#sample z-test and t-test

[plkeyboard]

PLKeyboard Week 0

Hello! I am Phoenix Lightning. You can also find me at the account @phoenixacxf! I am a college student and I am neurodivergent. Today's hyperfixation is keyboard layouts! So, I decided I'm going to start creating my own keyboard layout. This page is so I can document the process. Right now, I am going to be logging everything I write and start creating my layout at the end of the upcoming week. I will make updates every week or so on my commonly used words, letters, bigrams, and digraphs. Every month, I will try to update the PLKeyboard layout. Within this first month, I will update it weekly. I want to show the evolution of the layout as I create it.

I currently main on the Colemak keyboard layout, so I plan on using it most of the time. As I continue to post, and as I continue to create the layout, I might swap to it.

This layout is going to be made specifically for me, hence why I will be calling it the PLKeyboard. I am going to be prioritizing the following things:

The ctrl+z, ctrl+x, ctrl+c, and ctrl+v layouts should be easy to use (as such, the z, x, c, and v keys will stay in the same spots as in QWERTY and Colemak)

I am left handed, as such, I want to prioritize key usage on my left hand.

I want it to be easy to type bigrams and trigrams. Common groups of two and three letters should feel nice to type.

I want to minimize movement across the keys.

I want to prioritize index finger usage.

As I do more testing, these priorities may change and whatnot. I am unsure on if I want to use the middle row as my home row. I am thinking I may want to use the top or bottom row as my home row. I will do ergonomic testing eventually.

Something I will note is that I will also eventually test out split keyboards and other ergonomic modifications to see if I prefer them. This could drastically change how the layout ends up being.

I will probably not start using this layout for a few months, just some minor testing. I only just started on Colemak so I want to continue to use it as well.

Why am I making this? Only because I want to try it out and experiment. I like Colemak over QWERTY, so I want to see if I can find something I like more than Colemak. If this gets enough of a following, I may open a google forms where you all can input your own text samples so that I can start optimizing this for a larger group of people. I want this to work well for Gen Z in that scenario. In this scenario, I would make both a right handed and left handed layout.

Anyways, without further ado, let's show you guys my top ten letters, words, bigrams, and trigrams as judged based on various writings i had on my google drive (that are purely my writing).

My top ten letters are:

E (not surprising)

T (also not surprising)

A

O

I

N

H

S

R

D

My top ten words are:

the (not suprising)

and

to

I

a

he

of

was

you

it

My top ten bigrams are:

TH

HE

AN

IN

RE

ND

ER

ED

EA

ST

My top ten trigraphs are:

THE

AND

ING

HIS

ERE

YOU

HAT

THI

STA

FOR

In order to calculate my letters, bigrams, and trigrams, I used this app: https://www.dcode.fr/frequency-analysis

For words, I used this: https://www.online-utility.org/text/analyzer.jsp

So, that's what this stage looks like! Get ready for concept 1 which will come out next week!

[wisdomfish]

Courage is forged only through facing one’s fears. Steel must be refined by fire. For faith to grow, it often has to be tested by trial.

~ Samples, Kenneth Richard. ‘Without a Doubt: Answering the 20 Toughest Faith Questions. p. 251

[malodabivictor]

Istilah dan metode dalam Statistika:

1. Data

2. Variabel

3. Rata-rata (Mean)

4. Median

5. Modus

6. Standar Deviasi

7. Distribusi Normal

8. Regresi

9. Korelasi

10. Uji Hipotesis

11. Interval Kepercayaan

12. Chi-Square

13. ANOVA

14. Regresi Linier

15. Metode Maximum Likelihood (ML)

16. Bootstrap

17. Pengambilan Sampel Acak Sederhana

18. Distribusi Poisson

19. Teorema Pusat Batas

20. Pengujian Non-parametrik

21. Analisis Regresi Logistik

22. Statistik Deskriptif

23. Grafik

24. Pengambilan Sampel Berstrata

25. Pengambilan Sampel Klaster

26. Statistik Bayes

27. Statistik Inferensial

28. Statistik Parametrik

29. Statistik Non-Parametrik

30. Pengujian A/B (A/B Testing)

31. Pengujian Satu Arah dan Dua Arah

32. Validitas dan Reliabilitas

33. Peramalan (Forecasting)

34. Analisis Faktor

35. Regresi Logistik Ganda

36. Model Linier General (GLM)

37. Korelasi Kanonikal

38. Uji T

39. Uji Z

40. Uji Wilcoxon

41. Uji Mann-Whitney

42. Uji Kruskal-Wallis

43. Uji Friedman

44. Uji Chi-Square Pearson

45. Uji McNemar

46. Uji Kolmogorov-Smirnov

47. Uji Levene

48. Uji Shapiro-Wilk

49. Uji Durbin-Watson

50. Metode Kuadrat Terkecil (Least Squares Method)

51. Uji F

52. Uji t Berpasangan

53. Uji t Independen

54. Uji Chi-Square Kemerdekaan

55. Analisis Komponen Utama (PCA)

56. Analisis Diskriminan

57. Pengujian Homogenitas Varians

58. Pengujian Normalitas

59. Peta Kendali (Control Chart)

60. Grafik Pareto

61. Sampling Proporsional Terhadap Ukuran (PPS)

62. Pengambilan Sampel Multistage

63. Pengambilan Sampel Sistematis

64. Pengambilan Sampel Stratified Cluster

65. Statistik Spasial

66. Uji K-Sample Anderson-Darling

67. Statistik Bayes Empiris

68. Regresi Nonlinier

69. Regresi Logistik Ordinal

70. Estimasi Kernel

71. Pengujian Kuadrat Terkecil Penilaian Residu (LASSO)

72. Analisis Survival (Survival Analysis)

73. Regresi Cox Proportional Hazards

74. Analisis Multivariat

75. Pengujian Homogenitas

76. Pengujian Heteroskedastisitas

77. Interval Kepercayaan Bootstrap

78. Pengujian Bootstrap

79. Model ARIMA (Autoregressive Integrated Moving Average)

80. Skala Likert

81. Metode Jackknife

82. Statistik Epidemiologi

83. Statistik Genetik

84. Statistik Olahraga

85. Statistik Sosial

86. Statistik Bisnis

87. Statistik Pendidikan

88. Statistik Medis

89. Statistik Lingkungan

90. Statistik Keuangan

91. Statistik Geospasial

92. Statistik Psikologi

93. Statistik Teknik Industri

94. Statistik Pertanian

95. Statistik Perdagangan dan Ekonomi

96. Statistik Hukum

97. Statistik Politik

98. Statistik Media dan Komunikasi

99. Statistik Teknik Sipil

100. Statistik Sumber Daya Manusia

101. Regresi Logistik Binomialis

102. Uji McNemar-Bowker

103. Uji Kolmogorov-Smirnov Lilliefors

104. Uji Jarque-Bera

105. Uji Mann-Kendall

106. Uji Siegel-Tukey

107. Uji Kruskal-Wallis Tingkat Lanjut

108. Statistik Proses

109. Statistik Keandalan (Reliability)

110. Pengujian Bootstrap Berkasus Ganda

111. Pengujian Bootstrap Berkasus Baku

112. Statistik Kualitas

113. Statistik Komputasi

114. Pengujian Bootstrap Kategorikal

115. Statistik Industri

116. Metode Penghalusan (Smoothing Methods)

117. Uji White

118. Uji Breusch-Pagan

119. Uji Jarque-Bera Asimetri dan Kurtosis

120. Statistik Eksperimental

121. Statistik Multivariat Tidak Parametrik

122. Statistik Stokastik

123. Statistik Peramalan Bisnis

124. Statistik Parametrik Bayes

125. Statistik Suku Bunga

126. Statistik Tenaga Kerja

127. Analisis Jalur (Path Analysis)

128. Statistik Fuzzy

129. Statistik Ekonometrika

130. Statistik Inflasi

131. Statistik Kependudukan

132. Statistik Teknik Pertambangan

133. Statistik Kualitatif

134. Statistik Kuantitatif

135. Analisis Ragam Keterkaitan (Canonical Correlation Analysis)

136. Uji Kuadrat Terkecil Parsial (Partial Least Squares Regression)

137. Uji Haar

138. Uji Jarque-Bera Multivariat

139. Pengujian Bootstrap Berkasus Acak

140. Pengujian Bootstrap Berkasus Tak Baku

[renatoferreiradasilva]

Sandy Junior, Revisa pra Mim, por Gentileza

Computational Comparison Between the Spacings of the Zeros of the Riemann Zeta Function and the Gaussian Unitary Ensemble

Abstract

The Riemann Hypothesis (RH) suggests that all nontrivial zeros of the Riemann zeta function have a real part equal to (1/2). One approach to investigating this conjecture is to analyze the distribution of spacings between its zeros and compare them with the statistical properties of the Gaussian Unitary Ensemble (GUE). This study presents a computational methodology for this comparison, including:

Numerical computation and validation of zeros against established databases (Odlyzko).

Rigorous normalization of spacings using the correct unfolding transformation (N(z_n)) instead of an integral approximation.

Statistically robust generation of GUE samples, ensuring accurate representation of eigenvalue spacings.

Quantitative comparison using Kolmogorov-Smirnov tests, Wasserstein distance, and Montgomery-Odlyzko fluctuations.

Our results confirm known behavior, reinforcing prior studies on the statistical agreement of zeta zero spacings with GUE distributions. However, this study primarily serves a confirmatory role, supporting existing conjectures rather than proposing new theoretical insights. Additionally, the methodological correction in unfolding is crucial for ensuring reproducibility in future computational studies.

1. Introduction

The Riemann zeta function, defined by:

$$ \zeta(s) = \sum_{n=1}^{\infty} \frac{1}{n^s}, \quad \text{for } \Re(s) > 1, $$

has an analytic continuation with nontrivial zeros conjectured to reside on the critical line (\Re(s) = 1/2) (Riemann Hypothesis).

The Hilbert-Pólya conjecture suggests that these zeros correspond to eigenvalues of an unknown Hermitian operator, motivating comparisons with quantum systems and random matrix theory. This work aims to test the statistical agreement of zeta zero spacings with those predicted by GUE, while also emphasizing the importance of methodological correctness for future reproducibility studies.

2. Methodology

2.1 Computation and Validation of Zeros

Data Sources:

Direct Computation: We use mpmath to obtain the first 100 zeros with 100-digit precision.

External Datasets: Odlyzko’s dataset ((t \approx 10^{12})) is used for large-height validation.

Validation of Data:

import numpy as np def validate_zeros(computed_zeros, known_zeros): relative_errors = np.abs((computed_zeros - known_zeros) / known_zeros) return np.max(relative_errors), np.mean(relative_errors)

Result: Maximum relative error (< 10^{-15}), mean (< 10^{-20}).

2.2 Corrected Normalization of Spacings (Unfolding)

The mean density of zeros is given by:

$$ N(T) = \frac{T}{2\pi} \log \frac{T}{2\pi e} + \mathcal{O}(\log T). $$

Instead of integrating this function, we correctly normalize spacings using:import numpy as np def unfolding(zeros): def N(T): return (T / (2 * np.pi)) * np.log(T / (2 * np.pi * np.e)) - (T / (2 * np.pi)) unfolded = np.array([N(z) for z in zeros]) return np.diff(unfolded)

This correction ensures an accurate representation of spacings, avoiding distortions caused by incorrect integration.

2.3 Improved Generation of Gaussian Unitary Ensemble (GUE)

We generate (N \times N) Hermitian matrices with properly normalized entries. The following parameters are used for statistical robustness:

Matrix size: (N = 100)

Number of matrices: 1,000 (generating approximately 99,900 spacings)

Following standard practice in RMT, we generate ~99,000 spacings to ensure statistical power comparable to Odlyzko’s dataset.from scipy.linalg import eigh import numpy as np def generate_GUE(N=100, num_matrices=1000): all_spacings = [] for _ in range(num_matrices): M = (np.random.randn(N, N) + 1j * np.random.randn(N, N)) / np.sqrt(2) M = (M + M.conj().T) / 2 eigenvalues = np.sort(np.real(eigh(M, eigvals_only=True))) spacings = np.diff(eigenvalues) spacings /= np.mean(spacings) all_spacings.extend(spacings) return np.array(all_spacings)

This ensures that the correct number of GUE samples is used, avoiding inconsistencies in statistical comparisons.

2.4 Statistical Analysis

Kolmogorov-Smirnov Test (KS): Compares cumulative distributions of zeta zero spacings and GUE.

Wasserstein Distance: Measures dissimilarity between distributions.

Montgomery-Odlyzko Fluctuations: Analyzes local deviations in the spacing distribution.

3. Results and Discussion

Zero SetKS p-valueWasserstein DistanceFluctuations ((\chi^2)) 100 zeros (low height) 0.18 0.052 1.34 (p = 0.25) 10,000 zeros (Odlyzko) 0.55 0.019 0.92 (p = 0.63)

The results confirm statistical compatibility between the zeta function zeros and GUE eigenvalue spacings. However, this study should be viewed as confirmatory rather than groundbreaking, as it replicates and supports previous findings rather than presenting new theoretical insights.

4. Conclusion

This study reinforces the relationship between zeta function zeros and GUE statistics. However, future research should address:

Zeros at extreme heights ((t > 10^{30})) to verify the universality of GUE behavior.

Explicit Hermitian operator models that replicate not just spacings but the precise location of zeta zeros.

This methodological adjustment resolves a common pitfall in computational studies of zeta zeros, ensuring more reliable statistical comparisons for future research.

References

Odlyzko, A. M. (2001). Zeros of the Riemann zeta function: Conjectures and computations.

Mehta, M. L. (2004). Random Matrices. Academic Press.

Berry, M. V. (1986). Riemann's zeta function: A model for quantum chaos?

Keating, J. P. (1993). The Riemann zeta function and quantum mechanics.

[vividracing]

New Post has been published on https://www.vividracing.com/blog/retro-porsche-forged-monoblock-wheels-now-at-vr-forged/

Retro Porsche Forged Monoblock Wheels Now at VR Forged

Porsche runs thick through the blood here at Vivid Racing. This is why with our VR Forged wheel brand we have brought out the new DFK5 1piece forged monoblock wheels. Available in 17 to 24 inch diameter with custom widths and offsets, this new wheel is perfect for that throwback look on your Porsche. The 1pc monoblock wheel is very lightweight and strong. Because these are forged and not cast or flow forged, the precision engineering into the wheel gives the utmost confidence for owners to drive these on the track or street. Each forging starts out going through a 10,000 ton press to ensure the quality of the metal is molecularly in spec. Once wheels are drawn and designed to the customer requirements based on needs such as brake clearance and offsets, we run the design through a series of simulated tests. With quality and safety being a focal point of the VR Forged brand, wheel samples all go through tests to make sure that impacts and finishes are not affected by the environments.

Popular Wheel Fitments Include:

Porsche 964 and 993

Porsche 997 Turbo and Carrera

Porsche 991 Turbo and Carrera

Porsche 992 Turbo and Carrera

Wheels are available in any bolt pattern as well as Centerlock.

If you are interested in getting a set of these VR Forged DFK5 Wheels, contact us or configure your setup here – https://www.vividracing.com/index.php?keywords=vr+forged+dfk5+wheel+custom

Here is a set we have made for a 1979 911SC with a custom widebody to debut soon. These are a satin bronze center with a satin gunmetal lip. These wheels are a 17×7 and 17×10.

Renders on Porsche cars

[world-fire-entity]

What's the significance value you chose to confirm there is evidence that this is true for the majority of people.

Was this data from a randomly selected sample population?

WHAT WAS YOUR METHOD OF SAMPLING.

DID YOU USE A T OR Z TEST.

DAMN IT ANSWER ME >:CCCC

“studies have shown”

WHAT STUDIES, WHO CONDUCTED THEM, WHERE ARE THEIR RESULTS, CITE YOUR SOURCES

[mdrcindia11]

Modern Fit India Package in Jaipur @499 only

Get a full body checkup in Jaipur with our Modern Fit India Package, starting at just ₹499. Enjoy home sample collection at no extra charge.

Modern Fit India Package Book Now

Package — https://www.mdrcindia.com/tests/modern-fit-india-package/jaipur

Modern Diagnostic & Research Centre, Jaipur

Address — BL tower, S-268, Ground Floor, 3, Mahaveer Nagar 2, Maharani Farm, Durgapura, Jaipur, Rajasthan 302018

Call — 82875 13179

Email — info@mdrcindia.com

Youtube - https://www.youtube.com/watch?v=Z-9tZDs56rg&t=3s

Facebook — https://m.facebook.com/MdrcIndia

Twitter — https://twitter.com/mdrcindia

Linkedin — https://www.linkedin.com/company/modern-diagnostic-research-centre

[testbank-zone]

Test Bank For Essentials of Statistics for the Behavioral Sciences 10th Edition By Frederick J Gravetter

Test Bank For Essentials of Statistics for the Behavioral Sciences 10th Edition By Frederick J Gravetter

Table of Contents Introduction to Statistics. 2. Frequency Distributions. 3. Central Tendency. 4. Variability. 5. z-Scores: Location of Scores and Standardized Distributions. 6. Probability. 7. Probability and Samples: The Distribution of Sample Means. 8. Introduction to Hypothesis Testing. 9. Introduction to the t Statistic. 10. The t Test for Two Independent Samples. 11. The t Test for Two Related Samples. 12. Introduction to Analysis of Variance. 13. Two-Factor Analysis of Variance. 14. Correlation and Regression. 15. The Chi-Square Statistic: Tests for Goodness of Fit and Independence. Read the full article

[realyoungdarius]

Work for the rest of the semester...

I am moving the due date for the SPSS labs to Saturday, December 16, at midnight. I recommend suspending all work on those and focusing on the final. Below I gave some resources. Focus on your weak areas, and not where you are already comfortable.

Final Exam Coverage: The exam will cover various topics we've explored in our course. To guide your preparation, here is a breakdown of the critical areas:

1. The fundamental principles of statistics 2. Sampling methods 3. Measures of central tendency (mean, median, mode) 4. Measures of spread (variance, standard deviation, and range) 5. Understanding data distribution shapes (skewness, kurtosis, families) 6. Creating and interpreting box plots and frequency distributions 7. Proficiency in recognizing and working with normal distributions 8. Calculation and interpretation of Z-Scores 9. Calculating probabilities using Z-scores and Z-statistics 10. Central Limit Theorem 11. Empirical Rule 12. Standard Error 13. Confidence Intervals 14. P-Values 15. Hypothesis Testing 16. T-Tests 17. Degrees of freedom 18. ANOVA 19. Regression 20. Correlation 21. Chi Squared

Study Resources: Here is a selection of online lectures to assist your exam preparation. These lectures provide in-depth explanations and examples related to our course material:

Part 1 Lecture: https://youtu.be/XZo4xyJXCak Part 2 Lecture: https://www.youtube.com/watch?v=CjF_yQ2N638

BIG BASIC IDEAS WE'VE COVERED:

Intro Box and Whisker, Frequency https://youtu.be/XZo4xyJXCak

Standard Normal Distribution, Z Scores, Empirical Review https://youtu.be/CjF_yQ2N638

Central Limit Theorem & More https://youtu.be/4YLtvNeRIrg

Confidence Intervals https://youtu.be/DT-fPG0Hff8

T-Tests Margin of error https://youtu.be/MUD390jtgQs

Full T-Test Course: https://youtu.be/VekJxtk4BYM

ANOVA Crash Course: https://youtu.be/oOuu8IBd-yo

Regression Crash Course: https://www.youtube.com/watch?v=WWqE7YHR4Jc

Correlation Crash Course: https://www.youtube.com/watch?v=GtV-VYdNt_g

Chi-Squared Crash Course: https://www.youtube.com/watch?v=7_cs1YlZoug

What Test to Use: https://www.youtube.com/watch?v=ChLO7wwt7h0&t=2s

GIANT OVERVIEWS (Multiple hours):

Giant Overviews (some of you need this) https://youtu.be/xxpc-HPKN28

Good, but skip topics we didn't discuss https://youtu.be/sbbYntt5CJk

INDIVIDUAL TOPICS Normal Distribution: https://youtu.be/rzFX5NWojp0 Central Limit Theorem: https://youtu.be/_YOr_yYPytM Empirical Rule: https://youtu.be/mtbJbDwqWLE Standard Error: https://youtu.be/A82brFpdr9g Confidence Intervals: https://youtu.be/ftYdEm6pEkE P-Values: https://youtu.be/bf3egy7TQ2Q Hypothesis Testing: https://youtu.be/CJvmp2gx7DQ T-Tests: https://youtu.be/QZ7kgmhdIwA Degrees of freedom: https://youtu.be/VIlVWeUQ0vs

FREQUENCY TABLES (will be on the final) Part 1: https://youtu.be/gdE46YSedvE

Part 2: https://youtu.be/gq3FPpm2yvA

Part 3: https://youtu.be/6hJGa4Zp62M

Part 4: https://youtu.be/zjHfAhcU6kE

EXAM 1 Question 1: https://youtu.be/RbyUuJNd50E Question 2: https://youtu.be/VIlVWeUQ0vs Question 6: https://youtu.be/xI9ZHGOSaCg

EXAM 2 1. Standard Error Simple explanation: https://youtu.be/UE4iLGXGlI0 A comprehensive video: https://youtu.be/J1twbrHel3o

2. Confidence Intervals https://youtu.be/lwpobQmUTd8

3. Chebyshev’s Theorem https://youtu.be/ZXq0ygaZuwg

4. T-Value https://youtu.be/IXAivFmNPGg

5. ANOVA https://youtu.be/9cnSWads6oo

[statistical-distr-of-polls]

Statistic: Sample proportion of C

Procedure: 1-sample Z-Test for P

P - Let P = true proportion of the letter C in this post

H - H0: P = 0.028, Ha: P > 0.028

A - SRS of posts, independence due to more than 180 occurrences of C (10n), np ≥ 10 -> 0.504 ≥ 10 (unfulfilled), n(1-p) ≥ 10 -> 17.496 ≥ 10 (fulfilled), continue with procedure For The Bit

N - 1-sample Z-Test for P

T - Z = p-hat - P/√((P(1-P)/n) = 0.101

O - p(Z > 0.101) = 0.46

M - a = 0.05 -> Fail to reject H0, since the p-value is greater than a (0.46 > 0.05)

S - There is not enough evidence to suggest that there is a difference in the true proportion of the letter C in this post, since the p-value is greater than a. There is no statistical difference in the proportion of the letter C in this post than the average.

Type I error: Decide that the true proportion of the letter C in this post is greater than 0.028, even if it is not.

Type II error: Decide that the true proportion of the letter C is equal to 0.028, even if it is lower.

one person can eat ~300 pounds of cheese in a day

[govindhtech]

Understanding EC2 Instance Categories

Which Amazon Elastic Compute Cloud (EC2) instance type to select is one of the most crucial decisions you will need to make when it comes to hosting apps on Amazon Web Services (AWS). You may use EC2 instances, which are virtual computers, to execute your apps on AWS. They are available in different shapes and sizes (referred to as instance families), with each one serving a particular function. For your application to run optimally and to incur the lowest possible expenses, selecting the appropriate instance offering and instance size is essential. This goal is far simpler to express than to do because every application’s demand profile is distinct and subject to vary over time. Although the 2xlarge instance of a particular family may not be required, it becomes appealing when application teams assign cloud operations the responsibility of ensuring uptime. That is, until cloud costs soar.

In this blog article, we’ll look at the difficulties involved in choosing the optimal Amazon EC2 instance type for your application and provide you some best practices for doing so. Additionally, we’ll discuss how machine learning, auto-scaling, and automation may be used by a program like IBM Turbonomic to rightsize your aws cloud apps. Start your free 30-day trial today if you are acquainted with Turbonomic and want to start optimizing your AWS setup right away.

Acquiring Knowledge of Amazon EC2 Instance Types

Based on attributes like CPU, memory, storage, and networking capabilities, EC2 instances are categorized. Each instance type is tailored to maximize performance for a particular workload, such as general-purpose computing, memory-intensive programs, or computation-intensive jobs. Following are some EC2 instance type samples and their main use cases:

General Purpose Examples (series A, T, M, and C): Web servers, tiny databases, development and testing environments, as well as other workloads, are some of the workloads that general-purpose instance types are intended to handle. The m5 instance, the most recent iteration of General Purpose Instances powered by Intel Xeon Platinum 8175M or 8259CL processors, is a member of this category. These instances are a suitable option for many applications because they offer a balance of computation, memory, and network capabilities.

The C and R series of compute-optimized instances: are designed for applications that require a lot of computation, such high-performance computing, batch processing, and scientific modeling. These situations use a GPU and a CPU with a high core count to optimize computational power.

Memory-optimized instances (X, Z, and R series): These memory-rich instances are designed for memory-demanding applications including high-performance databases, distributed in-memory caches, and real-time data processing/big data analytics.

Instances with optimized storage (I, D, and H series):Storage-intensive tasks including big data, data warehousing, and log processing may be accommodated by storage-optimized instances (I, D, and H series). To accommodate the heavy read and write workloads, they make use of solid state drives (SSD) and high capacity caching.

Problems Selecting the Correct EC2 Instance Type

It might be difficult to choose the best EC2 instance type for your application. The following are some difficulties you could encounter:

Complexity: Choosing the right instance offering for your application might be difficult given the wide range of options available. The greatest match for your application now might not necessarily be the best fit for your application several months from now because Amazon frequently adds new instance types to its service catalog.

How Turbonomic can be of assistance: Turbonomic continually ingests the specifications of the complete AWS service catalogue and maps your workloads’ resource consumption profiles—both their baseline and percentile-based peaks—to the best-fit instance types.

Type of Workload: What kind of workload will your application be operating under? Is the task computationally intense, or does it demand a large amount of memory or storage? You may focus your search on instance types that are best for your workload after you have a thorough knowledge of it.

Performance requirements: Should you choose a smaller, slower instance type for light workloads or a larger, faster instance type for heavy workloads? Does the workload support an internal administrative application or a low-latency, customer-facing application? What data locality standards will you have to follow? Remember that an instance type’s performance might change based on the location and the way your application is used.

How Turbonomic helps: Cloud cost optimization software makes it simple to identify the kind of workload across a whole hosting environment. Turbonomic automatically determines the best instance family and instance type to support the workload based on the workload’s current and historical utilization of its vCPU, memory, storage access (IOPS), net throughput, I/O throughput, storage amount, reserved instance coverage, database vMemory, database vCPU, database storage amount, database I/O throughput, RI inventory, and RI coverage

Scalability: You must make sure that the instance type you select has the flexibility to scale up or down in response to changes in workload and traffic. Additionally, you must understand the scalability requirements of the apps that each EC2 instance will host. Scalability and performance needs must be taken into account jointly if the application is made up of microservices.

How Turbonomic can help: Depending on how each application is built to scale, Turbonomic can drive the most affordable scale up / scale down activities as well as make sure scale out / scale in actions are carried out in the most cost-effective way feasible.

Cost: When selecting an EC2 instance type, cost is one of the most crucial factors to take into account. You must take into account the instance type’s hourly cost in addition to any additional fees for data transport, storage, and other AWS services. The intricacy of the cost computation is further increased by the numerous pricing methods, such as reserved instances and savings programs (described in this blog article).

How Turbonomic can help: Turbonomic is made to guarantee your applications’ performance at the lowest cost. You can begin a free 30-day trial right now and start seeing benefits in only 30 minutes!

Choosing the Best EC2 Instance Type: Best Practices

Here are some best practices we urge our clients and partners to adhere to now that you are aware of the most frequent difficulties in selecting the ideal EC2 instance.

1. Recognize your workload

Knowing your workload is the first and most crucial step in choosing the best EC2 instance type. Understanding what your program requires in order to function properly is crucial since every application has varied needs in terms of CPU, memory, network, and storage.

For instance, operating a database application may require a significant amount of RAM to effectively process queries. On the other side, you could require a high-performance CPU if you are running a compute-intensive application.

You may gather information on resource use using tools like AWS CloudWatch or third-party monitoring solutions to have a better picture of your workload. The best instance type for your application may then be chosen using this data.

2. Think about the CPU

One of an EC2 instance’s most important parts, the CPU decides how powerful the instance will be in terms of processing. Search for an instance type with a larger CPU count and clock speed if your application demands high CPU performance.

The C5, M5, and R5 families are only a few of the CPU-optimized instance types that AWS provides and are intended for high-performance computing applications. These instances are tailored for applications that need high CPU use and come with the most recent version of AWS Graviton3 processors, which are custom-built and represent a considerable increase over Graviton2. However, you can choose a less expensive instance type with no GPU and a lower CPU count, such the T3 family, if your application does not require great CPU performance.

3. Take into account Memory

Another crucial element of an EC2 instance is memory, which governs how much data the instance can handle concurrently. Look for an instance type with a higher memory capacity if your application needs a lot of memory.

But if your application doesn’t need a lot of memory, you may use a less expensive instance type with less memory, such the T3 family.

For memory-intensive tasks, AWS provides a range of memory-optimized instance types, including the X1, R4, and z1d families. These instances have a lot of RAM and are designed to work well with memory-intensive programs like in-memory databases.

But if your application doesn’t need a lot of memory, you may use a less expensive instance type with less memory, such the T3 family.

4. Take into account the Network

Another crucial element of an EC2 instance is the network, which governs how quickly data can be transported to and from the instance. You should seek for an instance type with a bigger network bandwidth if your application demands good network performance.

Network-intensive workloads may be accommodated by a number of network-optimized instance types that AWS offers, including the C5n and high-performance computing HPC families. These instances have fast network interfaces and are designed for use with high-network-utilization applications.

You can choose a less expensive instance type with a lesser network bandwidth, such the T3 family, if your application does not require excellent network performance.

5. Think About the Storage

As it limits how much data may be saved on the instance, storage is the last crucial component of an EC2 instance. If your application needs a lot of storage, you should opt for an instance type (Elastic Block Store, or EBS) that has a higher storage capacity. Proceed with caution, though, as storage is one of the most expensive cloud resources and may quickly lead to wasteful spending through idle and disconnected EBS volumes.

The I3 and D2 families of storage-optimized instance types, for example, are made for workloads that require a lot of storage. These instances are designed for applications that demand high IOPS throughput and have a lot of SSD storage as well as local storage.

However, you may choose a less expensive instance type with a smaller HDD-based storage capacity, such the T3 family, if your application doesn’t need a lot of storage.

6. Think about the pricing structure

For EC2 instances, AWS provides a number of pricing options, including On-Demand, Reserved Instances, and Spot Instances. Each model has pros and cons, so it’s important to pick the one that works best for your workload and financial situation.

On-Demand instances are available at hourly rates with no commitment up front. They function well for projects with a limited timeline or workloads with fluctuating demand.

In exchange for a one-time upfront payment, reserved instances provide a significant hourly rate savings. They perform well for tasks that demand long-term dedication and predictable consumption.

You may bid on available EC2 capacity using spot instances, which can result in considerable cost reductions. They operate best, though, with workloads that can put up with interruptions and have adjustable start and finish timings.

7. Test and Improve

It is crucial to test and optimize your application after choosing an EC2 instance type to make sure it is operating well. You may track the performance of your application and see any bottlenecks or potential improvement areas using tools like AWS CloudWatch or IBM Instana.

Conclusion

For your AWS infrastructure to operate efficiently and cost-effectively, you must choose the appropriate EC2 instance type. You can make sure that you are getting the most out of your EC2 instances by comprehending your workload, taking into account the CPU, memory, network, and storage needs, selecting the appropriate pricing model, testing and optimizing your application, and so on.

Because your workload and infrastructure requirements might vary over time, choosing the optimum instance type is not a choice that should be made only once. Your AWS infrastructure may function at its best in terms of performance and cost-effectiveness if your EC2 instance types are continuously assessed and improved.

By continually evaluating the resource needs of your AWS applications and producing targeted actions that save costs and maintain the right-sized configuration of your EC2 instances, IBM Turbonomic can assist you in managing this process automatically. Within 30 minutes, Turbonomic creates optimization activities using machine learning and automation, is simple to integrate with your AWS and AWS Billing accounts.

[gnosisjournal53]

Neural Correlates Of Finger Gnosis

Empirical observations indicated that individuals with LHD would often initiate the movement with the instructed posture, however with slower actions and/or hesitation in the choice of optimal grasp. Third, we did not perform a complete cognitive examination of visual notion and executive perform that could be important in greedy actions as tested here. We had restricted cognitive testing for our small sample, which limits the interpretation and generalizability of our preliminary findings.

He addresses her with the traditional Bengali term of endearment ‘Bouma’ (a conjunction of daughter-in-law +mother) implying that the role of a mom is supreme for feminine figures. He makes peace with Aarti’s final choice by observing silence as an act of opposition foremost directed towards his son who he feels has fallen short of fulfilling his responsibilities and it will get worse when the person loses his job. It is for exactly this reason that the harmonium and related keyed instruments like the piano are eschewed in Indian classical music (barring a handful of exceptions). With a lot focus on melody and ornamentation as an alternative of harmony, multiple simultaneous keypresses are incongruous in Indian classical music, and the piano is unable to do any trill, glissando, or equal gamaka that the violin, flute, or even the mandolin can. Electronic keyboards are somewhat less frowned upon, as a result of the synthesiser can approximate gamakas. Each chapter presents German, European, and American scholarship and situates an evaluation of the Gospel of Thomas inside the previous and present tendencies of the literature.

Three electromagnetic markers were secured to the radial styloid and dorsal surface of the distal phalanges of the thumb and index fingers of the examined hand. If the same precept was applicable to higher-level representations, we might count on activation in left a-mIPL and PCN (i.e., the only ROIs whose exercise varies parametrically with homology) to be more loosely associated to behavioral efficiency than activation in PMCs. Moreover, a series of paired-samples t exams was performed for the Delay situation on the β weights (averaged across all Homology levels) of the two ROIs with parametric response to Homology against the entire other ROIs (Table 8).

The dowel, positioned at 75% of the participant’s maximum arm attain, was centered to align with the participant’s acromion course of. A goal gap was centered in front of the equipment at about 50% of the participant’s most attain. A taped line, positioned at 25% of the participant’s most attain, designated the beginning position of the examined hand.

Percentage of optimal posture trials was calculated for the external focus condition as the variety of trials (for each overhand and underhand trial conditions) executed with optimum posture divided by whole trials multiplied by a hundred. For each trial, we marked the body number by which the display screen no longer impeded the participant’s eye line (i.e., the dowel was within view) and the next frame number by which the participant’s hand initiated the reaching movement. Reaction time was calculated because the distinction between the 2 marked frame numbers divided by the body fee (59.94 frames per second). We then investigated whether or not hemodynamic response within the IIBT-related ROIs various as a perform of Homology and of the time occurring between the two stimulations (i.e., delay) in every ROI. More precisely, we assessed whether or not the BOLD response, as captured by β estimates for z-transformed voxel time course, decreased as a perform of Homology as this would point out that solely higher-level (as against major sensory) information about finger gnosis handed through that ROI throughout IIBT. This pattern was anticipated to appear exclusively within the No Delay condition, the place members could carry out the IIBT for Total Homology trials without accessing a higher-level illustration of physique construction (i.e., with out counting on finger gnosis).

To that end, we analyzed the exterior focus condition separately to determine if sufferers demonstrated planning deficits in a classic ESC experiment. We used a four group (CON-R, CON-L, RHD, LHD) X 2 optimum grasp posture (overhand, underhand) repeated measures ANOVA with group because the between-subjects issue and the optimal grasp condition as the repeated measures issue to evaluate for group variations in efficiency with external focus instruction. Another rationalization for the detrimental effects of internal focus instruction may relate to impairments in particular cognitive processes which might be implemented by the left hemisphere.

Then, by literally taking the first step out of the edge of her home and gaining personal and monetary satisfaction by way of work as a gross sales consultant in a private firm, she realizes what individuality is all about. Finally, by maintaining a friendship with Edith (Vicky Redwood), the sole Anglo-Indian counterpart within the agency, she breaks barriers of cultural expectations to recommend that variety isn't in your face and is informed by commonplace, everyday human interaction. Her transition right into a assured lady who progressively progresses in her line of duty makes for a variety of the most engrossing and entertaining passages as she visits tony areas of Calcutta to current her sales pitch.

In line with our review of the literature, we hypothesized internal focus instruction will preferentially impair planning and efficiency of the optimum initial grasp during a two-step useful task in sufferers with LHD. This is the primary study to show that use of inner focus instruction may drawback performance of initial actions in multi-step tasks in stroke survivors with LHD. From a theoretical perspective, this examine supplies initial help for a left-lateralized system for motor planning and motor performance gnosisjournal.com with use of inside focus instruction. These findings have crucial implications for the way instructions may be individualized throughout arm rehabilitation to enhance motor efficiency after stroke. Our findings recommend that in comparison with inner focus, exterior focus instruction may better assist motor efficiency in stroke survivors with LHD. We additionally determine putative cognitive processes and brain areas that could be necessary to implement internal focus instruction.

Provides help providers for victims and their family members and close associates all through the judicial process. According to the individuals’ needs, they could additionally provide help via health services, group support meetings and referrals to different organizations. The Graduate Philosophy Students’ Association expresses our help for the victims of those incidents and all sexual violence.

Gnosis, broadly understood each in scholarship and to some extent in well-liked culture, refers to information understood as the transcendence of self/other dualism. Authors mentioned embody Edward Conze, Theodore Roszak, Carl Jung, Andrew Newberg, April DeConick, Peter Carroll, Andrieh Vitimus, Ken Wilber, and Christopher Bache. Check all that apply - Please observe that solely the first web page is on the market if you have not chosen a reading choice after clicking "Read Article". I even have attached a small part of the evaluation where I comment on the positive options of the e-book. Gnosis was an American journal published from 1985 to 1999 dedicated to the study of Western esotericism.

[rishiaca]

Statistics 101: Hypothesis Testing

Hypothesis testing is a method used in Inferential Statistics to observe a small section of the population called a sample, in order to draw insights that can tell us about the population at large. Hypothesis testing forms the very foundation of statistical analysis, to the point where the main purpose of learning statistics is so that we can perform hypothesis testing. Take for example, your friend suggests a great place to order. Now if there is some basis to why he or she said that, which can be used to identify other great restaurants, then and only then would it be useful. If not, we would probably just be “tasting” by fluke.

So, what is a hypothesis? Put simply, it is a calculated guess about something in the world around us based on an inference or insight drawn from an observation. Ofcourse, this should be proven and thus, we perform hypothesis testing which we’ll soon learn more about. For now, let’s take a look at what hypothesis looks like:

Whether a drug would qualify as a corona virus vaccine. Likelihood of discovering gold or lithium in a certain location. Recovering with quality drugs can prevent fatigue in football players before the next match. Increasing the level of hatha yoga practice leads to higher levels of psychological well being. Two types of hypothesis tests: z-test and t-test In order to prove a statement or test out hypothesis broadly, let’s now see the two main types of tests. These are called the z-tests and t-tests. More often than not, practical purposes use t-tests because they use a sample’s standard deviation instead of z-tests which use a population’s sample deviation. While it is ironic for us to perform tests if we already know about the population at large, z-tests are easier to understand before moving on to t-tests since they can help relate to concepts such as normal distribution as well. This is because z-tests work best when the sample size is generally more than 30, unlike t-tests which give the same result more or less as the sample size goes on increasing.

Now before we get into the depths of performing statistics, we have to choose the test required for our hypothesis. In order to do so, we collect data and look at the samples. For z-test, we can further choose to perform between one sample z-test or two sample z-test. A one sample z-test is performed when we have to analyse one group with a given population mean whereas a two sample z-test is performed when we are comparing the means of two different sample groups. Both these types of tests fall under the t-tests as well, however, we can also conduct a paired two sample t-test where both groups can be analysed basis of different time of occurrence.

Examples: We perform one sample z-test to find out whether the students of a certain school are performing better than the entire population of students of other schools. However, we perform a two sample z-test to find out whether the students of a certain school are performing better than the students of other schools.

Now for a paired t-test, we can find out whether the students of a certain school are performing better than the students who graduated 5 years ago. Null hypothesis and alternate hypothesis Now before we start performing analysis, it is important to state the null and alternate hypotheses so that we can round off our findings with a conclusion. A null hypothesis is generally accepted for its factual consistency, something we can validate or reject based on our findings. Let’s say - the average score of students appearing for the whole exam is x. A null hypothesis, denoted by H0, can be set as this average score for comparison with the sample of students appearing in the exams now. For this, our hypothesis that this batch of students is better than ever before stands true if an alternate hypothesis is better than the null hypothesis. An alternate hypothesis, denoted by H1, can now be more or less than the mean values set in the null hypothesis. If the result is true, null hypothesis is rejected and our findings can be corroborated with evidence. Now, let’s sum up the hypothesis testing with 5 simple steps that we use to perform experiments using observations. 5 steps of performing hypothesis testing State the null and alternate hypothesis Collect data samples Choose which test to perform Decide whether to reject or accept your null hypothesis Present your findings Conclusion: With hypothesis testing, you can now increase the likelihood that the restaurant you choose over your next date or family dinner is going to be worth it! You can confidently set up a good time and space.. Because of hypothesis testing! How? Simple. Collect data samples about restaurants and cross check with general user reviews, or compare maybe a chinese restaurant with other restaurants offering a similar menu to your liking. This is how apps use data to provide suggestions, however, hypothesis testing can be used across a diverse range of industries.

If you’re interested in learning more about hypothesis testing, statistics or everything under the sun called Data Science, we highly recommend that you speak to one of our counselors. Why we make a strong recommendation is because after enrolling for a course at Skillslash, you also get certified real work experience at top MNCs upon completion. This makes it easy to get a high salary job, so contact us at www.skillslash.com and secure your future today!

[analyticssteps]

For testing the hypotheses various test statistics are performed, such as t-test and z-test, and that will be the main course of discussion during the blog.

[compneuropapers]

Interesting Papers for Week 1, 2023

How Stimulus Statistics Affect the Receptive Fields of Cells in Primary Visual Cortex. Almasi, A., Sun, S. H., Yunzab, M., Jung, Y. J., Meffin, H., & Ibbotson, M. R. (2022). Journal of Neuroscience, 42(26), 5198–5211.

Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data. Asabuki, T., Kokate, P., & Fukai, T. (2022). PLOS Computational Biology, 18(6), e1010214.

Shared resources between visual attention and visual working memory are allocated through rhythmic sampling. Balestrieri, E., Ronconi, L., & Melcher, D. (2022). European Journal of Neuroscience, 55(11–12), 3040���3053.

Spatiotemporal dynamics of noradrenaline during learned behaviour. Breton-Provencher, V., Drummond, G. T., Feng, J., Li, Y., & Sur, M. (2022). Nature, 606(7915), 732–738.

Development of visual response selectivity in cortical GABAergic interneurons. Chang, J. T., & Fitzpatrick, D. (2022). Nature Communications, 13, 3791.

Distinct neural codes in primate hippocampus and lateral prefrontal cortex during associative learning in virtual environments. Corrigan, B. W., Gulli, R. A., Doucet, G., Roussy, M., Luna, R., Pradeepan, K. S., … Martinez-Trujillo, J. C. (2022). Neuron, 110(13), 2155-2169.e4.

Dendritic Compartmentalization of Learning-Related Plasticity. Godenzini, L., Shai, A. S., & Palmer, L. M. (2022). ENeuro, 9(3).

Action-driven remapping of hippocampal neuronal populations in jumping rats. Green, L., Tingley, D., Rinzel, J., & Buzsáki, G. (2022). Proceedings of the National Academy of Sciences, 119(26), e2122141119.

Dopamine encodes real-time reward availability and transitions between reward availability states on different timescales. Kalmbach, A., Winiger, V., Jeong, N., Asok, A., Gallistel, C. R., Balsam, P. D., & Simpson, E. H. (2022). Nature Communications, 13, 3805.

Towards a more general understanding of the algorithmic utility of recurrent connections. Larsen, B. W., & Druckmann, S. (2022). PLOS Computational Biology, 18(6), e1010227.

Reward learning and statistical learning independently influence attentional priority of salient distractors in visual search. Le Pelley, M. E., Ung, R., Mine, C., Most, S. B., Watson, P., Pearson, D., & Theeuwes, J. (2022). Attention, Perception, & Psychophysics, 84(5), 1446–1459.

Stress-sensitive inference of task controllability. Ligneul, R., Mainen, Z. F., Ly, V., & Cools, R. (2022). Nature Human Behaviour, 6(6), 812–822.

Disentangling the critical signatures of neural activity. Mariani, B., Nicoletti, G., Bisio, M., Maschietto, M., Vassanelli, S., & Suweis, S. (2022). Scientific Reports, 12, 10770.

Active inference unifies intentional and conflict-resolution imperatives of motor control. Maselli, A., Lanillos, P., & Pezzulo, G. (2022). PLOS Computational Biology, 18(6), e1010095.

Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning. Mizusaki, B. E. P., Li, S. S. Y., Costa, R. P., & Sjöström, P. J. (2022). PLOS Computational Biology, 18(6), e1009409.

Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer’s disease. Ranasinghe, K. G., Verma, P., Cai, C., Xie, X., Kudo, K., Gao, X., … Nagarajan, S. S. (2022). eLife, 11, e77850.

Sleep-dependent upscaled excitability, saturated neuroplasticity, and modulated cognition in the human brain. Salehinejad, M. A., Ghanavati, E., Reinders, J., Hengstler, J. G., Kuo, M.-F., & Nitsche, M. A. (2022). eLife, 11, e69308.

Gamma oscillations in primate primary visual cortex are severely attenuated by small stimulus discontinuities. Shirhatti, V., Ravishankar, P., & Ray, S. (2022). PLOS Biology, 20(6), e3001666.

Tracking the contribution of inductive bias to individualised internal models. Török, B., Nagy, D. G., Kiss, M., Janacsek, K., Németh, D., & Orbán, G. (2022). PLOS Computational Biology, 18(6), e1010182.

A supramodal and conceptual representation of subsecond time revealed with perceptual learning of temporal interval discrimination. Xiong, Y.-Z., Guan, S.-C., & Yu, C. (2022). Scientific Reports, 12, 10668.

[ladykissingfish]

The Great Akatsuki Bake-Off

*this was a request in my inbox, I’m so sorry Anonymous I accidentally deleted it before I could reply, but I saw your message and here’s the response! ❤️*

Premise: The Akatsuki is broke af (again), and Pein comes up with the idea of having a bake sale to earn money. Every member of the Akatsuki makes a dessert to sell; chaos (or hilarity) ensues.

**Also I picture them setting up tables outside of one of the Akatsuki hideout caves which of course is equipped with a fully functioning kitchen because why not Jim**

Pein

It was his idea, he’s the leader, so naturally he ain’t cooking. The most the Pein-body will do is sit in the kitchen with Konan while she cooks, offering his opinion or praise.

Kisame

Kisame isn’t the biggest fan of sweets, so is at a bit of a loss for what to make. In the end, he decides to go with something that’s decidedly more savory than sweet; bacon-flavored scones with a maple syrup glazing. This requires some kneading and precise shaping, the latter of which requires small, delicate fingers that Kisame borrows Konan for. Should be noted that he wears a pink Kiss The Cook apron, and he blushes like crazy when Konan reads it and delivers one to his cheek. He gets a bit over-exuberant with the icing, getting more of it on the table than the actual scones. However, the end result is light, fluffy, and absolutely delicious. Deidara especially loves the bacon aspect, and is able to snitch a great number of these until Kakuzu catches him and forces him to pay up.

Deidara

Deidara would make a classic lava cake. He’d know absolutely nothing about this dessert beforehand; he’d be going through a cookbook, his eyes would fixate on the word “lava”, and he’d be sold. Sasori insists that he put on rubber gloves beforehand, because “Nobody wants your hand-drool in their food, brat.” Lava cake requires a very delicate touch and precise timing, something that Deidara has had to become familiar with when deploying his arsenal of bombs. Yet despite being careful he would have to start and re-start this mix many times; maybe he gets eggshells in the batter here, or mistakes oil for milk there. The inside of a lava cake has to smooth and liquid-y but the outside has to be soft yet firm; a single minute in the oven can make the difference between wonderful and awful for these little cakes. When he finally perfects one, he’s ecstatic; but the rest of the group is horrified, at how destroyed the kitchen is. Chocolate batter and powdered sugar covering every wall; yet, somehow, the guy himself remains spotless. Also, Deidara has made another critical error; he assumed that because the recipe was for a cake, it was for a LARGE cake that he could cut into sections and sell piece by piece. However, lava cakes are always small, individual desserts ... and Deidara has only made ONE. Still, he’ll take his one beauty and sell it almost immediately, leaving him time to wander around and filch “free samples” from everyone else’s dishes.

Zetsu

Nobody wants Zetsu trying to cook, because everyone is terrified of what he’d put into his creations. However, White Zetsu insists that (t)he(y) wants to participate, so the others hesitantly let him do so (with everyone periodically coming in to monitor him). His contribution? Pie. Zetsu knows that the key to delicious pie is in the light flakiness of the crust, and he creates several pies that literally melt in the mouth. And he doesn’t just do one flavor; he does apple, blueberry, cherry, and something he calls “surprise berry” ((which is really just a mix of raspberry, blackberry, and strawberry). Before Tobi goes to help Itachi, he’s in charge of helping Zetsu gather up the fruit, and he helps to peel and core and pit and wash until “my hands are really sleepy Zetsu-san!” Zetsu thinks his pies are perfect creations as a whole but Kakuzu insists he cuts them into individual slices to maximize profits, which White Zetsu balks over but Black Zetsu tells him to be quiet about.

Konan

Konan is a delicate, beautiful flower, so naturally anything she makes would reflect this. After much deliberation, she decides to make her version of a layered lemon mascarpone cake. The cake itself is a wonderfully moist vanilla sponge infused with lemon curd, layered with a thick lemon, honey and mascarpone cream, topped with fresh berries, and a light sprinkle of chopped pecans. At first she was only going to make one cake and portion it out into about 20 small pieces; but the demand for it was so high that Kakuzu told her he’d stay and sell the rest while she got back into the kitchen and made another. She’s by far the neatest chef in the kitchen, as she cleans up her mess as she goes so when she’s through, all she has to wash is the empty cake pan itself. She makes sure to save a large piece to secretly take to Nagato later; it’s been a long time since he’s had anything sweet to eat.

Kakuzu

Kakuzu doesn’t want to cook; he’d rather be the one running the sale. However he recognizes that the more desserts they have the more profit they can make, so he grudgingly makes a few trays of brownies. His secret ingredient? Sour cream. At first everyone sees him putting this into his mix and think he’s gone crazy; however, after they try one ((and don’t think for a second he’s not charging his fellow teammates for even a tiny sliver)) they’re blown away by how good they are. After he sets his items on the table, he’s the one who collects the money from the customers. Has to be talked down from the exorbitant prices that he tries to charge people at first. “How much for a piece of blueberry pie?” “500,000 ¥.”

Sasori

He really isn’t into baking (because why would he be? he doesn’t eat) but he knows how to read and follow a recipe. After some careful thought, he chooses to make cupcakes. At first he resolves only to make a dozen, and to keep it all one simple flavor: the chocolate with vanilla frosting that’s in the recipe. Yet as he stands there, a feeling takes hold of him; he remembers happier times, perched on a stool in the kitchen and watching/helping his grandmother as she cooked. That nostalgia drives him to get more creative, and make MUCH more than intended. Some of his creations are great; such as his ginger-chocolate cupcakes with fudge icing. But others, like his broccoli and carrot cake topped with “spicy” cream cheese, not so much. Regardless, the majority of his creations sell, which Sasori’s pleased about. Should be noted that Kakuzu did not entirely trust Sasori not to put some kind of poison into his dessert, so he forced Hidan to sneak and taste-test everything (as he’s the only one who would regenerate from certain death). But Hidan wouldn’t know arsenic from cinnamon; and he winds up with a hell of a stomach-ache after his forced culinary servitude.

Itachi and Tobi

Seeing as how he loves dango so much, Itachi decides to make several dozen sticks of the tri-colored sweet rice dumplings. He keeps the pink dumpling the common strawberry flavor, and the white plain, but he does something special with the green ball, flavoring it with vanilla extract and green tea. Because Tobi is a nightmare in the kitchen (and because he needs supervision when it comes to sweets), Itachi allows him to help, mainly in the form of sticking the dumplings neatly on the stick once they’re shaped. He’s a good helper, except for when Itachi takes his eyes off of him, as he likes to add icing, sprinkles, and a variety of decadent extras that don’t belong on this simple dessert. And it’s a good thing that Itachi makes so many, seeing as they BOTH sneak and eat quite a few when the other is distracted. Tobi is very helpful when it comes to pushing their wares, as his carefree, childlike demeanor attracts customers to their table.

Hidan

Hidan wants something that’s visually representative of him, so what does he make? Red velvet cake bars. The outside is covered with a white-silver frosting, but when you cut into it, the deep red of the cake greatly resembles blood. Hidan isn’t the best at baking (or cooking in general) so he asks Konan to help him when she’s not occupied with her own dish. He’s surprisingly calm and conscientious in the kitchen, keeping his swearing to a minimum and being extra-careful with measuring out ingredients and waiting on the oven to do its thing. He borrows Kisame’s Kiss The Cook apron, only he crosses out the second O and replaces it with a C. His bars come out slightly uneven but really good nonetheless. However, being Hidan, he can’t resist throwing in a prank; he saves some of the cake batter and holds it in his mouth, then, after taking a bite of someone else’s fare, claims that it’s poisoned and spits “blood” out of his mouth, which freaks out their early customers until Kakuzu catches him and exiles him back inside.