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Dark Parables
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Dark Parables
Genres
Adventure, puzzle, hidden object
Developers
Blue Tea Games
Eipix Entertainment
Publishers
Big Fish Games
Platforms
Windows, Mac OS X, iOS, Android (operating system)
First release
Dark Parables: Curse of Briar Rose
March 11, 2010
Latest release
Dark Parables: Requiem for the Forgotten Shadow
February 24, 2017
Dark Parables is a casual game series originally developed by Blue Tea Games, and by Eipix Entertainment from the eighth installment onwards. The games are distributed by Big Fish Games. They are available for PC, Mac OS, iOS and Android devices. The series features ‘Hidden Object’ scenes where, in order to progress through a game, the player must find all the pieces that make up a certain item to be used as a 'cause and effect' in another area of the game. Every game in the series is based around a different fairy tale, with some overlap between games, there is also some divergence from the series' continuity in storylines (especially after Eipix Entertainment took up the franchise).
There have been thirteen games in the Dark Parables series to date.[1] In each installment the player plays as "The Fairytale Detective", a detective who is dispatched to exotic locations to investigate reports of disturbances of suspected/confirmed fairytale nature. Although in the games the player is simply addressed as "Detective", the appellation "The Fairytale Detective" is used for the first time in the third installment, and again in the sixth and tenth installment.
Contents
[hide]
1 History
2 Installments
2.1 Part 1 - Curse of Briar Rose
2.2 Part 2 - The Exiled Prince
2.3 Part 3 - Rise of the Snow Queen
2.4 Part 4 - The Red Riding Hood Sisters
2.5 Part 5 - The Final Cinderella
2.6 Part 6 - Jack and the Sky Kingdom
2.7 Part 7 - Ballad of Rapunzel
2.8 Part 8 - The Little Mermaid and the Purple Tide
2.9 Part 9 - The Queen of Sands
2.10 Part 10 - Goldilocks and the Fallen Star
2.11 Part 11 - The Swan Princess and the Dire Tree
2.12 Part 12 - The Thief and the Tinderbox
2.13 Part 13 - Requiem for the Forgotten Shadow
2.14 Part 14 - Return of the Salt Princess
3 Reception
4 References
History[edit]
Dark Parables was inspired by the amazing comic book series, Fables. Also at that time, the epitome of adventure hidden object game experience was Return to Ravenhearst. I wanted to merge the strong stories similar to Fables with the gameplay experience of RTR, while adding innovative features that makes our brand stand out. This is what became of Dark Parables.
“
”
the owner of Blue Tea Games, Steven Zhao[2]
Owner and creative director of Blue Tea Games, Steven Zhao, stated in an interview that inspirations for Dark Parables were the comic book series Fables and casual game Mystery Case Files: Return to Ravenhearst.[2]
Installments[edit]
Part 1 - Curse of Briar Rose[edit]
Dark Parables: Curse of Briar Rose is the first installment in the Dark Parables franchise and was released on March 11, 2010. Set in Edinburgh, Scotland, the player takes on the role of The Detective to investigate and discover the true tale of the real Sleeping Beauty, and save her from the disembodied spirit of the evil sorceress who originally cursed her, and who now needs a new body and has her sights set on Briar Rose's.[3] After Briar Rose is reawakened, she tells you of the "fabled Frog Prince, who still walks amongst us to this day", setting up events for the following game, The Exiled Prince.
The bonus gameplay in this game is titled "The Spinning Room" and serves as a further teaser for the following game, The Exiled Prince, by introducing the fact that Princess Briar Rose had a sister named Princess Ivy, (who, in The Exiled Prince, is James the Frog Prince's first and one true love).
Part 2 - The Exiled Prince[edit]
Dark Parables: The Exiled Prince is the second installment in the Dark Parables franchise and was released on February 25, 2011. Set in Black Forest, Germany, the player as The Detective is sent to investigate the tale of The Frog Prince after the disappearance of the chancellor's daughter. It is revealed the fabled prince is still alive, cursed with Immortality, and now roams the deserted path in the forest, capturing unsuspecting visitors trying to find a solution to his curse.[4]
After rescuing the chancellor's daughter, she told the detective about the appearance of the alleged 'white lady', which is actually the Snow Queen in the next installment Rise of the Snow Queen. In connection to this, the bonus gameplay in this game is titled "The Frozen Lair" and was a teaser for the following game, Rise of the Snow Queen.
Part 3 - Rise of the Snow Queen[edit]
Dark Parables: Rise of the Snow Queen,the third installment in the series, was released on December 2, 2011. Set in the Bernese Alps, Switzerland, the player journeys into the mythical Snowfall Kingdom and investigate the disappearance of several children. The legendary Snow Queen has risen and has been terrorizing nearby villages with her supernatural powers in her search for the "Golden Child". The player must rescue the missing children and put a stop to the fabled queen's plan.[5]
The bonus gameplay in this game is based on the tale "Hansel and Gretel", revealing the origin of "the Golden Child" seen in Rise of the Snow Queen. The player is Hansel as he rescues his sister from a witch and releases a goddess from captivity.
Part 4 - The Red Riding Hood Sisters[edit]
Dark Parables: The Red Riding Hood Sisters is the fourth installment in the Dark Parables franchise and was released on September 21, 2012. Set in the forests of the Vosges Mountains, Eastern France, the player as the detective returns to defeat the fabled Wolf Queen.[6]
The bonus gameplay in this game is based on the fable "The Boy Who Cried Wolf", telling the backstory of how the Mist Kingdom became overrun by the Mist Wolves. The player is 'The Boy Who Cried Wolf', as he seeks to save his father from the Greedy King of the Mist Kingdom and rescue a mermaid from captivity.
Part 5 - The Final Cinderella[edit]
Dark Parables: The Final Cinderella is the fifth installment in the Dark Parables franchise, and was released on May 23, 2013. Set in the Matese mountains of southern Italy. For decades, a mysterious witch, known as "Godmother", has been hunting maidens, in search of the legendary "Cinderella"; a pure-hearted maiden. Play as the fabled detective, venture into the mirror world to rescue this Final Cinderella alongside Pinocchio, from the evil Godmother's clutches, and stop her evil plans to steal the girl's pure soul!
The bonus gameplay is called "The Oriental Cinderella". It tells the story of a Chinese Princess, Shan Mao, a "Cinderella", who was turned into a nine-tailed fox by the Spider Witch, who is in fact her evil stepsister, Princess Chi. You, the player, play as the Prince, (whose marriage proposal Princess Shan Mao has accepted), as he goes on a quest to save Shan, with the help of Godmother (before she turned evil, showing how she met Geppetto), from Princess Chi.
After the end credits, it reveals that the next installment will center around "Jack and the Beanstalk".
Part 6 - Jack and the Sky Kingdom[edit]
Dark Parables: Jack and the Sky Kingdom is the sixth installment in the Dark Parables series released on February 6, 2014. Accompanied by a legendary and infamous Robin Hood-esq fortune hunter called Jack, the Fairytale Detective ventures up to the Sky Kingdom, (a mythical floating kingdom), after chunks of it have rained down upon a town in Alblasserwaard, South Holland, and discover the history behind the chaos and stop the Sky Kingdom King and Princes' wreaking havoc and destruction. The bonus gameplay is called "Rumpelstiltskin and the Queen". It tells the story of how the young Queen of the Sky Kingdom, (the miller's daughter from "Rumpelstiltskin") rescues her infant daughter from Rumpelstiltskin.
Part 7 - Ballad of Rapunzel[edit]
Dark Parables: Ballad of Rapunzel is the seventh installment in the Dark Parables series, released on June 26, 2014. As revealed on the game's beta demo, the events of the seventh installment are set at "the ruins of the ancient Floralia kingdom, (named for the kingdom's patron, the Roman goddess, Flora) on this mountain," Mount Sněžka, Czech Republic, in Central Europe, and the game is set 10-years after the events of 'Rise of the Snow Queen'. Along with Gerda and Prince Ross Red (Snow White's estranged fraternal twin brother), the player explores the cause of the waves of deadly pollen that is building to spread across the land.
The Bonus Chapter gameplay is called "The Thumbelina Curse". Following the events of The Ballad of Rapunzel, the player play as Gerda, the Golden Child (descendant of Hansel, the first Golden Child), as she seeks to locate Kai, who disappeared at the beginning of the Main game.
Part 8 - The Little Mermaid and the Purple Tide[edit]
Dark Parables: The Little Mermaid and the Purple Tide is the eighth installment in the Dark Parables series. It is the first sequel developed by Eipix Entertainment. The Collector’s Edition was released on November 13, 2014 for PC and Mac, with an iOS version released afterwards. The events of the eighth installment are set the Island of Crete, off of the coast of Greece, in the Mediterranean Sea. As the Detective, the player must investigate the "Legendary Sunken Kingdom of Prasino" (under the patronage of the ancient Greek Goddess, Thalassa), after reports of a Purple Tide approaching the coastline, killing all marine life in its path and flooding a coastal village.
The Bonus Chapter gameplay in this game is called "Trusty John and King Bluebeard".
Part 9 - The Queen of Sands[edit]
Dark Parables: The Queen of Sands is the ninth installment of the Dark Parables series, once again developed by Eipix Entertainment. The Collector’s Edition was released on May 21, 2015 for PC and Mac, with iOS and Android versions to be released at a later date.
In the (fictional) town of Montafleur, provincial France, the streets are overrun by a purple mist created by the "Queen of Sands", transforming all of the townsfolk exposed into nightmarish creatures.
The Bonus chapter is centred around the Parable of The Seven Ravens.
Part 10 - Goldilocks and the Fallen Star[edit]
Dark Parables: Goldilocks and the Fallen Star is the tenth game of Dark Parables franchise. The CE was released on November 19, 2015. The Kingdom of Barsia has been invaded by mechanical beasts that are attacking its citizens.
Part 11 - The Swan Princess and the Dire Tree[edit]
Dark Parables: The Swan Princess and The Dire Tree is the eleventh installment of the Dark Parables series.[7] It was released on July 7, 2016 as Collector's Edition version.
Part 12 - The Thief and the Tinderbox[edit]
The 12th game of this series was released on September 22, 2016 as a Collector's Edition version. The player investigates a rogue arsonist who is setting the Mountain Kingdom's forests on fire.
Part 13 - Requiem for the Forgotten Shadow[edit]
The 13th game of this series.The BETA edition was released on November 17, 2016. On February 24th, 2017, the Collector's Edition was released on the web.
Part 14 - Return of the Salt Princess[edit]
The 14th game of this series.The BETA game was released in September 2017. This episode will be developed by Blue Tea Games who return to the series since 2014.
Reception[edit]
This section is empty. You can help by adding to it. (October 2015)
References[edit]
This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (January 2015) (Learn how and when to remove this template message)
Jump up ^ "Big Fish Games presents the Blue Tea Games collection". Big Fish Games. Retrieved 2012-09-23.
^ Jump up to: a b "Series Spotlight: Dark Parables". Big Fish Games. 21 September 2012. Retrieved 2012-09-23.
Jump up ^ "Dark Parables: Curse of the Briar Rose Collector's Edition Game Download for PC". Big Fish Games. Retrieved 2012-09-23.
Jump up ^ "Dark Parables: The Exiled Prince Collector's Edition Game Download for PC". Big Fish Games. Retrieved 2012-09-23.
Jump up ^ "Dark Parables: Rise of the Snow Queen Collector's Edition Game Download for PC". Big Fish Games. Retrieved 2012-09-23.
Jump up ^ "Dark Parables: The Red Riding Hood Sisters Collector's Edition Game Download for PC". Big Fish Games. Retrieved 2012-09-23.
Jump up ^ "Dark Parables: The Swan Princess and The Dire Tree Collector's Edition". Big Fish Games. Retrieved 2016-08-27.
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Outlier
From Wikipedia, the free encyclopedia
Jump to: navigation, search
This article is about the statistical term. For other uses, see Outlier (disambiguation).
Figure 1. Box plot of data from the Michelson–Morley experiment displaying four outliers in the middle column, as well as one outlier in the first column.
In statistics, an outlier is an observation point that is distant from other observations.[1][2] An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set.[3] An outlier can cause serious problems in statistical analyses.
Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution. In the former case one wishes to discard them or use statistics that are robust to outliers, while in the latter case they indicate that the distribution has high skewness and that one should be very cautious in using tools or intuitions that assume a normal distribution. A frequent cause of outliers is a mixture of two distributions, which may be two distinct sub-populations, or may indicate 'correct trial' versus 'measurement error'; this is modeled by a mixture model.
In most larger samplings of data, some data points will be further away from the sample mean than what is deemed reasonable. This can be due to incidental systematic error or flaws in the theory that generated an assumed family of probability distributions, or it may be that some observations are far from the center of the data. Outlier points can therefore indicate faulty data, erroneous procedures, or areas where a certain theory might not be valid. However, in large samples, a small number of outliers is to be expected (and not due to any anomalous condition).
Outliers, being the most extreme observations, may include the sample maximum or sample minimum, or both, depending on whether they are extremely high or low. However, the sample maximum and minimum are not always outliers because they may not be unusually far from other observations.
Naive interpretation of statistics derived from data sets that include outliers may be misleading. For example, if one is calculating the average temperature of 10 objects in a room, and nine of them are between 20 and 25 degrees Celsius, but an oven is at 175 °C, the median of the data will be between 20 and 25 °C but the mean temperature will be between 35.5 and 40 °C. In this case, the median better reflects the temperature of a randomly sampled object (but not the temperature in the room) than the mean; naively interpreting the mean as "a typical sample", equivalent to the median, is incorrect. As illustrated in this case, outliers may indicate data points that belong to a different population than the rest of the sample set.
Estimators capable of coping with outliers are said to be robust: the median is a robust statistic of central tendency, while the mean is not.[4] However, the mean is generally more precise estimator.[5]
Contents
[hide]
1 Occurrence and causes
1.1 Causes
2 Detection
2.1 Peirce's criterion
2.2 Tukey's fences
2.3 In anomaly detection
2.4 Modified Thompson Tau test
3 Working with outliers
3.1 Retention
3.2 Exclusion
3.3 Non-normal distributions
3.4 Set-membership uncertainties
3.5 Alternative models
4 See also
5 References
6 External links
Occurrence and causes[edit]
Relative probabilities in a normal distribution
In the case of normally distributed data, the three sigma rule means that roughly 1 in 22 observations will differ by twice the standard deviation or more from the mean, and 1 in 370 will deviate by three times the standard deviation.[6] In a sample of 1000 observations, the presence of up to five observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number – see Poisson distribution – and not indicate an anomaly. If the sample size is only 100, however, just three such outliers are already reason for concern, being more than 11 times the expected number.
In general, if the nature of the population distribution is known a priori, it is possible to test if the number of outliers deviate significantly from what can be expected: for a given cutoff (so samples fall beyond the cutoff with probability p) of a given distribution, the number of outliers will follow a binomial distribution with parameter p, which can generally be well-approximated by the Poisson distribution with λ = pn. Thus if one takes a normal distribution with cutoff 3 standard deviations from the mean, p is approximately 0.3%, and thus for 1000 trials one can approximate the number of samples whose deviation exceeds 3 sigmas by a Poisson distribution with λ = 3.
Causes[edit]
Outliers can have many anomalous causes. A physical apparatus for taking measurements may have suffered a transient malfunction. There may have been an error in data transmission or transcription. Outliers arise due to changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. A sample may have been contaminated with elements from outside the population being examined. Alternatively, an outlier could be the result of a flaw in the assumed theory, calling for further investigation by the researcher. Additionally, the pathological appearance of outliers of a certain form appears in a variety of datasets, indicating that the causative mechanism for the data might differ at the extreme end (King effect).
Detection[edit]
There is no rigid mathematical definition of what constitutes an outlier; determining whether or not an observation is an outlier is ultimately a subjective exercise. There are various methods of outlier detection.[7][8][9][10] Some are graphical such as normal probability plots. Others are model-based. Box plots are a hybrid.
Model-based methods which are commonly used for identification assume that the data are from a normal distribution, and identify observations which are deemed "unlikely" based on mean and standard deviation:
Chauvenet's criterion
Grubbs' test for outliers
Dixon's Q test
ASTM E178 Standard Practice for Dealing With Outlying Observations
Mahalanobis distance and leverage are often used to detect outliers, especially in the development of linear regression models.
Subspace and correlation based techniques for high-dimensional numerical data[10]
Peirce's criterion[edit]
Main article: Peirce's criterion
It is proposed to determine in a series of m {\displaystyle m} observations the limit of error, beyond which all observations involving so great an error may be rejected, provided there are as many as n {\displaystyle n} such observations. The principle upon which it is proposed to solve this problem is, that the proposed observations should be rejected when the probability of the system of errors obtained by retaining them is less than that of the system of errors obtained by their rejection multiplied by the probability of making so many, and no more, abnormal observations. (Quoted in the editorial note on page 516 to Peirce (1982 edition) from A Manual of Astronomy 2:558 by Chauvenet.) [11][12][13][14]
Tukey's fences[edit]
Other methods flag observations based on measures such as the interquartile range. For example, if Q 1 {\displaystyle Q_{1}} and Q 3 {\displaystyle Q_{3}} are the lower and upper quartiles respectively, then one could define an outlier to be any observation outside the range:
[ Q 1 − k ( Q 3 − Q 1 ) , Q 3 + k ( Q 3 − Q 1 ) ] {\displaystyle {\big [}Q_{1}-k(Q_{3}-Q_{1}),Q_{3}+k(Q_{3}-Q_{1}){\big ]}}
for some nonnegative constant k {\displaystyle k} . John Tukey proposed this test, where k = 1.5 {\displaystyle k=1.5} indicates an "outlier", and k = 3 {\displaystyle k=3} indicates data that is "far out".[15]
In anomaly detection[edit]
Main article: Anomaly detection
In the data mining task of anomaly detection, other approaches are distance-based[16][17] and density-based such as Local Outlier Factor (LOF),[18] and most of them use the distance to the k-nearest neighbors to label observations as outliers or non-outliers.[19]
Modified Thompson Tau test[edit]
The modified Thompson Tau test[citation needed] is a method used to determine if an outlier exists in a data set. The strength of this method lies in the fact that it takes into account a data set’s standard deviation, average and provides a statistically determined rejection zone; thus providing an objective method to determine if a data point is an outlier. Note: Although intuitively appealing, this method appears to be unpublished (it is not described in Thompson (1985)[20]) and one should use it with caution.
How it works: First, a data set's average is determined. Next the absolute deviation between each data point and the average are determined. Thirdly, a rejection region is determined using the formula: Rejection Region = t α / 2 ( n − 1 ) n n − 2 + t α / 2 2 {\displaystyle {\text{Rejection Region}}{=}{\frac {{t_{\alpha /2}}{\left(n-1\right)}}{{\sqrt {n}}{\sqrt {n-2+{t_{\alpha /2}^{2}}}}}}} ; where t α / 2 {\displaystyle {t_{\alpha /2}}} is the critical value from the Student t distribution with n-2 degrees of freedom, n is the sample size, and s is the sample standard deviation. To determine if a value is an outlier: Calculate δ = |(X - mean(X)) / s|. If δ > Rejection Region, the data point is an outlier. If δ ≤ Rejection Region, the data point is not an outlier.
The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier). Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region. This process is continued until no outliers remain in a data set.
Some work has also examined outliers for nominal (or categorical) data. In the context of a set of examples (or instances) in a data set, instance hardness measures the probability that an instance will be misclassified ( 1 − p ( y | x ) {\displaystyle 1-p(y|x)} where y {\displaystyle y} is the assigned class label and x {\displaystyle x} represent the input attribute value for an instance in the training set t {\displaystyle t} ).[21] Ideally, instance hardness would be calculated by summing over the set of all possible hypotheses H {\displaystyle H} :
I H ( ⟨ x , y ⟩ ) = ∑ H ( 1 − p ( y , x , h ) ) p ( h | t ) = ∑ H p ( h | t ) − p ( y , x , h ) p ( h | t ) = 1 − ∑ H p ( y , x , h ) p ( h | t ) . {\displaystyle {\begin{aligned}IH(\langle x,y\rangle )&=\sum _{H}(1-p(y,x,h))p(h|t)\\&=\sum _{H}p(h|t)-p(y,x,h)p(h|t)\\&=1-\sum _{H}p(y,x,h)p(h|t).\end{aligned}}}
Practically, this formulation is unfeasible as H {\displaystyle H} is potentially or infinite and calculating p ( h | t ) {\displaystyle p(h|t)} is unknown for many algorithms. Thus, instance hardness can be approximated using a diverse subset L ⊂ H {\displaystyle L\subset H} :
I H L ( ⟨ x , y ⟩ ) = 1 − 1 | L | ∑ j = 1 | L | p ( y | x , g j ( t , α ) {\displaystyle IH_{L}(\langle x,y\rangle )=1-{\frac {1}{|L|}}\sum _{j=1}^{|L|}p(y|x,g_{j}(t,\alpha )}
where g j ( t , α ) {\displaystyle g_{j}(t,\alpha )} is the hypothesis induced by learning algorithm g j {\displaystyle g_{j}} trained on training set t {\displaystyle t} with hyperparameters α {\displaystyle \alpha } . Instance hardness provides a continuous value for determining if an instance is an outlier instance.
Working with outliers[edit]
The choice of how to deal with an outlier should depend on the cause. Some estimators are highly sensitive to outliers, notably estimation of covariance matrices.
Retention[edit]
Even when a normal distribution model is appropriate to the data being analyzed, outliers are expected for large sample sizes and should not automatically be discarded if that is the case. The application should use a classification algorithm that is robust to outliers to model data with naturally occurring outlier points.
Exclusion[edit]
Deletion of outlier data is a controversial practice frowned upon by many scientists and science instructors; while mathematical criteria provide an objective and quantitative method for data rejection, they do not make the practice more scientifically or methodologically sound, especially in small sets or where a normal distribution cannot be assumed. Rejection of outliers is more acceptable in areas of practice where the underlying model of the process being measured and the usual distribution of measurement error are confidently known. An outlier resulting from an instrument reading error may be excluded but it is desirable that the reading is at least verified.
The two common approaches to exclude outliers are truncation (or trimming) and Winsorising. Trimming discards the outliers whereas Winsorising replaces the outliers with the nearest "nonsuspect" data.[22] Exclusion can also be a consequence of the measurement process, such as when an experiment is not entirely capable of measuring such extreme values, resulting in censored data.[23]
In regression problems, an alternative approach may be to only exclude points which exhibit a large degree of influence on the estimated coefficients, using a measure such as Cook's distance.[24]
If a data point (or points) is excluded from the data analysis, this should be clearly stated on any subsequent report.
Non-normal distributions[edit]
The possibility should be considered that the underlying distribution of the data is not approximately normal, having "fat tails". For instance, when sampling from a Cauchy distribution,[25] the sample variance increases with the sample size, the sample mean fails to converge as the sample size increases, and outliers are expected at far larger rates than for a normal distribution. Even a slight difference in the fatness of the tails can make a large difference in the expected number of extreme values.
Set-membership uncertainties[edit]
A set membership approach considers that the uncertainty corresponding to the ith measurement of an unknown random vector x is represented by a set Xi (instead of a probability density function). If no outliers occur, x should belong to the intersection of all Xi's. When outliers occur, this intersection could be empty, and we should relax a small number of the sets Xi (as small as possible) in order to avoid any inconsistency.[26] This can be done using the notion of q-relaxed intersection. As illustrated by the figure, the q-relaxed intersection corresponds to the set of all x which belong to all sets except q of them. Sets Xi that do not intersect the q-relaxed intersection could be suspected to be outliers.
Figure 5. q-relaxed intersection of 6 sets for q=2 (red), q=3 (green), q= 4 (blue), q= 5 (yellow).
Alternative models[edit]
In cases where the cause of the outliers is known, it may be possible to incorporate this effect into the model structure, for example by using a hierarchical Bayes model, or a mixture model.[27][28]
See also[edit]
Anomaly (natural sciences)
Robust regression
Studentized residual
Data transformation (statistics)
Winsorizing
References[edit]
Jump up ^ Grubbs, F. E. (February 1969). "Procedures for detecting outlying observations in samples". Technometrics. 11 (1): 1–21. doi:10.1080/00401706.1969.10490657. An outlying observation, or "outlier," is one that appears to deviate markedly from other members of the sample in which it occurs.
Jump up ^ Maddala, G. S. (1992). "Outliers". Introduction to Econometrics (2nd ed.). New York: MacMillan. pp. 88–96 [p. 89]. ISBN 0-02-374545-2. An outlier is an observation that is far removed from the rest of the observations.
Jump up ^ Grubbs 1969, p. 1 stating "An outlying observation may be merely an extreme manifestation of the random variability inherent in the data. ... On the other hand, an outlying observation may be the result of gross deviation from prescribed experimental procedure or an error in calculating or recording the numerical value."
Jump up ^ Ripley, Brian D. 2004. Robust statistics Archived 2012-10-21 at the Wayback Machine.
Jump up ^ Chandan Mukherjee, Howard White, Marc Wuyts, 1998, "Econometrics and Data Analysis for Developing Countries Vol. 1" [1]
Jump up ^ Ruan, Da; Chen, Guoqing; Kerre, Etienne (2005). Wets, G., ed. Intelligent Data Mining: Techniques and Applications. Studies in Computational Intelligence Vol. 5. Springer. p. 318. ISBN 978-3-540-26256-5.
Jump up ^ Rousseeuw, P; Leroy, A. (1996), Robust Regression and Outlier Detection (3rd ed.), John Wiley & Sons
Jump up ^ Hodge, Victoria J.; Austin, Jim, A Survey of Outlier Detection Methodologies, doi:10.1023/B:AIRE.0000045502.10941.a9
Jump up ^ Barnett, Vic; Lewis, Toby (1994) [1978], Outliers in Statistical Data (3 ed.), Wiley, ISBN 0-471-93094-6
^ Jump up to: a b Zimek, A.; Schubert, E.; Kriegel, H.-P. (2012). "A survey on unsupervised outlier detection in high-dimensional numerical data". Statistical Analysis and Data Mining. 5 (5): 363–387. doi:10.1002/sam.11161.
Jump up ^ Benjamin Peirce, "Criterion for the Rejection of Doubtful Observations", Astronomical Journal II 45 (1852) and Errata to the original paper.
Jump up ^ Peirce, Benjamin (May 1877 – May 1878). "On Peirce's criterion". Proceedings of the American Academy of Arts and Sciences. 13: 348–351. doi:10.2307/25138498. JSTOR 25138498.
Jump up ^ Peirce, Charles Sanders (1873) [1870]. "Appendix No. 21. On the Theory of Errors of Observation". Report of the Superintendent of the United States Coast Survey Showing the Progress of the Survey During the Year 1870: 200–224. . NOAA PDF Eprint (goes to Report p. 200, PDF's p. 215).
Jump up ^ Peirce, Charles Sanders (1986) [1982]. "On the Theory of Errors of Observation". In Kloesel, Christian J. W.; et al. Writings of Charles S. Peirce: A Chronological Edition. Volume 3, 1872-1878. Bloomington, Indiana: Indiana University Press. pp. 140–160. ISBN 0-253-37201-1. – Appendix 21, according to the editorial note on page 515
Jump up ^ Tukey, John W (1977). Exploratory Data Analysis. Addison-Wesley. ISBN 0-201-07616-0. OCLC 3058187.
Jump up ^ Knorr, E. M.; Ng, R. T.; Tucakov, V. (2000). "Distance-based outliers: Algorithms and applications". The VLDB Journal the International Journal on Very Large Data Bases. 8 (3–4): 237. doi:10.1007/s007780050006.
Jump up ^ Ramaswamy, S.; Rastogi, R.; Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. Proceedings of the 2000 ACM SIGMOD international conference on Management of data - SIGMOD '00. p. 427. doi:10.1145/342009.335437. ISBN 1581132174.
Jump up ^ Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4.
Jump up ^ Schubert, E.; Zimek, A.; Kriegel, H. -P. (2012). "Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection". Data Mining and Knowledge Discovery. doi:10.1007/s10618-012-0300-z.
Jump up ^ Thompson .R. (1985). "A Note on Restricted Maximum Likelihood Estimation with an Alternative Outlier Model".Journal of the Royal Statistical Society. Series B (Methodological), Vol. 47, No. 1, pp. 53-55
Jump up ^ Smith, M.R.; Martinez, T.; Giraud-Carrier, C. (2014). "An Instance Level Analysis of Data Complexity". Machine Learning, 95(2): 225-256.
Jump up ^ Wike, Edward L. (2006). Data Analysis: A Statistical Primer for Psychology Students. pp. 24–25. ISBN 9780202365350.
Jump up ^ Dixon, W. J. (June 1960). "Simplified estimation from censored normal samples". The Annals of Mathematical Statistics. 31 (2): 385–391. doi:10.1214/aoms/1177705900.
Jump up ^ Cook, R. Dennis (Feb 1977). "Detection of Influential Observations in Linear Regression". Technometrics (American Statistical Association) 19 (1): 15–18.
Jump up ^ Weisstein, Eric W. Cauchy Distribution. From MathWorld--A Wolfram Web Resource
Jump up ^ Jaulin, L. (2010). "Probabilistic set-membership approach for robust regression" (PDF). Journal of Statistical Theory and Practice.
Jump up ^ Roberts, S. and Tarassenko, L.: 1995, A probabilistic resource allocating network for novelty detection. Neural Computation 6, 270–284.
Jump up ^ Bishop, C. M. (August 1994). "Novelty detection and Neural Network validation". Proceedings of the IEE Conference on Vision, Image and Signal Processing. 141 (4): 217–222. doi:10.1049/ip-vis:19941330
External links[edit]
Wikimedia Commons has media related to Outliers.
Renze, John. "Outlier". MathWorld.
Balakrishnan, N.; Childs, A. (2001) [1994], "Outlier", in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 978-1-55608-010-4
Grubbs test described by NIST manual
Authority control
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Baby, Please Don't Go
From Wikipedia, the free encyclopedia
Jump to: navigation, search
"Baby, Please Don't Go"
Single by Joe Williams' Washboard Blues Singers
B-side
"Wild Cow Blues"
Released
1935 (1935)
Format
10-inch 78 rpm
Recorded
Chicago, October 31, 1935
Genre
Blues
Length
3:22
Label
Bluebird
Songwriter(s)
Traditional (J. Williams credited on record)
Producer(s)
Lester Melrose
"Baby, Please Don't Go" is a blues song which has been called "one of the most played, arranged, and rearranged pieces in blues history" by music historian Gerard Herzhaft (fr).[1] Delta blues musician Big Joe Williams popularized the song with several versions beginning in 1935.
After World War II, Chicago blues and rhythm and blues artists adapted the song to newer music styles. In 1952, a doo-wop version by the Orioles reached the top ten on the race records chart. In 1953, Muddy Waters recorded the song as an electric Chicago-ensemble blues piece, which influenced many subsequent renditions. By the early 1950s, the song became a blues standard.
In the 1960s, "Baby, Please Don't Go" became a popular rock song after the Northern Irish group Them recorded it in 1964. Several music writers have identified Jimmy Page, a studio guitarist at the time, as participating in the recording, although his exact contributions are unclear. Subsequently, Them's uptempo rock arrangement also made it a rock standard. "Baby, Please Don't Go" has been inducted into both the Blues and Rock and Roll Halls of Fame.
Contents
[hide]
1 Background
2 Original song
3 Later blues and R&B recordings
4 Van Morrison and Them rendition
5 AC/DC version
6 Recordings by other artists
7 Recognition and legacy
8 Notes
Background[edit]
"Baby, Please Don't Go" is likely an adaptation of "Long John", an old folk theme which dates back to the time of slavery in the United States.[1] Blues researcher Paul Garon notes that the melody is based on "Alabamy Bound", composed by Tin Pan Alley writer Ray Henderson, with lyrics by Buddy DeSylva and Bud Green in 1925.[2][a] The song, a vaudeville show tune, inspired several other songs between 1925 and 1935, such as "Elder Greene Blues", "Alabama Bound", and "Don't You Leave Me Here".[2][3] These variants were recorded by Charlie Patton, Lead Belly, Monette Moore, Henry Thomas, and Tampa Red.[2]
Author Linda Dahl suggests a connection to a song with the same title by Mary Williams Johnson in the late 1920s and early 1930s.[4] However, Johnson, who was married to jazz-influenced blues guitarist Lonnie Johnson, never recorded it and her song is not discussed as influencing later performers.[1][3][5] Blues researcher Jim O'Neal notes that Williams "sometimes said that the song was written by his wife, singer Bessie Mae Smith (aka Blue Belle and St. Louis Bessie) [not the same as the popular Bessie Smith of the 1920s and 1930s]".[3]
Original song[edit]
Big Joe Williams used the imprisonment theme for his October 31, 1935, recording of "Baby, Please Don't Go". He recorded it during his first session for Lester Melrose and Bluebird Records in Chicago.[3] It is an ensemble piece with Williams on vocal and guitar accompanied by Dad Tracy on one-string fiddle and Chasey "Kokomo" Collins on washboard, who are listed as "Joe Williams' Washboard Blues Singers" on the single.[3] Musical notation for the song indicates a moderate-tempo fifteen-bar blues in 4
4 or common time in the key of B flat.[6][b] As with many Delta blues songs of the era, it remains on the tonic chord (I) throughout without the progression to the subdominant (IV) or dominant (V) chords.[6] The lyrics express a prisoner's anxiety about his lover leaving before he returns home:[8]
Now baby please don't go, now baby please don't go
Baby please don't go back to New Orleans, and get your cold ice cream
I believe there's a man done gone, I believe there's a man done gone
I believe there's a man done gone to the county farm, with a long chain on
The song became a hit and established Williams' recording career.[9] On December 12, 1941, he recorded a second version titled "Please Don't Go" in Chicago for Bluebird, with a more modern arrangement and lyrics.[10] Blues historian Gerard Herzhaft calls it "the most exciting version",[1] which Williams recorded using his trademark nine-string guitar. Accompanying him are Sonny Boy Williamson I on harmonica and Alfred Elkins on imitation bass (possibly a washtub bass).[11] Since both songs appeared before recording industry publications began tracking such releases, it is unknown which version was more popular. In 1947, he recorded it for Columbia Records with Williamson and Ransom Knowling on bass and Judge Riley on drums. This version did not reach the Billboard Race Records chart,[12] but represents a move toward a more urban blues treatment of the song.
Later blues and R&B recordings[edit]
Big Joe Williams various recordings inspired other blues musicians to record their interpretations of the song[13] and it became a blues standard.[1] Early examples include Papa Charlie McCoy as "Tampa Kid" (1936), Leonard "Baby Doo" Caston (1939), Lightnin' Hopkins (1947), John Lee Hooker (1949) and Big Bill Broonzy (1952).[14] By the early 1950s, the song was reworked in contemporary musical styles, with an early rhythm and blues/jump blues version by Billy Wright (1951),[1] a harmonized doo-wop version by the Orioles (a number eight R&B hit in 1952),[c] and a Afro-Cuban-influenced rendition by Rose Mitchell (1954).[1]
In 1953, Muddy Waters recast the song as a Chicago-blues ensemble piece with Little Walter and Jimmy Rogers.[15] Chess Records originally issued the single with the title "Turn the Lamp Down Low", although the song is also referred to as "Turn Your Lamp Down Low",[3] "Turn Your Light Down Low",[16] or "Baby Please Don't Go".[d] He regularly performed the song, several of which were recorded. Live versions appear on Muddy Waters at Newport 1960 and on Live at the Checkerboard Lounge, Chicago 1981 with members of the Rolling Stones.[17] AllMusic critic Bill Janovitz cites the influence of Waters' adaptation:
The most likely link between the Williams recordings and all the rock covers that came in the 1960s and 1970s would be the Muddy Waters 1953 Chess side, which retains the same swinging phrasing as the Williams takes, but the session musicians beef it up with a steady driving rhythm section, electrified instruments and Little Walter Jacobs wailing on blues harp.[18]
Van Morrison and Them rendition[edit]
"Baby, Please Don't Go"
Single by Them
B-side
"Gloria"
Released
November 6, 1964 (1964-11-06) (UK)
1965 (US)
Format
7-inch 45 rpm
Recorded
October 1964
Genre
Blues rock
Length
2:40
Label
Decca (UK)
Parrot (US)
Songwriter(s)
Traditional (Williams credited)
Producer(s)
Bert Burns
"Baby Please Don't Go" was one of the earliest songs recorded by Them, fronted by a 19-year-old Van Morrison. Their rendition of the song was derived from a John Lee Hooker version recorded in 1949 as "Don't Go Baby" using the pseudonym "Texas Slim" (King 4334),[19] which later appeared on a 1959 album, Highway of Blues, that Van Morrison heard and felt was "something really unique and different" with "more soul" than he'd previously heard.[19] Them recorded "Baby, Please Don't Go" for Decca Records in October 1964.
Besides Morrison, there is conflicting information about who participated in the session. In addition to the group's original members (guitarist Billy Harrison, bassist Alan Henderson, drummer Ronnie Millings, and keyboard player Eric Wrixon), others have been suggested: Pat McAuley on keyboards, Bobby Graham on a second drum kit, Jimmy Page on second guitar,[20] and Peter Bardens on keyboards.[21] As Page biographer George Case notes, "There is a dispute over whether it is Page's piercing blues line that defines the song, if he only played a run Harrison had already devised, or if Page only backed up Harrison himself".[22] Morrison has acknowledged Page's participation in the early sessions: "He played rhythm guitar on one thing and doubled a bass riff on the other"[23] and Morrison biographer Johnny Rogan notes that Page "doubled the distinctive riff already worked out by Billy Harrison".[23] Music critic Greil Marcus comments that during the song's quieter middle passage "the guitarist, session player Jimmy Page or not, seems to be feeling his way into another song, flipping half-riffs, high, random, distracted metal shavings".[24][e] Them's blues-rock arrangement is "now regarded justly as definitive", with "much of its appeal emanat[ing] from the tingling lead guitar section", according to music writer Adam Clayson.[26]
"Baby, Please Don't Go" was released as Them's second single on November 6, 1964.[20] With the B-side, "Gloria", it became their first hit, reaching number ten on the UK Singles Chart in February 1965.[27] The single was released in the U.S. in 1965, but only "Gloria" became a hit the following year.[28] The song was not included on Them's original British or American albums (The Angry Young Them and Them Again), however, it has appeared on several compilation albums, such as The Story of Them Featuring Van Morrison and The Best of Van Morrison.[29] When it was reissued in 1991 as a single in the UK (London LON 292), it reached number 65 in the chart.[27] Van Morrison also accompanied John Lee Hooker during a 1992 performance, where Hooker sings and plays "Baby, Please Don't Go" on guitar while sitting on a dock, with harmonica backing by Morrison; it was released on the 2004 Come See About Me Hooker DVD.[30]
AC/DC version[edit]
Angus Young and Bon Scott at the Ulster Hall in August 1979
"Baby, Please Don't Go" was a feature of AC/DC's live shows since their beginning.[31] Although they have expressed their interest and inspiration in early blues songs,[32] music writer Mick Wall identifies Them's adaptation of the song as the likely source.[33] In November 1974, Angus Young, Malcolm Young, and Bon Scott recorded it for their 1975 Australian debut album, High Voltage.[32] Tony Currenti is sometimes identified as the drummer for the song, although he suggests that it had been already recorded by Peter Clack.[34] Wall notes that producer George Young played bass for most of the album,[33] although Rob Bailey claims that many of the album's tracks were recorded with him.[35]
High Voltage and a single with "Baby, Please Don't Go" were released simultaneously in Australia in February 1975.[35][f] AllMusic critic Eduardo Rivadavia called the song "positively explosive".[36] Albert Productions issued it as the single's B-side. However, the A-side was largely ignored and "Baby, Please Don't Go" began receiving airplay.[33] The single entered the chart at the end of March 1975[37] and peaked at number 10 in April.[38] Also on 23 March 1975, one month after drummer Phil Rudd and bassist Mark Evans joined AC/DC, the group performed the song for the first time (this performance would also be repeated on 6 April and 27 April which is why there is often conflicting dates for this performance) on the Australian music program Countdown.[39][40] [18] For their appearance, "Angus wore his trade mark schoolboy uniform while Scott took the stage wearing a wig of blonde braids, a dress, make-up, and earrings", according to author Heather Miller.[40] Joe Bonomo describes Scott as "a demented Pippi Longstocking", and Perkins notes his "tattoos and a disturbingly short skirt."[32] Evans describes the reaction:
As soon as his vocals are about to begin he comes out from behind the drums dressed as a schoolgirl. And it was like a bomb went off in the joint; it was pandemonium, everybody broke out in laughter. [Scott] had a wonderful sense of humor.[40]
Scott mugs for the camera and, during the guitar solo/vocal improvization section, he lights a cigarette as he duels with Angus with a green mallet.[18] Rudd laughs throughout the performance.[18] Although "Baby, Please Don't Go" was a popular part of AC/DC's performances (often as the closing number), the song was not released internationally until their 1984 compilation EP '74 Jailbreak.[32] The video from the Countdown show is included on the Family Jewels DVD compilation in 2005.
Recordings by other artists[edit]
Aerosmith recorded "Baby, Please Don't Go" for their blues cover album, Honkin' on Bobo, which was released on March 30, 2004.[41] The album was produced by Jack Douglas, who had worked on the group's earlier albums, and reflects a return to their hard rock roots.[41] Billboard magazine describes the song as "the kind of straight-ahead, hard-driving track that always typified the band's [1970s] records".[42] Edna Gundersen of USA Today called their version a "terrific revival."[43] It was the first single to be released from the album and reached number seven on the Mainstream Rock Tracks chart.[44] A music video, directed by Mark Haefeli, was produced to promote the single.[45] Subsequently, the song has become a staple of the band's concert repertoire.[46][47]
Other recorded renditions include those by Mose Allison (Transfiguration of Hiram Brown, 1960),[48] Paul Revere & the Raiders (Just Like Us!, 1966),[49] the Amboy Dukes (reached number 106 on the Billboard Bubbling Under Hot 100 Singles record chart in 1968),[50] Beacon Street Union (The Clown Died in Marvin Gardens, 1968), Gary Glitter (Glitter, 1972), Budgie (Never Turn Your Back on a Friend, 1973)[51] and Renée Geyer (Swing, 2013).
Recognition and legacy[edit]
Big Joe Williams' "Baby, Please Don't Go" is included in the Rock and Roll Hall of Fame list of "500 Songs That Shaped Rock and Roll".[52] In 1992, it was inducted into the Blues Foundation Hall of Fame in the "Classics of Blues Recordings" category.[3] The Foundation noted that, in addition to various blues recordings, "the song was revived in revved-up fashion by rock bands in the '60s such as Them, the Amboy Dukes, and Ten Years After".[3]
Notes[edit]
Footnotes
Jump up ^ An earlier "I'm Alabama Bound", with its own recording history, was published by Robert Hoffman in 1909.
Jump up ^ The sheet music includes a 1944 copyright date, indicating a later version of the song[7] (Williams' 1935 recording is in the key of B).
Jump up ^ Music historian Larry Birbaum suggested that the Orioles' 1951 version inspired James Brown's first hit "Please, Please, Please" (1956).[5]
Jump up ^ Muddy Waters' original Chess single lists the songwriters as "Strutt, Alexander", although reissues credit "McKinley Morganfield" (his legal name). The song is registered as "Turn the Lamps [sic] Down Low" with Joseph Lee Williams as the songwriter. ISWC T-070.278.618-2.
Jump up ^ Beginning about 1:22 in Them's recording, bassy-sounding riffs appear.[25]
Jump up ^ The Albert Productions AC/DC single misidentified the songwriter as Big Bill Broonzy.[35]
Citations
^ Jump up to: a b c d e f g Herzhaft 1992, p. 437.
^ Jump up to: a b c Garon 2004, p. 39.
^ Jump up to: a b c d e f g h O'Neal, Jim (1992). "1992 Hall of Fame Inductees: "Baby Please Don't Go" – Big Joe Williams (Bluebird 1935)". The Blues Foundation. Retrieved April 26, 2015.
Jump up ^ Dahl 1984, p. 110.
^ Jump up to: a b Birnbaum 2012, p. 302.
^ Jump up to: a b Hal Leonard 1995, pp. 17–19.
Jump up ^ Hal Leonard 1995, p. 17.
Jump up ^ Gioia 2008, p. 130.
Jump up ^ Herzhaft 1992, p. 381.
Jump up ^ Demetre 1994, p. 23.
Jump up ^ Demetre 1994, p. 29.
Jump up ^ Whitburn 1988, pp. 444–445.
Jump up ^ Escott 2002, p. 54.
Jump up ^ Garon, p. 40.
Jump up ^ Palmer 1989, p. 28.
Jump up ^ Garon 2004, p. 40.
Jump up ^ Gordon 2002, p. 266.
^ Jump up to: a b c d Bonomo 2010, eBook.
^ Jump up to: a b Murray 2002, pp. 212, 302.
^ Jump up to: a b Thompson 2008, p. 303.
Jump up ^ Strong 2002, eBook.
Jump up ^ Case 2007, p. 35.
^ Jump up to: a b Rogan 2006, pp. 101, 111.
Jump up ^ Marcus 2010, eBook.
Jump up ^ Them (1964). Baby, Please Don't Go (Song recording). London: Decca Records. Event occurs at 1:22. F.12018.
Jump up ^ Clayson 2006, p. 61.
^ Jump up to: a b "Them – Singles". Official Charts. Retrieved April 26, 2015.
Jump up ^ "Them – Awards". AllMusic. Retrieved April 26, 2015.
Jump up ^ "Van Morrison/Them: Baby Please Don't Go – Appears On". AllMusic. Retrieved April 26, 2015.
Jump up ^ "John Lee Hooker: Come and See About Me – Overview". AllMusic. Retrieved April 26, 2015.
Jump up ^ Walker 2011, p. 135.
^ Jump up to: a b c d Perkins 2011, eBook.
^ Jump up to: a b c Wall 2013, eBook.
Jump up ^ Fink 2014, p. 83.
^ Jump up to: a b c Walker 2011, p. 139.
Jump up ^ Rivadavia, Eduardo. "AC/DC: High Voltage (Australia) – Album Review". AllMusic. Retrieved April 26, 2015.
Jump up ^ Walker 2011, p. 145.
Jump up ^ Walker 2011, p. 148.
Jump up ^ "Specials : Countdown". AC/DC-Bootlegs. Retrieved April 25, 2017.
^ Jump up to: a b c Miller 2009, eBook.
^ Jump up to: a b Erlewine, Stephen Thomas. "Aerosmith: Honkin' on Bobo – Album Review". AllMusic. Retrieved April 26, 2015.
Jump up ^ Billboard 2004, pp. 13, 15.
Jump up ^ Gundersen, Edna (March 29, 2004). "Clapton, Aerosmith dabble in the blues". USA Today. Gannett Company. Retrieved July 1, 2015.
Jump up ^ "Aerosmith: Honkin' on Bobo – Awards". AllMusic. Retrieved April 26, 2015.
Jump up ^ "Aerosmith: 'Baby Please Don't Go' Video Posted Online". Blabbermouth.net. May 20, 2004. Retrieved July 1, 2015.
Jump up ^ Hauk, Hunter (August 6, 2010). "Concert review: Aerosmith at Superpages.com Center". The Dallas Morning News. A.H. Belo. Retrieved July 1, 2015.
Jump up ^ Stingley, Mick (October 14, 2010). "Aerosmith/The J. Geils Band – Concert Review". The Hollywood Reporter. Prometheus Global Media. Retrieved July 1, 2015.
Jump up ^ Greenwald, Matthew. "Mose Allison: Baby Please Don't Go, Composed by Big Joe Williams". AllMusic. Retrieved March 15, 2016.
Jump up ^ Ruhlmann, William. "Paul Revere & the Raiders – Just Like Us! album review". AllMusic. Retrieved December 18, 2015.
Jump up ^ Billboard (March 23, 1968). "Bubbling Under the Hot 100". Billboard. Nielsen Business Media. 80 (9): 54. ISSN 0006-2510.
Jump up ^ Prato, Greg. "Budgie: Never Turn Your Back on a Friend – Album review". AllMusic. Retrieved December 18, 2015.
Jump up ^ "500 Songs that Shaped Rock and Roll". Rock and Roll Hall of Fame. 1995. Archived from the original on April 22, 2007. Retrieved April 26, 2015.
References
Billboard (April 3, 2004). "'Honk' if You Love Old Aerosmith". Billboard. Nielsen Business Media. 116 (14). ISSN 0006-2510.
Birnbaum, Larry (2012). Before Elvis: The Prehistory of Rock 'n' Roll. Scarecrow Press. ISBN 978-0-8108-8629-2.
Bonomo, Joe (2010). AC/DC's Highway To Hell. Bloomsbury Publishing USA. ISBN 978-1441141583.
Case, George (2007). Jimmy Page: Magus, Musician, Man – An Unauthorized Biography. Hal Leonard. ISBN 978-1-4234-0407-1.
Clayson, Adam (2006). Led Zeppelin: The Origin of the Species. Chrome Dreams. ISBN 1-84240-345-1.
Dahl, Linda (1984). Stormy Weather. Proscenium Publishers.
Demetre, Jacques (1994). The Prewar Blues Story 1926–1943 (CD compilation booklet). Various artists. Best of Blues. OCLC 874878605. Best of Blues 20.
Escott, Colin (2002). B.B. King: The Vintage Years (Box set booklet). B.B. King. Ace Records, Ltd. OCLC 52004950. Ace ABOXCD 8.
Fink, Jesse (2014). The Youngs: The Brothers Who Built AC/DC. St. Martin's Press. ISBN 978-1466865204.
Garon, Paul (2004). "Baby Please Don't Go/Don't You Leave Me Here". In Komara, Edward. Encyclopedia of the Blues. New York City: Routledge. ISBN 978-1135958329.
Gioia, Ted (2008). Delta Blues (Norton Paperback 2009 ed.). New York City: W. W. Norton. ISBN 978-0-393-33750-1.
Gordon, Robert (2002). Can't Be Satisfied: The Life and Times of Muddy Waters. Little, Brown. ISBN 0-316-32849-9.
Hal Leonard (1995). "Baby Please Don't Go". The Blues. Milwaukee, Wisconsin: Hal Leonard. ISBN 0-79355-259-1.
Herzhaft, Gerard (1992). "Baby, Please Don't Go". Encyclopedia of the Blues. Fayetteville, Arkansas: University of Arkansas Press. ISBN 1-55728-252-8.
Marcus, Greil (2010). When That Rough God Goes Riding: Listening to Van Morrison. PublicAffairs. ISBN 978-1-58648-821-5.
Miller, Heather (2009). AC/DC: Hard Rock Band. Enslow Publishers. ISBN 978-0766030312.
Murray, Charles Shaar (2002). Boogie Man: The Adventures of John Lee Hooker in the American Twentieth Century. Macmillan. ISBN 978-0-312-27006-3.
Palmer, Robert (1989). Muddy Waters: Chess Box (Box set booklet). Muddy Waters. Universal City, California: Chess Records/MCA Records. OCLC 154264537. CHD3-80002.
Perkins, Jeff (2011). AC/DC – Uncensored on the Record. Coda Books. ISBN 978-1908538543.
Rogan, Johnny (2006). Van Morrison: No Surrender. Random House. ISBN 978-0-09-943183-1.
Strong, Martin C. (2002). The Great Rock Discography. Canongate Publishing. ISBN 978-1-84195-312-0.
Thompson, Gordon (2008). Please Please Me: Sixties British Pop, Inside Out. Oxford University Press. ISBN 978-0-19-533318-3.
Walker, Clinton (2011). Highway to Hell: The Life and Death of AC/DC Legend Bon Scott. Verse Chorus Press. ISBN 978-1891241864.
Wall, Mick (2013). AC/DC: Hell Ain't a Bad Place to Be. Macmillan. ISBN 978-1250038753.
Whitburn, Joel (1988). Top R&B Singles 1942–1988. Menomonee Falls, Wisconsin: Record Research. ISBN 0-89820-068-7.
[show]
v
t
e
Muddy Waters
Studio albums
1964
Folk Singer
1967
Super Blues
1968
Electric Mud
1969
After the Rain
Fathers and Sons
1972
The London Muddy Waters Sessions
1977
Hard Again
1978
I'm Ready
1981
King Bee
Live albums
1960
At Newport 1960
1969
Fathers and Sons
1979
Muddy "Mississippi" Waters – Live
2012
Live at the Checkerboard Lounge, Chicago 1981
Compilations
1958
The Best of Muddy Waters
1967
The Real Folk Blues
1969
Sail On
2000
Rollin' Stone: The Golden Anniversary Collection
2001
The Anthology (1947–1972)
Noted singles
1950
"Rollin' and Tumblin'"
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1951
"Long Distance Call"
1953
"Baby Please Don't Go"
1954
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"I'm Ready"
1955
"Mannish Boy"
1956
"Trouble No More"
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"Got My Mojo Working"
1962
"You Shook Me"
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Them
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Jackie McAuley
Peter Bardens
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Singles
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"Don't Start Crying Now" (1964)
"Baby, Please Don't Go" / "Gloria" (1964)
"Here Comes the Night" (1965)
"One More Time" (1965)
"It's All Over Now, Baby Blue"
"Call My Name"
"I Can Only Give You Everything"
"Richard Cory" (1966)
Compilations
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[show]
v
t
e
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"Walk This Way"
"You See Me Crying"
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