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euphoriaxnyc · 2 years

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I have exciting new to share! Commodity is now available at one of my favorite beauty retailers. You can pick up this Scent Space Edit exclusively at @Sephora. The Commodity Fragrances Scent Space Edit features six perfumes; Book, Gold, Velvet, Moss, Paper, and Milk. There are three variations of each fragrance; Personal, Expressive, and Bold. This collection features a nice variety and it's a great way to explore their fragrances. Notes: Milk - Cold Milk, Marshmallow, Mahogany, and Tonka Bean. Gold - Juniper Berries, Amber, Creamy Sandalwood, and Sweet Vanilla. Book - Bergamot, crisp Eucalyptus, Vetiver, and Warm Sandalwood. Paper - Iso E Super, woods, Sweet Amber, and Skin Musk. Velvet - Roasted Almonds, Soft Rose Petals, and Sweet Amber. Moss - Bergamot, Oakmoss, and Petitgrain. http://www.commodityfragrances.com *Gifted by @commodity #commodityfragrance #sephora #sephorafragrance #styleyourcommodity #nichefragrance #commodityfragrances #nichefragrances #nicheperfumes #unisexfragrances #scentspaceedit #fragrancecommunity #unisexperfume #discoverykit #discoveryset #fragrancediscovery #sotd #scentoftheday #parfums #fragrancecollection #samplesets #perfumevial #perfumesales #perfumevials #explorationset #perfumeoftheday #eaudeparfum #fragranceniche #fragrance #fragrances #parfum (at New York City, N.Y.) https://www.instagram.com/p/CiazA80p42T/?igshid=NGJjMDIxMWI=

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fabritual12 · 2 years

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Fabric Swatches

Linen Fabric Samples set , Fabric by the yard , Linen Dress Fabric swatch - Block print Dress / Ready to ship.

Visit Shop:-https://etsy.me/38GLXNb

Linen Fabric with Hand Block Print for dresses , clothing and apparels . Swatches Included - Authentic Bagru Block Print , Indigo Dabu block prints, pre-dip Colored with Block Print, Shibori Print , Tie dye print etc . PATTERNS- Floral Geometric Paisley Flower vine polka dot Ajrak Ethnic and many more Traditional BLOCK PRINT PATTERNS on Best Pure Linen Fabrics. ◼Your swatches will include many types of Pure Linen(diffrent Thickness)with various patterns and base colors ◼Sizes of The swatches can be diffrent probably according to their block print pattern . ◼ All of our block prints are handmade by using completely natural colors , process and stencils . ◼ Length of the swatches can be vary due to different sizes of block print patterns ◼Linen fabric characteristics: • Pure 100% linen washed and softened from the manufacturing process • Softened linen fabric are very soft and are highly absorbent • Washed linen fabric products not shrink. • Easy to care. Please Do Not Tumble Dry! • Looks nice not ironed CUSTOMIZATION OF FABRIC , PATTERNS AND COLORS IS ALSO AVAILABLE AS PER YOUR CHOICE ALSO CHECK OUR BLOCK PRINT THICK LINEN THICK COTTON FABRICS FOR HOME DECOR-: https://www.etsy.com/listing/1204844985/block-print-samples-samples-swatches

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tatyanab66 · 2 years

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📣 Friendly updates from the Soap Shop 🧼 All orders over $50 qualify for Complementary "Soap Sample Set"🧨so you'll be able to try different soaps according to the season 🍂🍁🌲🌿🍀 Wish you all Happy Fall 🍂🍁and Holiday season ✨️🎄

#samples #sampleset #soapset

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#Complementary #soapgifts #simplyherbaldelight #localbrand #freesamples #fallvibes #quality

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aujasvisulekh · 2 years

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ANOVA and Hypothesis Testing.

The Syntax:

-- coding: utf-8 --

""" Created on Wed Sep 14 16:20:39 2022

@author: Aujasvi Sulekh """ import pandas as pd import numpy as np import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi

//Load the NESARC dataset

nesarc = pd.read_csv ('NESARC Pds.csv' , low_memory=False)

//Set PANDAS to show all columns in DataFrame

pd.set_option('display.max_columns', None)

//Set PANDAS to show all rows in DataFrame

pd.set_option('display.max_rows', None)

nesarc.columns = map(str.upper , nesarc.columns)

pd.set_option('display.float_format' , lambda x:'%f'%x)

//Change variables to numeric

nesarc['AGE'] = pd.to_numeric(nesarc['AGE'], errors='coerce') nesarc['S3BQ4'] = pd.to_numeric(nesarc['S3BQ4'], errors='coerce') nesarc['S3BQ1A5'] = pd.to_numeric(nesarc['S3BQ1A5'], errors='coerce') nesarc['S3BD5Q2B'] = pd.to_numeric(nesarc['S3BD5Q2B'], errors='coerce') nesarc['S3BD5Q2E'] = pd.to_numeric(nesarc['S3BD5Q2E'], errors='coerce') nesarc['MAJORDEP12'] = pd.to_numeric(nesarc['MAJORDEP12'], errors='coerce') nesarc['GENAXDX12'] = pd.to_numeric(nesarc['GENAXDX12'], errors='coerce')

//Subset the sample

sampleset = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & (nesarc['S3BQ1A5']==1)]

//Cannabis users, ages 18-30

samplecopy = sampleset.copy()

//Setting missing data for quantity of cannabis (measured in joints), variable S3BQ4

samplecopy['S3BQ4'] = samplecopy['S3BQ4'].replace(99, np.nan) samplecopy['S3BQ4'] = samplecopy['S3BQ4'].replace('BL', np.nan)

//Creating a new subset while removing the null values

newsub_a = samplecopy[['S3BQ4', 'MAJORDEP12']].dropna()

//Using ols function for calculating the F-statistic and the associated p value

//Depression (categorical, explanatory variable) and joints quantity (quantitative, response variable) correlation

model_a = smf.ols(formula='S3BQ4 ~ C(MAJORDEP12)', data = newsub_a) result_a = model_a.fit() print(result_a.summary())

//Measure mean and spread for categorical variable MAJORDEP12, major depression

print ('Means for joints quantity by major depression status') mean_a = newsub_a.groupby('MAJORDEP12').mean() print(mean_a)

print ('Standard deviations for joints quantity by major depression status') sdev_a = newsub_a.groupby('MAJORDEP12').std() print(sdev_a)

//Creating a new subset while removing the null values

newsub_b = samplecopy[['S3BQ4', 'GENAXDX12']].dropna()

//Using ols function for calculating the F-statistic and the associated p value

//Anxiety (categorical, explanatory variable) and joints quantity (quantitative, response variable) correlation

model_b = smf.ols(formula='S3BQ4 ~ C(GENAXDX12)', data = newsub_b) result_b = model_b.fit() print(result_b.summary())

//Measure mean and spread for categorical variable GENAXDX12, general anxiety

print ('Means for joints quantity by major general anxiety status') mean_b = newsub_b.groupby('GENAXDX12').mean() print(mean_b)

print ('Standard deviations for joints quantity by general anxiety status') sdev_b = newsub_b.groupby('GENAXDX12').std() print(sdev_b)

//Setting missing data for frequency of cannabis use, variable S3BD5Q2E

samplecopy['S3BD5Q2E'] = samplecopy['S3BD5Q2E'].replace(99, np.nan) samplecopy['S3BD5Q2E'] = samplecopy['S3BD5Q2E'].replace('BL', np.nan)

newsub_c = samplecopy[['S3BQ4', 'S3BD5Q2E']].dropna()

//Using ols function for calculating the F-statistic and associated p value

//Frequency of cannabis use (10 level categorical, explanatory variable) and joints quantity (quantitative, response variable) correlation

model_c = smf.ols(formula='S3BQ4 ~ C(S3BD5Q2E)', data = newsub_c) result_c = model_c.fit() print(result_c.summary())

//Measure mean and spread for categorical variable S3BD5Q2E, frequency of cannabis use

print('Means for joints quantity by frequency of cannabis use status')

//cannabis_use_mean= newsub_b.groupby('S3BD5Q2E').mean()

cannabis_use_mean= samplecopy['S3BD5Q2E'].mean() print(cannabis_use_mean)

print('Standard deviations for joints quantity by frequency of cannabis use status') cannabis_use_sdev = newsub_c.groupby('S3BD5Q2E').std() print(cannabis_use_sdev)

//Run a post hoc test (paired comparisons), using Tukey HSDT

multicmp = multi.MultiComparison(newsub_c['S3BQ4'], newsub_c['S3BD5Q2E']) result = multicmp.tukeyhsd() print(result.summary())

Theory and Introduction:

Analysis of variance, or ANOVA, is a statistical method that separates observed variance data into different components to use for additional tests. A one-way ANOVA is used for three or more groups of data, to gain information about the relationship between the dependent and independent variables.

The data was provided by the National Epidemiological Survey on Alcohol and Related Conditions (NESARC), which was conducted in a random sample of 43,093 U.S. adults and designed to determine the magnitude of alcohol use and psychiatric disorders. Sample size is important because the larger the sample size, the more accurate the findings. NESARC’s unusually large sample size also made it possible to achieve stable estimates of even rare conditions. NESARC participants came from all walks of life and a variety of ages, and the level of analysis studied was individual. They represented all regions of the United States and included residents of the District of Columbia, Alaska, and Hawaii. In addition to sampling individuals living in traditional households, NESARC investigators questioned military personnel living off base and people living in a variety of group accommodations such as boarding or rooming houses and college quarters. More specifically, the sample consists of 24,575 (57.1%) males and 18,518 (42.9%) females, among of whom 9,535 (22.13%) were aged between 18 and 30 years old. The data analytic subset, examined in this study, includes individuals aged between 18 and 30 years old who reported using cannabis at least once in their life (N=2,412).

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