Presented by Sam Clifford at the 2018 UseR conference, Brisbane, Australia. The talk describes the design of SEB113 - Quantitative Methods in Science, a first year statistics/mathematics unit in the Bachelor of Science at Queensland University of Technology. The unit uses RStudio and the tidyverse packages to give students the skills to do meaningful data manipulation and analysis without relying on prior knowledge of advanced mathematics.
NaĆÆve Bayes Machine Learning Classification with R Programming: A case study ...SubmissionResearchpa
Ā
This analytical review paper clearly explains NaĆÆve Bayes machine learning techniques for simple probabilistic classification based on bayes theorem with the assumption of independence between the characteristics using r programming. Although there is large gap between which algorithm is suitable for data analysis when there was large categorical variable to be predict the value in research data. The model is trained in the training data set to make predictions on the test data sets for the implementation of the NaĆÆve Bayes classification. The uniqueness of the technique is that gets new information and tries to make a better forecast by considering the new evidence when the input variable is of largely categorical in nature that is quite similar to how our human mind works while selecting proper judgement from various alternative of choices and can be applied in the neuronal network of the human brain does using r programming. Here researcher takes binary.csv data sets of 400 observations of 4 dependent attributes of educational data sets. Admit is dependent variable of gre, score gpa and rank of previous grade which ultimately determine whether student will be admitted or not for next program. Initially the gra and gpa variables has 0.36 percent significant in the association with rank categorical variable. The box plot and density plot demonstrate the data overlap between admitted and not admitted data sets. The naĆÆve Bayes classification model classify the large data with 0.68 percent for not admitted where as 0.31 percent were admitted. The confusion matrix, and the prediction were calculated with 0.68 percent accuracy when 95 percent confidence interval. Similarly, the training accuracy is increased from 29 percent to 32 percent when naĆÆve Bayes algorithm method as use kernel is equal to TRUE that ultimately decrease misclassification errors in the binary data sets. Yagyanath Rimal. (2019). NaĆÆve Bayes Machine Learning Classification with R Programming: A case study of binary data sets .Ā International Journal on Orange Technologies,Ā 1(2), 27-34. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/358 Pdf Url: https://journals.researchparks.org/index.php/IJOT/article/view/358/347 Paper Url: https://journals.researchparks.org/index.php/IJOT/article/view/358
Linear Programming Problems with Icosikaipentagonal Fuzzy Numberijtsrd
Ā
The objective of this paper is to introduce a new fuzzy number with twenty five points called as Icosikaipentagonal fuzzy number. In which Fuzzy numbers develop a membership function where there are no limitations of any specified form. The aim of this paper is to define Icosikaipentagonal fuzzy number and its some arithmetic operations. Fuzzy Linear Programming problem is one of the active research areas in optimization. Many real world problems are modelled as Fuzzy Linear Programming Problems. Icosikaipentagonal fuzzy number proposed a ranking function to solve fuzzy linear programming problems. Dr. S. Paulraj | G. Tamilarasi "Linear Programming Problems with Icosikaipentagonal Fuzzy Number" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31357.pdf Paper Url :https://www.ijtsrd.com/mathemetics/applied-mathamatics/31357/linear-programming-problems-with-icosikaipentagonal-fuzzy-number/dr-s-paulraj
For more course tutorials visit
www.newtonhelp.com
Curve-fitting Project - Linear Regression Model
A. Summary For this assignment you will be collecting data which exhibits a relatively linear trend, finding the line of best fit, plotting the data and the line, interpreting the slope, and using the linear equation to make
NaĆÆve Bayes Machine Learning Classification with R Programming: A case study ...SubmissionResearchpa
Ā
This analytical review paper clearly explains NaĆÆve Bayes machine learning techniques for simple probabilistic classification based on bayes theorem with the assumption of independence between the characteristics using r programming. Although there is large gap between which algorithm is suitable for data analysis when there was large categorical variable to be predict the value in research data. The model is trained in the training data set to make predictions on the test data sets for the implementation of the NaĆÆve Bayes classification. The uniqueness of the technique is that gets new information and tries to make a better forecast by considering the new evidence when the input variable is of largely categorical in nature that is quite similar to how our human mind works while selecting proper judgement from various alternative of choices and can be applied in the neuronal network of the human brain does using r programming. Here researcher takes binary.csv data sets of 400 observations of 4 dependent attributes of educational data sets. Admit is dependent variable of gre, score gpa and rank of previous grade which ultimately determine whether student will be admitted or not for next program. Initially the gra and gpa variables has 0.36 percent significant in the association with rank categorical variable. The box plot and density plot demonstrate the data overlap between admitted and not admitted data sets. The naĆÆve Bayes classification model classify the large data with 0.68 percent for not admitted where as 0.31 percent were admitted. The confusion matrix, and the prediction were calculated with 0.68 percent accuracy when 95 percent confidence interval. Similarly, the training accuracy is increased from 29 percent to 32 percent when naĆÆve Bayes algorithm method as use kernel is equal to TRUE that ultimately decrease misclassification errors in the binary data sets. Yagyanath Rimal. (2019). NaĆÆve Bayes Machine Learning Classification with R Programming: A case study of binary data sets .Ā International Journal on Orange Technologies,Ā 1(2), 27-34. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/358 Pdf Url: https://journals.researchparks.org/index.php/IJOT/article/view/358/347 Paper Url: https://journals.researchparks.org/index.php/IJOT/article/view/358
Linear Programming Problems with Icosikaipentagonal Fuzzy Numberijtsrd
Ā
The objective of this paper is to introduce a new fuzzy number with twenty five points called as Icosikaipentagonal fuzzy number. In which Fuzzy numbers develop a membership function where there are no limitations of any specified form. The aim of this paper is to define Icosikaipentagonal fuzzy number and its some arithmetic operations. Fuzzy Linear Programming problem is one of the active research areas in optimization. Many real world problems are modelled as Fuzzy Linear Programming Problems. Icosikaipentagonal fuzzy number proposed a ranking function to solve fuzzy linear programming problems. Dr. S. Paulraj | G. Tamilarasi "Linear Programming Problems with Icosikaipentagonal Fuzzy Number" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31357.pdf Paper Url :https://www.ijtsrd.com/mathemetics/applied-mathamatics/31357/linear-programming-problems-with-icosikaipentagonal-fuzzy-number/dr-s-paulraj
For more course tutorials visit
www.newtonhelp.com
Curve-fitting Project - Linear Regression Model
A. Summary For this assignment you will be collecting data which exhibits a relatively linear trend, finding the line of best fit, plotting the data and the line, interpreting the slope, and using the linear equation to make
2016. 7. 27 Presentation (Co-presenter: Jia Li)
Research Method for Political Science III (Instructor: Yuki Yanai)
Graduate School of Law, Kobe University, Japan
Some characters in this slide are invisible. High-resolution slide is available on my homepage (http://www.jaysong.net).
Intuition ā Based Teaching Mathematics for EngineersIDES Editor
Ā
It is suggested to teach Mathematics for engineers
based on development of mathematical intuition, thus, combining
conceptual and operational approaches. It is proposed to teach
main mathematical concepts based on discussion of carefully
selected case studies following solving of algorithmically generated
problems to help mastering appropriate mathematical tools.
The former component helps development of mathematical intuition;
the latter applies means of adaptive instructional technology
to improvement of operational skills. Proposed approach is applied
to teaching uniform convergence and to knowledge generation
using Computer Science object-oriented methodology.
Principal Component Analysis and ClusteringUsha Vijay
Ā
Identifying the borrower segments from the give bank data set which has 27000 rows and 77 variable using PROC PRINCOMP. variables, it is important to reduce the data set to a smaller set of variables to derive a feasible
conclusion. With the effect of multicollinearity two or more variables can share the same plane in the in dimensions. Each row of the data can
be envisioned as a 77 dimensional graph and when we project the data as orthonormal, it is expected that the certain characteristics of the
data based on the plots to cluster together as principal components. In order to identify these principal components. PROC PRINCOMP is
executed with all the variables except the constant variables(recoveries and collection fees) and we derive a plot of Eigen values of all the
principal components
Grouping and Displaying Data to Convey Meaning: Tables & Graphs chapter_2 _fr...Prashant Borkar
Ā
This presentation is about Grouping and Displaying Data to Convey Meaning: Tables and Graphs
Contents were taken from Statistics for Management by Levin & Rubin.
Presentation includes,
How can we Arrange Data?
Raw Data
Arranging Data using Data Array & Frequency Distribution
Constructing a Frequency Distribution
Graphing Frequency Distributions
It also covers some solved examples of it.
Decision Tree Algorithm Implementation Using Educational Data ijcax
Ā
There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.
2016. 7. 27 Presentation (Co-presenter: Jia Li)
Research Method for Political Science III (Instructor: Yuki Yanai)
Graduate School of Law, Kobe University, Japan
Some characters in this slide are invisible. High-resolution slide is available on my homepage (http://www.jaysong.net).
Intuition ā Based Teaching Mathematics for EngineersIDES Editor
Ā
It is suggested to teach Mathematics for engineers
based on development of mathematical intuition, thus, combining
conceptual and operational approaches. It is proposed to teach
main mathematical concepts based on discussion of carefully
selected case studies following solving of algorithmically generated
problems to help mastering appropriate mathematical tools.
The former component helps development of mathematical intuition;
the latter applies means of adaptive instructional technology
to improvement of operational skills. Proposed approach is applied
to teaching uniform convergence and to knowledge generation
using Computer Science object-oriented methodology.
Principal Component Analysis and ClusteringUsha Vijay
Ā
Identifying the borrower segments from the give bank data set which has 27000 rows and 77 variable using PROC PRINCOMP. variables, it is important to reduce the data set to a smaller set of variables to derive a feasible
conclusion. With the effect of multicollinearity two or more variables can share the same plane in the in dimensions. Each row of the data can
be envisioned as a 77 dimensional graph and when we project the data as orthonormal, it is expected that the certain characteristics of the
data based on the plots to cluster together as principal components. In order to identify these principal components. PROC PRINCOMP is
executed with all the variables except the constant variables(recoveries and collection fees) and we derive a plot of Eigen values of all the
principal components
Grouping and Displaying Data to Convey Meaning: Tables & Graphs chapter_2 _fr...Prashant Borkar
Ā
This presentation is about Grouping and Displaying Data to Convey Meaning: Tables and Graphs
Contents were taken from Statistics for Management by Levin & Rubin.
Presentation includes,
How can we Arrange Data?
Raw Data
Arranging Data using Data Array & Frequency Distribution
Constructing a Frequency Distribution
Graphing Frequency Distributions
It also covers some solved examples of it.
Decision Tree Algorithm Implementation Using Educational Data ijcax
Ā
There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.
The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the explain operator, whose goal is to provide an answer to the user asking "why does a measure show these values?". Specifically, we propose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the polynomials that best approximate the relationship between a measure and the other cube measures, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency.
An alternative learning experience in transition level mathematicsDann Mallet
Ā
QUT Mathematical Sciences Seminar series, November 1 2013
Traditionally at QUT, mathematics and statistics are taught using a face-to-face lecture/tutorial model involving large lecture classes for around 1/2 to 3/4 of the time and smaller group tutorials for the remainder of the time. This is also one of the main models for teaching at other campus-based institutions. Recently, in response to (learning) technology advances and changes in the ways learners seek education, QUT has made a significant commitment to a āDigital Transformationā project across the university. In this seminar I will present a technical overview, with some demonstrations, of a pilot project that seeks to investigate how digital transformation might work in a QUT mathematics or statistics subject. In particular, I will discuss the use of tablet PC technology and specialist software to produce video learning packages. This approach has been trialled in a transition level mathematics unit this semester. I will also cover integration of these learning packages with QUTs Learning Management System āBlackboardā. This seminar is a technical preview to another talk I will give early in the new year that will look at the impact of the altered learning experience on student outcomes, feedback and the unit itself.
A data science observatory based on RAMP - rapid analytics and model prototypingAkin Osman Kazakci
Ā
RAMP approach to analytics: Rapid Analytics and Model Prototyping; collaborative data challenges with in-built data science process management tools and analytics; An observatory of data science and scientists. Presented at the Design Theory Special Interest Group of International Design Society. Mines ParisTech and Centre for Data Science.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
Ā
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate
between algorithms with statistical implementation provides better consequence in terms of accurate
prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical
models, which provide less manual calculations. Presage is the essence of data science and machine
learning requisitions that impart control over situations. Implementation of any dogmas require proper
feature extraction which helps in the proper model building that assist in precision. This paper is
predominantly based on different statistical analysis which includes correlation significance and proper
categorical data distribution using feature engineering technique that unravel accuracy of different models
of machine learning algorithms.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...IJCSES Journal
Ā
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.
Similar to Classes without Dependencies - UseR 2018 (20)
How to Make a Field invisible in Odoo 17Celine George
Ā
It is possible to hide or invisible some fields in odoo. Commonly using āinvisibleā attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
Ā
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Operation āBlue Starā is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Ā
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Ā
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Ā
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
1. Classes without dependencies
Teaching the tidyverse to ļ¬rst year science students
Sam Clifford, Iwona Czaplinski, Brett Fyļ¬eld, Sama Low-Choy, Belinda
Spratt, Amy Stringer, Nicholas Tierney
2018-07-12
2. The student bodyās got a bad preparation
SEB113 a core unit in QUTās 2013 redesign of Bachelor of Science
Introduce key math/stats concepts needed for ļ¬rst year science
OP 13 cutoff (ATAR 65)
Assumed knowledge: Intermediate Mathematics
Some calculus and statistics
Not formally required
Diagnostic test and weekly prep material
Basis for further study in disciplines (explicit or embedded)
Still needs to be a self-contained unit that teaches skills
3. What they need is adult education
Engaging students with use of maths/stats in science
Build good statistical habits from the start
Have students doing analysis
that is relevant to their needs
as quickly as possible
competently
with skills that can be built on
Introduction to programming
reproducibility
separating analysis from the raw data
ļ¬exibility beyond menus
correcting mistakes becomes easier
4. You go back to school
Bad old days
Manual calculation of test statistics
Reliance on statistical tables
Donāt want to replicate senior high school study
Reduce reliance on point and click software that only does
everything students need right now (Excel, Minitab)
Students donāt need to become R developers
Focus on functionality rather than directly controlling every element,
e.g. LATEXvs Word
5. Itās a bad situation
Initial course development was not tidy
New B Sc course brought forward
Grab bag of topics at request of science academics
Difļ¬cult to ļ¬nd tutors who could think outside ātraditionalā stat. ed.
very low student satisfaction initially
Rapid and radical redesign required
tidyverse an integrated suite focused on transforming data frames
Vectorisation > loops
RStudio > JGR > Rgui.exe
6. What you want is an adult education (Oh yeah!)
Compassion and support for learners
Problem- and model-based
Technology should support learning goals
Go further, quicker by not focussing on mechanical calculations
Workļ¬ow based on functions rather than element manipulation
Statistics is an integral part of science
Statistics isnāt about generating p values
see Cobb in Wasserstein and Lazar [2016]
7. Machines do the work so people have time to think ā IBM (1967)
All models are wrong, but some are useful ā Box (1987)
8. Now here we go dropping science, dropping it all over
Within context of scientiļ¬c method:
Aims
Methods and Materials
1. Get data/model into an analysis environment
2. Data munging
Results
3. Exploration of data/model
4. Compute model
5. Model diagnostics
Conclusion
6. Interpret meaning of results
9. I said you wanna be startinā somethinā
Redesign around ggplot2
ggplot2 introduced us to tidy data requirements
Redesign based on Year 11 summer camp
This approach not covered by textbooks at the time
Tried using JGR and Plot Builder for one semester
Extension to wider tidyverse
Replace unrelated packages/functions with uniļ¬ed approach
Focus on what you want rather than directly coding how to do it
Good effort-reward with limited expertise
10. Summer(ise) loving, had me a blast; summer(ise) loving,
happened so fast
R is a giant calculator that can operate on objects
ggplot() requires a data frame object
dplyr::summarise() to summarise a column variable
dplyr::group_by() to do summary according to speciļ¬ed
structure
Copy-paste or looping not guaranteed to be MECE
Group-level summary stats leads to potential statistical models
Easier, less error prone, than repeated usage of =AVERAGE()
11. We want the funk(tional programming paradigm)
Tidy data as observations of variables with structure [Wickham,
2014b]
R as functional programming [Wickham, 2014a]
Actions on entire objects to do things to data and return useful
information
Students enter understanding functions like y(x) = x2
function takes input
function returns output
e.g. mean(x) = i xi/n
Week 4: writing functions to solve calculus problems
magrittr::%>% too conceptually similar to ggplot2::+ for
novices to grasp in ļ¬rst course
12. Like Frankie sang, I did it my way
Whatās the mean gas mileage for each engine geometry and
transmission type for the 32 cars listed in 1974 Motor Trends
magazine?
Loops For each of the pre-computed number of
groups, subset, summarise and store how
you want
tapply() INDEX a list of k vectors, 1 summary
FUNction, returns k-dimensional array
dplyr specify grouping variables and which sum-
mary statistics, returns tidy data frame ready
for model/plot
13. Night of the living baseheads
Like all procedural languages, plot() has one giant list of
arguments
Focus is on how plot is drawn rather than what you want to plot
Inefļ¬ciency of keystrokes
re-stating the things being plotted
setting up plot axis limits
loop counters for small multiples, etc.
14. Toot toot, chugga chugga, big red car
Say we want to plot carsā fuel efļ¬ciency against weight
library(tidyverse)
data(mtcars)
mtcars <- mutate(
mtcars, l100km = 235.2146/mpg,
wt_T = wt/2.2046,
am = factor(am, levels = c(0,1),
labels=c("Auto", "Manual")),
vs = factor(vs, levels = c(0,1),
labels=c("V","S")))
plot(y=mtcars$l100km, x=mtcars$wt_T)
1.0 1.5 2.0 2.5
101520
mtcars$wt_T
mtcars$l100km
Fairly quick to say what
goes on x and y axes
More arguments ā better
graph
xlim, ylim
xlab, ylab
main
type, pch
What if we want to see how
it varies with
engine geometry
transmission type
16. One, two, princes kneel before you
Both approaches do the same thing
Idea base ggplot2
Plot variables Specify vectors Coordinate system de-
ļ¬ned by variables
Small multiples Loops, subsets, par facet_grid
Common axes Pre-computed Inherited from data
V/S A/M annotation Strings Inherited from data
Axis labels Per axis set For whole plot
Focus on putting things on the page vs representing variables
17. I got a grammar Hazel and a grammar Tilly
Plots are built from [Wickham, 2010]
data ā which variables are mapped to aesthetic elements
geometry ā how do we draw the data?
annotations ā what is the context of these shapes?
Build more complex plots by adding commands and layering elements,
rather than by stacking individual points and lines e.g.
make a scatter plot, THEN
add a trend line (with inherited x, y), THEN
facet by grouping variable, THEN
change axis information
18. When Iām good, Iām very good; but when Iām bad, Iām better
Want to make good plots as soon as possible
Learning about Tufteās principles [Tufte, 1983, Pantoliano, 2012]
Discuss what makes a plot good and bad
Seeing how ggplot2 code translates into graphical elements
Week 2 workshop has students making best and worst plots for a
data set, e.g.
19. Sie ist ein Model und sie sieht gut aus
Make use of broom package to get model summaries
Get data frames rather than summary.lm() text vomit
tidy()
parameter estimates
CIs
t test info [Greenland et al., 2016]
glance()
everything else
ggplot2::fortify()
regression diagnostic info instead of plot.lm()
stat_qq(aes(x=.stdresid)) for residual quantiles
geom_point(aes(x=.ļ¬tted, y=.resid)) for ļ¬tted vs
residuals
20. When you hear some feedback keep going take it higher
Positives
More conļ¬dence and students see use of maths/stats in science
Students enjoy group discussions in workshops
Some students continue using R over Excel in future units
Labs can be done online in own time
Negatives
Request for more face to face help rather than online
Labs can be done online in own time (but are they?)
Downloading of slides rather than attending/watching lectures
21. Things can only get better
Focus on what you want from R rather than how you do it
representing variables graphically
summarising over structure in data
tidiers for models
Statistics embedded in scientiļ¬c theory [Diggle and Chetwynd, 2011]
Problem-based learning
groups of novices
supervised by tutors
discussion of various approaches
22. Peter J. Diggle and Amanda G. Chetwynd. Statistics and Scientiļ¬c
Method: An Introduction for Students and Researchers. Oxford
University Press, 2011.
Sander Greenland, Stephen J. Senn, Kenneth J. Rothman, John B. Carlin,
Charles Poole, Steven N. Goodman, and Douglas G. Altman.
Statistical tests, p values, conļ¬dence intervals, and power: a guide to
misinterpretations. European Journal of Epidemiology, 31(4):337ā350,
apr 2016. URL https://doi.org/10.1007/s10654-016-0149-3.
Mike Pantoliano. Data visualization principles: Lessons from Tufte, 2012.
URL https:
//moz.com/blog/data-visualization-principles-lessons-from-tufte.
Edward Tufte. The Visual Display of Quantitative Information. Graphics
Press, 1983.
Ronald L. Wasserstein and Nicole A. Lazar. The ASA's statement on
p-values: Context, process, and purpose. The American Statistician, 70
(2):129ā133, Apr 2016. URL
https://doi.org/10.1080/00031305.2016.1154108.
23. H. Wickham. Advanced R. Chapman & Hall/CRC The R Series. Taylor &
Francis, 2014a. ISBN 9781466586963. URL
https://books.google.com.au/books?id=PFHFNAEACAAJ.
Hadley Wickham. A layered grammar of graphics. Journal of
Computational and Graphical Statistics, 19(1):3ā28, 2010. doi:
10.1198/jcgs.2009.07098.
Hadley Wickham. Tidy data. Journal of Statistical Software, 59(1):1ā23,
2014b. ISSN 1548-7660. URL
https://www.jstatsoft.org/index.php/jss/article/view/v059i10.