuestions.Rmd Rmd html Turn in one question of variable association. (UNM Google Scholar)Inclass: Rmd html Look at datasets in R, create subset of data, rename variables, numerical summaries. 
0301/31  06  Research Questions 
video: 
Citation ADA1_HW_02_LiteratureReview.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.html Ch 04 LiteratureReview\LiteratureReview.Rmd and LiteratureReview.bib Ch 04 LiteratureReview\AnotherExample\AnotherExample.Rmd and references MultipleReferences\*.bib Rmd html Turn in one citation to a research question.02 Literature review Rmd html bibInclass: Rmd html Univariate plots of numerical and categorical variables.03 Data subset, univariate summaries and plots Rmd html (See the link above the table “Erik’s NESARC data, nicotine and depression”.) 
0402/05  07  Writing About Empirical Research  Read: PDS Ch 5 Writing About Empirical ResearchVideo: noneQuiz: PDS Quiz 05 Writing About Empirical ResearchOptional Read: RepResBook Ch 04 RepResBook Ch 05read: PDS Ch 9, Ch 00 R, Ch 11 R; video: 111; quiz: quiz 
Research Plan Rmd html. Ch 05 Assignment ResearchProposal\ResearchProposal.Rmd and ResearchProposal.bib Ch 05 Assignment ResearchProposal\GradingRubric.Rmd ADA1_HW_022_ResearchProposal.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.htmlInclass: Rmd html Complete at least one bivariate coding relationship. 
0402/07  08  Research Plan  continued
Inclass: Rmd html 

0502/12  09  Working With Data, Data Management  Read: PDS Ch 6 Working with Data PDS Ch 7 Data ManagementVideo: PDS Video: 04. Working with Data PDS Video: 05. Data ManagementQuiz: PDS Quiz 06 Working With Data PDS Quiz 07 Data ManagementOptional Read: RepResBook Ch 06read: Ch 8.4, 8.2 R; video: 081 corr/log, 083 LS reg eq; quiz: quiz 
For 2 weeks: Data managment Ch 07: DataManagement Addhealth example DataManagementAssignment\DataManagementExample.Rmd Addhealth example, a little more DataManagementAssignment\DataManagementStuff.Rmd NESARC all parts DataManagementAssignment\DataManagementTemplate.Rmd NESARC some parts, different from Template DataManagementAssignment\NESARCcommands.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.html ADA1_WS_05_DataSubset_NumSum.Rmd ADA1_HW_03_DataSubset_UniNumSumPlot.Rmd ADA1 DataManagementExample_AddHealth.Rmd ADA1 DataManagementStuff_AddHealth_Gapminder.RmdThe Data Management Assignment is Challenging and will require many hours of your time—so please start now — not next week when it is due. Directions: Select three secondary categorical variables from the data set you are using. Recode your data to create factors with appropriate labels. Create a barplot for each of your factors, and write a sentence or two explaining each barplot. Show all R code used to manage your data and used to create your barplots with R code chunks in your *.Rmd file. Submit the resulting *.html file. Rmd html Look at datasets in R, create subset of data, rename variables, numerical summaries.Inclass: Rmd html dat Build intuition using SLR App, interpret properties of linear regression fit. 
0502/14  10  Subsetting data and R Programming  video: ADA1 Data subsetting 
03 Data subset, univariate summaries and plots Rmd html (See the link above the table “Erik’s NESARC data, nicotine and depression”.)Inclass: Rmd html dat Plot, transform, plot, and interpret.05 Rmd html 
0602/19  11  continued  Optional Read: RepResBook Ch 07read: Ch 8.1, 8.3.1 R, Ch 7.5.1 only sections on “conditional probability” and the following example R; video: 081 corr/log, 082 corr hyp test, 074 two prop & cond prob; quiz: quiz 
continued
Inclass: Rmd html 
0602/21  12  continued 
quiz 06b, Guess Ages (for next inclass) 
continued
Inclass: Rmd html d1 
0702/26  13  Graphing Univariate  Read: PDS Ch 8 Graphing: One Variable at a TimeVideo: PDS Video: 06. Graphing: One Variable at a TimeQuiz: PDS Quiz 08a Frequency Tables PDS Quiz 08b Graphing VariablesOptional Read: RepResBook Ch 08 
Univariate graphing ADA1_WS_06_PlottingUnivariate.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.html Ch 08 Categorical Graphs\CategoricalGraphs.Rmd Ch 08 Categorical Graphs\Graphs.Rmd Ch 08 Categorical Graphs\SH.Rmd Ch 8.3 Mean and Var calculations Statistics\MeanVarianceRV.RmdRmd html Univariate plots of numerical and categorical variables.Inclass: Rmd html Guess Ages, Legos. (Legos part 2 Rmd html dat, diagram).BBC Radio 4: More or Less, “sampling” 9 min audio 
0702/28  14  continued  continued
Inclass: Rmd html 

0803/05  15  Graphing Bivariate  Read: PDS Ch 9 Graphing RelationshipsVideo: PDS Video: 07. Graphing RelationshipsQuiz: PDS Quiz 09 Graphing RelationshipsOptional Read: RepResBook Ch 09 
Ch 09 Bivariate Graphs Graphs\BivariateMultivariateGraphs.Rmd Ch 09 Create numerical variable, plot ReadingData\Exercise.Rmd ADA1_WS_07_PlottingBivariate.Rmd ADA1_HW_04_BivariatePlot_DataCleaning.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.htmlRmd html Complete at least one bivariate coding relationship.Inclass: Rmd html one and twosample tests using data we collected in class. 
0803/07  16  continued  continued  
0903/12  Spring Break  
0903/14  Spring Break  
1003/19  17  Hypothesis Testing  Read: PDS Ch 10 Hypothesis TestingVideo: PDS Video: 08. Hypothesis Testing Ch 10 Sampling distributions Statistics\SamplingDistributions.Rmd Ch 10 Project template Statistics\StatisticsTemplate.RmdQuiz: PDS Quiz 10 Hypothesis TestingOptional Read: RepResBook Ch 10 
Ch 10 fake data example Statistics\HypothesesTesting.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.html ADA1_WS_15_HypTest_OneTwoSam.Rmd ADA1_HW_08_Inference_HypTestOneTwoSam.RmdRmd html one and twosample tests using data we collected in class.Inclass: Rmd html NP onesample tests and CIs, and ANOVA with pairwise comparisons. 
1003/21  18  Rmd html Hypothesis testing (one and twosample)
Inclass: Rmd html dat 

1103/26  19  ANOVA  Read: PDS Ch 11 Analysis of VarianceVideo: PDS Video: 09. Analysis of Variance Ch 11 Video Statistics\ANOVA.RmdQuiz: PDS Quiz 11 ANOVAOptional Read: RepResBook Ch 11 
Ch 11 ANOVA example Practice\Practice1.Rmd ADA1_WS_17_ANOVA_PairwiseComparisons.Rmd ADA1_HW_09_ANOVA_Assumptions.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.htmlRmd html ANOVA, model assumptions, and paired comparisons.Inclass: Rmd html dat Popular kids. 
1103/28  20  continued
Inclass: Rmd html 

1204/02  21  Contingency tables  Read: PDS Ch 12 ChiSquare Test of IndependenceVideo: PDS Video: 10. ChiSquare Test of Independence Ch 12 Video Statistics\ChiSquare.RmdQuiz: PDS Quiz 12 Chi SquareOptional Read: RepResBook Ch 12 
ADA1_WS_12_CategoricalTables.Rmd ADA1_WS_20_TwowayCatTables.Rmd ADA1_HW_11_TwowayCat_SLR.Rmd ADA1_HW_06_Corr_CatTab.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.htmlRmd html dat Popular kids.06 Rmd htmlInclass: Rmd html AddHealth W4 Pregnancy.Summary of Methods we’ve covered 
1204/04  22  continued
Inclass: Rmd html 

1304/09  23  Correlation and Interactions  Read: PDS Ch 13 Correlation Coefficient PDS Ch 14 ModerationVideo: PDS Video: 11. Correlation PDS Video: 12. Moderation Ch 14 Moderation\Moderation.RmdQuiz: PDS Quiz 13 Correlation PDS Quiz 14 Exploring ModerationOptional Read: RepResBook Ch 13 
ADA1_WS_10_LogTransform.Rmd ADA1_WS_11_Correlation.Rmd ADA1_HW_06_Corr_CatTab.Rmd Ch 13: Correlation\Correlation.Rmd Rubric STT2810ClassRepoghpages\CoursePacing\GenericPacing.htmlRmd html Data collection (hand span and word memory), correlation, regression to the mean. Spurious Correlations 06 Rmd htmlInclass: Rmd html Key statistical principles, ethics.With additional time, clarify which research questions you’ll present in your poster with a peer mentor. (Null results are ok!)Statistics is about communication, including writing and presenting. 
1304/11  24  Continued Work on posterCh 18 S4R_Content\assess\poster\*.pptx for poster template — need to modify Work on posterInclass: Rmd html Work on designing poster content at the bottom of your HW document.13 Rmd htmlWork on your poster content.Try to complete your poster planning in your HW document. 

1404/16  25  Linear Regression  Read: PDS Ch 15 Linear Regression: Summarizing the Pattern of the Data with a Line PDS Ch 17 Confounding and Multivariate ModelsVideo: PDS Video: 13. The Question of Causation PDS Video: 14. Multivariate Models and Confounding Ch 15 Reg and Logistic Reg plotting Regression\Regression.RmdQuiz: PDS Quiz 15 Regression PDS Quiz 17 ConfoundingOptional Read: RepResBook Ch 14 
Ch 15 Reg example Practice\Practice1.Rmd Ch 15 Leverage examples Statistics\LevInf.Rmd ADA1_WS_09_LinearRegression.Rmd ADA1_HW_05_SLR_Log.Rmd ADA1_WS_21_SimpleLinearRegression.RmdRmd html Regression of height vs hand span using data from our class. 11 Rmd html Rmd html dat Build intuition using SLR App, interpret properties of linear regression fit.poster template pdf, Rnw, sty, bib, logoProf Erhardt’s example poster pdf, Rnw 
1404/18  26  Continued Work on poster 

1504/23  27  Sampling and Designing Studies, Poster Presentation  Read: PDS Ch 16 Sampling and Designing Studies PDS Ch 18 Poster PresentationVideo: PDS Video: 15. Writing Your Poster PresentationQuiz: none 
ADA1_WS_23_ExpObs_DescribeStudy.Rmd
ADA1_HW_13_PosterCompleteInHWDoc.Rmd ADA1_WS_24_StatisticalCommunication.Rmd Work on poster Rmd html Work on designing poster content at the bottom of your HW document.
Work on poster Inclass: Course evaluations, submit receipt (capture screen image) as inclass assignment. See email for more details. Due next Wednesday 12/7. Complete and submit your poster in LaTeX pdf format. Transition from Markdown to LaTeX 
1504/25  28  Work on poster  
1604/30  29  Complete the Learning Studio Opinio postsemester survey required for classroom assessment.
Survey Dierker Post 
Work on poster
ARI Graphix 

1605/02  30  POSTERS  Poster sessions in SMLC Atrium  Poster presentation
Poster Schedule (be on time): Congratulations on a great semester! 
1705/06  Finals week  No final!  
(I reserve the right to continue to improve the materials throughout the semester.)
Syllabus
Description: Techniques for the visual presentation of numerical data, descriptive statistics, introduction to probability and basic probability models used in statistics, introduction to sampling and statistical inference, illustrated by examples from a variety of fields. In this special Statistics for Research (S4R) version, we will emphasize the skills of data analysis, visualization, and communication for undergraduate research.
Prerequisite: See UNM catalog
Semesters offered: Spring 2019
Lecture: Spring 2019 schedule
TR 15301645, CTLB 330, Stat 145.014, CRN 30479
Location: CTLB 330 (building 55, northeast of Zimmerman) Video
Office hours: Mon/Tue 13:0014:00, and by appointment in SMLC 312
email: “Erik B. Erhardt” <erike@stat.unm.edu>, please include “S4R” with a descriptive subject line, such as “S4R Homework 02 plot”
Textbook: Required custom book is available for free on this webpage: Passion Driven Statistics.
Laptops running R: I encourage you to bring a laptop to class each day so you can try the R programming exercises in class. If you don’t have one, no problem, there are some laptops in class and teamwork is encouraged — sit next to someone friendly who likes to share.
Saving data: If you’re using classroom computers, use Flashdrives or UNM’s OneDrive (available in LoboMail) for saving files. The CTLB computers do not connect to your standard UNM drive space.
Teaching Assistants and Peer Mentors
Stat grad students TAs
Kelli Kasper <kkasper@unm.edu>, office hours M 1112, W 1012 in SMLC 306.
Peer Mentors, SEP
Leah Puglisi <lhpuglisi@unm.edu>, former student, office hours M W 34 in SMLC 319.
Student learning outcomes
 Students will learn to use a reproducible research workflow.
 Students will improve their technology expertise.
 Students will learn to work with large data sets.
 Students will learn to create and present graphs for both univariate and multivariate data.
 Students will learn how to construct and test hypotheses.
Meeting the learning outcomes
You will acquire new information in this class, but the emphasis is comprehending, integrating, and applying information. Rote factual memorization is the lowest form of learning. Effective learning takes place by explaining, integrating, applying, and analyzing facts, hypotheses, and theories.
Learning in this class occurs by:
 Doing – completion of exercises that require analysis of data to answer questions and test hypotheses, or researching answers to reading assignments.
 Discussion – interaction with classmates to assemble and synthesize information you’d utilizing the collective skills and knowledge base of the group.
 Listening, acting, and reflecting – activities during class time provide insights into information not available in readings and includes review difficult material to aid comprehension. Note taking permits later reflection on lecture content. Listening to the professor lecture is the least effective learning tool for both students, however, and you should plan on coming to every class prepared to participate in active and reflective learning opportunities.
Assessment
 Quizzes are due each Tuesday before class. Purpose: to assess reading and video comprehension and assure you’re prepared to actively participate in class activities with minimal lecture. (There are 17, 20% of final grade, the lowest 2 are dropped.) Most weeks plan for 1 hour reading and video with a 20 minute quiz. Quizzes are not timed, they can be taken twice, and the higher of the two scores is used for grade calculation.
 Viewing quiz solutions after the due date in UNM Learn is not intuitive. Click on the “Begin” button (this is the nonintuitive part, since you are not actually beginning the quiz), then click “View All Attempts” to see the scores. Finally, click “Calculated Grade” to see the feedback for each question of the quiz.
 Inclass assignments are assigned each Tuesday and due before the Thursday class at 3:30pm, submitted to UNM Learn. Purpose: to struggle and find success in class with the concepts and skills. (About 12, includes class participation, 20% of final grade, the lowest 2 are dropped.) Most weeks plan to finish in class.
 Homework (HW) assignments are assigned each Thursday and due the following Thursday, submitted to UNM Learn. Purpose: to apply concepts and skills to your class poster project. (About 12, 40% of final grade, the lowest few are dropped.) Most weeks plan on 14 hours per assignment with a substantial start in class.
 Poster will be developed through semester (most HW assignment contribute to poster), the last couple weeks we’ll complete them, and the last week we’ll have poster presentations. Purpose: to have an overarching set of questions to answer using methods learned in the course, with a deliverable you can be proud of! (1 poster and presentation, 12% poster, 2% presentation, and 2% evaluations of others of final grade.) In the last couple weeks, assembling this poster may take 510 hours using a template provided to you.
 Course surveys are due at the beginning and end of the course. Purpose: to participate in national projectbased learning projects and improve the course. (About 4, 4% of final grade.)
All assignments in this class are electronic and submitted to UNM Learn for grading.
Late assignments will not be accepted.
All R code for the assignment should be included with the part of the problem it addresses (for code and output use a fixedwidth font, such as Courier); this will happen automatically by using RMarkdown.
Do NOT use your R code and output as your answer to the problem, but include them to show me how you arrived at your answer. Your prose solution should be provided to interpret the output. Output without explanation will not be given credit.
Collaboration and citation
For homeworks I encourage you to work together. Please discuss the data, code, and problems with one another, but do your own exploration and write up. We expect everyone to hand in substantially different homeworks, and we will enforce this under the honor code. The small benefit you might get from plagiarism is not worth the severe penalty (of lost trust, being reported to the dean, no points for the assignment, etc.).
As in life, please use any resources available to you. Projects and some homeworks will explicitly encourage you to use resources on the internet, but showing extra initiative will always be appreciated. You may find R programming tough at first, so feel free discuss your problems with other classmates or meet with or email questions to the TAs or me. Meeting in person is often much more productive than questions by email. If emailing, include your Rmd file and any required files (such as your .bib file) and a description of what you’re trying to do and where your error or trouble is.
I encourage you to use the ideas of others, but make them your own, giving credit. For projects have a formal bibliography, for homework cite casually, and for code simply copy the URL in as a comment (which is doubly helpful to you for finding the resource again).
Absences policy
I will follow the UNM absences policy with two unexcused absences. This means I can drop you from the class if you have a third absence; this paragraph serves as your warning. No one wants that, but I have found that I need to take attendance in freshman courses otherwise this policy is abused. If we all respect ourselves and each other then we won’t need attendance sheets and you’ll all achieve more.
Statements
Disability statement
If you have a documented disability that will impact your work in this class, please contact me to discuss your needs. You’ll also need to register with the Accessibility Resource Center in 2021 Mesa Vista Hall (building 56) across the courtyard east from the SUB.
Title IX statement
In an effort to meet obligations under Title IX, UNM faculty, Teaching Assistants, and Graduate Assistants are considered “responsible employees” by the Department of Education (see pg 15). This designation requires that any report of gender discrimination which includes sexual harassment, sexual misconduct and sexual violence made to a faculty member, TA, or GA must be reported to the Title IX Coordinator at the Office of Equal Opportunity. For more information on the campus policy regarding sexual misconduct.
Citizenship and/or Immigration Status
All students are welcome in this class regardless of citizenship, residency, or immigration status. Your professor will respect your privacy if you choose to disclose your status. As for all students in the class, family emergencyrelated absences are normally excused with reasonable notice to the professor, as noted in the attendance guidelines above. UNM as an institution has made a core commitment to the success of all our students, including members of our undocumented community. The Administration’s welcome is found on the UNM website: http://undocumented.unm.edu/.
Our Classroom
We’re doing this because:
 We want you to be empowered with statistics.
 We believe everyone should get out of this course with awesome skills
 Realtime feedback promotes efficient learning
“It encourages me to engage actively with the course material and take responsibility for my learning.”
GAISE Connections
Our six recommendations include the following:
 Teach statistical thinking.
 Teach statistics as an investigative process of problemsolving and decisionmaking.
 Give students experience with multivariable thinking.
 Focus on conceptual understanding.
 Integrate real data with a context and purpose.
 Foster active learning.
 Use technology to explore concepts and analyze data.
 Use assessments to improve and evaluate student learning.
Learning without thought is labor lost.
What I hear, I forget.
What I see, I remember.
What I do, I understand.
– Confucius
Archive
Saving data: If you’re using classroom computers, use Flashdrives or UNM’s OneDrive (available in LoboMail) for saving files. The CTLB computers do not connect to your standard UNM drive space. I recommend using a very systematic folder structure, such as S4R/HW, S4R/Class, S4R/Reading, S4R/Poster, etc. Do not just work on files in your downloads folder or your desktop; respect your data and code!
Unicode compile problems: If you knit to pdf you may get this error: “! Package inputenc Error: Unicode char”. ASCII is a small character set what we use to program in, Unicode is an extended character set that looks pretty (for example “straight quotes” become “curly quotes”) but causes code to break. You get unwanted Unicode when you copy/paste from a pdf or some other source into your code. To fix this, you have to find the Unicode and replace it with it’s ASCII equivalent. To do this: CtrlF to find, search for “[^\x00\x7F]” (without quotes), select “Regex” for regular expressions, and find the “Next” one. As it finds instances, replace the characters manually until there are no more. These characters will typically be curly quotes or fancy dashes.