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ryoung39-blog · 6 years ago

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Magnet Binary Adding Machine

Our efforts to design a magnetically based binary adding machine have resulted in this system. Compromised of several modular parts, this whole system is actually a number of individual pieces that when used in conjunction, create the workings of a complete half adder. When run through the system multiple times, one can continuously add binary digits, therefore working as a full adder.

Its important to note how we coded these pods. A vertical pod can be read as a “1″ and a horizontal can be read as a “0″. In prior experiments, we had coded magnetic polarity as either a one or a zero (North side being “1″ and South side being “0″), but labeling them as such gave us limited options. Namely, this only resulted in a natural XOR gate. This made it extremely difficult to develop any other system than this.

In order to accomplish a new method of inputs and outputs, we used two forms of magnet pods. The first one (on the left side) functions as the initial input. These are fixed values, incapable of rotating around within their housing. The second (on the right) can spin, thus rotating itself into the proper orientation.

When the rotating output pod leaves the gate, it is lock in place using piece like the one pictured. This allows the output pod to work in the same fashion as the initial fixed inputs.

youtube

The AND gate is our simplest mechanism. Two inputs placed on the outside slides control the orientation of a single magnet. Every placement of the input except a “1″ and “1″ result in a “0″. The is due to the proximity of the horizontal “0″ input. Because it is closer to the rotating output pod, the output pod rotates closer to the horizontal.

youtube

Our XOR and OR gates function as identical mechanisms with very small differences in their locking mechanisms.

Our gates would sometimes result in undesirable outputs, namely 45 degree angles. This was an issue, since we can only work in horizontal and vertical variables. In order to solve this, each gate was given a different locking mechanism for its output pod.

These output mechanisms had extending arms that, when entered into the second stage of the gate, would push out either a fixed “1″ or “0″ pod.

In essence, if the output pod is rotated at a 45 degree angle, the arm is as well, therefore bypassing the adjacent output arm and directly coming into contact with the opposing output.

Our system did not come with out issues. Although we were able to work out some of the larger problems of polarity (and magnetism in general). Our system lacks an infrastructure to clearly denote and mark the path of the outputs. Perhaps a railing system could be of some use in the next draft.

Its hard to avoid the fact that our locking arm solution was more of a fix for an existing problem rather than a cohesive part of the system. It was, in fact, a method proposed to resolve an undesirable outcome that happened to be in inherent issue of our machine.

E+R

#Magnetic Binary adder #Binary adding machine #connectiveenvironments #assignment_3 #magnets are cool #magnets

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connective-environments-fall2020 · 4 years ago

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Assignment 3: Urban Noise

The main crux of my project is the process of audio visualization. By utilizing audio visualization form multiple points within an urban environment while also displaying each location in conjunction with one another. By placing monitoring stations at multiple points in a city, each with its own specific audio signature the display will provide viewers with an understanding of the sound quality of the environment that they are in as well as the sound quality of the rest of the city.

Context

The premise is to place the monitoring stations and displays in multiple area within a city, each with differing audio qualities. This could mean that they could be placed at high and low density streets, public park spaces and open spaces, even within interiors of transit hubs or commercial buildings.

These locations would be decided by referencing noise density maps of the city, allowing for the systems to be placed at the most effective locations in terms of providing the greatest possible comparison and understanding of the sound landscape of the city.

Visualization

The choice to display audio in the form of a graph came both from technical considerations as well as from the desire to make the display as simple and understandable as possible. By being able to picture the sound as a graph as well as be listening to the sound, I feel that a comprehension is achieved. With this comprehension, one can start to imagine the sound quality of other locations by viewing the graphs created by those sounds.

#assignment_3 #spencer_bauer

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mdakram09 · 3 years ago

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Assignment Week 3

Code and Description Link: https://github.com/mdakram09/Machine-Learning-for-Data-Analysis-Coursera-Assignment/blob/main/assignment_3.ipynb

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connective-environments-2019 · 5 years ago

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Welcome

Welcome to the Connective-Environments Fall 2020 blog. We will be using this blog in addition to the Instagram accounts as the main repository to document the awesome things you will be designing and to document your progress.

In order to post in this blog, you will need to join the group through the invitation you received, and create your account if you don’t have one yet. Couple rules of good practice:

1. Keep the blog’s wall neat.

To keep the wall neat, it is a good practice to add a “Read More” break in your text after the first few sentences that will direct a reader to the full extended post. If you don’t do that, the blog’s wall will soon be crowded with lengthy posts. You can add a “read more” break either in Rich Text format or in HTML format.

To add a “read more” break in Rich Text format, press Enter so that you create a new line and the following four icons will appear:

Click on the last of the four (the one with the three dots).

You can read more about this here.

Keep in mind that the “read more” breaks work only in text-based posts (like this one); they do not work in photo-based posts. Use a concise descriptive sentence before you add the “read more” break so that readers can understand the topic of the post.

2. Use consistent tags.

Use the following tags when you write:

#assignments, #assignment_1, #assignment_2, #assignment_3, #assignment_4, #final_paper, #links, #your_current_team_name, etc., and generally any other tag that others can use too.

Since you will be switching teams throughout the semester, use a tag with your current team’s name in order to group posts according to each team.

3. Document your work for others

Post photos documenting in detail your work and your process. Others should be reading your posts and learn from you. Focus on what works and what does not work. Explain why. Don’t just post a photo with a one-liner. I will show more examples of what it means to document work. Until then, you can have a look at my own class webpage when I took How to Make (Almost) Anything class (not necessarily the best example) at MIT. Also make sure you have a look and explore the websites of other students, in more recent years, in that class, in how they document their work here, or more specifically:2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, …..Web documentation is graded.

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shskpadhy · 5 years ago

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Assignment-2.1 (Course-02 : Week-01)

Course – 02, Week - 01

Assignment – 2_1

Dataset : Gapminder

Variables under consideration :

· urbanrate (explanatory variable for both research questions) : it is collapsed into groups of [0-10], [10-20],…………[80-90] and [90-100].

· alcconsumption (responsive variable for 1st research question)

· lifeexpectancy (responsive variable for 2nd research question)

Redefining Research Questions and Related Hypothesis

Research Question – 01

The per capita alcohol consumption of a country depends on its urban rate.

H0 : For all groups of the urban-rate, the group-wise mean per-capita alcohol consumptions are equal

HA : The mean per-capita alcohol consumption of all the groups are not equal

Research Question – 02

The average life-expectancy of new born baby in a country depends on its urban rate.

H0 : For all groups of the urban-rate, the group-wise mean life expectancies are equal

HA : The mean life expectancies of all the groups are not equal

Code

# -*- coding: utf-8 -*-

"""

Created on Fri Jun 19 16:35:22 2020

@author: ASUS

"""

# Assignment_3

import pandas

import numpy

import seaborn

import matplotlib.pyplot as plt

import statsmodels.formula.api as smf

import statsmodels.stats.multicomp as multi

#importing data

data = pandas.read_csv('Dataset_gapminder.csv', low_memory=False)

#Set PANDAS to show all columns in DataFrame

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

#Set PANDAS to show all rows in DataFrame

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

# bug fix for display formats to avoid run time errors

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

#printing number of rows and columns

print ('Rows')

print (len(data))

print ('columns')

print (len(data.columns))

#------- Variables under consideration------#

# alcconsumption

# urbanrate

# lifeexpectancy

# Setting values to numeric

data['urbanrate'] = data['urbanrate'].convert_objects(convert_numeric=True)

data['alcconsumption'] = data['alcconsumption'].convert_objects(convert_numeric=True)

data['lifeexpectancy'] = data['lifeexpectancy'].convert_objects(convert_numeric=True)

data2 = data

# urbanrate

data2['urbgrps']=pandas.cut(data2.urbanrate,[0,10,20,30,40,50,60,70,80,90,100])

data2["urbgrps"] = data2["urbgrps"].astype('category')

# ------Analysis -------#

#------Urbgrps vs. alcconsumption---------#

print ('Urbgrps vs. alcconsumption')

#-----Descriptive analysis----#

print ('Descriptive data analysis : ')

print ('C->Q bar graph')

seaborn.factorplot(x='urbgrps', y='alcconsumption', data=data2, kind="bar", ci=None)

plt.ylabel('Per capita alcohol consumption in a year')

plt.title('Scatterplot for the Association Between Urban Rate and per-capita alcohol consumption')

print ('Seems H0 is to be rejected')

#----Unferential statistics-----#

print ('ANOVA-F test')

sub1_1 = data2[['alcconsumption', 'urbgrps']].dropna()

model1 = smf.ols(formula='alcconsumption ~ C(urbgrps)', data=sub1_1).fit()

print (model1.summary())

m1_1= sub1_1.groupby('urbgrps').mean()

print (m1_1)

#-----Post hoc----#

print ('Post-hoc test')

post_h_1 = multi.MultiComparison(sub1_1['alcconsumption'], sub1_1['urbgrps'])

res1 = post_h_1.tukeyhsd()

print(res1.summary())

# ------- urbgrps vs. lifexpectancy -------#

print ('urbgrps vs. lifexpectancy')

print ('Descriptive statistical analysis')

print ('C->Q bar graph')

seaborn.factorplot(x='urbgrps', y='lifeexpectancy', data=data2, kind="bar", ci=None)

plt.ylabel('Life Expectancy')

plt.title('Scatterplot for the Association Between Urban Rate and life expectancy')

#----Unferential statistics-----#

print ('ANOVA-F test')

sub1_2 = data2[['lifeexpectancy', 'urbgrps']].dropna()

model2 = smf.ols(formula='lifeexpectancy ~ C(urbgrps)', data=sub1_2).fit()

print (model2.summary())

m1_2= sub1_2.groupby('urbgrps').mean()

print (m1_2)

#-----Post hoc----#

print ('Post-hoc test')

post_h_2 = multi.MultiComparison(sub1_2['lifeexpectancy'], sub1_2['urbgrps'])

res2 = post_h_2.tukeyhsd()

print(res2.summary())

Output

Rows

213

columns

Urbgrps vs. alcconsumption

Descriptive data analysis :

C->Q bar graph

Seems H0 is to be rejected

ANOVA-F test

__main__:40: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use Series.infer_objects()

For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.

__main__:41: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use Series.infer_objects()

For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.

__main__:42: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use Series.infer_objects()

For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.

OLS Regression Results

==============================================================================

Dep. Variable: alcconsumption R-squared: 0.143

Model: OLS Adj. R-squared: 0.103

Method: Least Squares F-statistic: 3.625

Date: Tue, 07 Jul 2020 Prob (F-statistic): 0.000634

Time: 15:10:23 Log-Likelihood: -537.03

No. Observations: 183 AIC: 1092.

Df Residuals: 174 BIC: 1121.

Df Model: 8

Covariance Type: nonrobust

===================================================================================================================

coef std err t P>|t| [0.025 0.975]

-------------------------------------------------------------------------------------------------------------------

Intercept 5.8516 0.328 17.843 0.000 5.204 6.499

C(urbgrps)[T.Interval(10, 20, closed='right')] -0.5483 1.249 -0.439 0.661 -3.014 1.917

C(urbgrps)[T.Interval(20, 30, closed='right')] -1.9921 0.949 -2.100 0.037 -3.865 -0.119

C(urbgrps)[T.Interval(30, 40, closed='right')] -1.1990 0.930 -1.289 0.199 -3.035 0.637

C(urbgrps)[T.Interval(40, 50, closed='right')] 0.1864 0.990 0.188 0.851 -1.767 2.140

C(urbgrps)[T.Interval(50, 60, closed='right')] 1.6110 0.930 1.731 0.085 -0.225 3.447

C(urbgrps)[T.Interval(60, 70, closed='right')] 3.0216 0.819 3.691 0.000 1.406 4.637

C(urbgrps)[T.Interval(70, 80, closed='right')] 2.0307 0.949 2.140 0.034 0.158 3.903

C(urbgrps)[T.Interval(80, 90, closed='right')] 3.2649 0.990 3.299 0.001 1.312 5.218

C(urbgrps)[T.Interval(90, 100, closed='right')] -0.5236 1.361 -0.385 0.701 -3.209 2.162

==============================================================================

Omnibus: 8.172 Durbin-Watson: 1.832

Prob(Omnibus): 0.017 Jarque-Bera (JB): 8.039

Skew: 0.500 Prob(JB): 0.0180

Kurtosis: 3.233 Cond. No. 1.37e+16

==============================================================================

Warnings:

[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

[2] The smallest eigenvalue is 1.1e-30. This might indicate that there are

strong multicollinearity problems or that the design matrix is singular.

alcconsumption

urbgrps

(0, 10] nan

(10, 20] 5.303333

(20, 30] 3.859545

(30, 40] 4.652609

(40, 50] 6.038000

(50, 60] 7.462609

(60, 70] 8.873226

(70, 80] 7.882273

(80, 90] 9.116500

(90, 100] 5.328000

Post-hoc test

Multiple Comparison of Means - Tukey HSD, FWER=0.05

========================================================

group1 group2 meandiff p-adj lower ��upper reject

--------------------------------------------------------

(10, 20] (20, 30] -1.4438 0.9 -6.7067 3.8191 False

(10, 20] (30, 40] -0.6507 0.9 -5.8731 4.5716 False

(10, 20] (40, 50] 0.7347 0.9 -4.6203 6.0896 False

(10, 20] (50, 60] 2.1593 0.9 -3.0631 7.3816 False

(10, 20] (60, 70] 3.5699 0.381 -1.4161 8.5559 False

(10, 20] (70, 80] 2.5789 0.8146 -2.684 7.8418 False

(10, 20] (80, 90] 3.8132 0.389 -1.5418 9.1681 False

(10, 20] (90, 100] 0.0247 0.9 -6.2546 6.3039 False

(20, 30] (30, 40] 0.7931 0.9 -3.5803 5.1665 False

(20, 30] (40, 50] 2.1785 0.8319 -2.3525 6.7094 False

(20, 30] (50, 60] 3.6031 0.1993 -0.7703 7.9765 False

(20, 30] (60, 70] 5.0137 0.0051 0.9255 9.1019 True

(20, 30] (70, 80] 4.0227 0.1067 -0.399 8.4445 False

(20, 30] (80, 90] 5.257 0.0104 0.726 9.7879 True

(20, 30] (90, 100] 1.4685 0.9 -4.1246 7.0615 False

(30, 40] (40, 50] 1.3854 0.9 -3.0984 5.8692 False

(30, 40] (50, 60] 2.81 0.5145 -1.5145 7.1345 False

(30, 40] (60, 70] 4.2206 0.0329 0.1847 8.2565 True

(30, 40] (70, 80] 3.2297 0.3364 -1.1437 7.6031 False

(30, 40] (80, 90] 4.4639 0.052 -0.0199 8.9477 False

(30, 40] (90, 100] 0.6754 0.9 -4.8796 6.2304 False

(40, 50] (50, 60] 1.4246 0.9 -3.0592 5.9084 False

(40, 50] (60, 70] 2.8352 0.4672 -1.3709 7.0413 False

(40, 50] (70, 80] 1.8443 0.9 -2.6866 6.3752 False

(40, 50] (80, 90] 3.0785 0.4877 -1.559 7.716 False

(40, 50] (90, 100] -0.71 0.9 -6.3898 4.9698 False

(50, 60] (60, 70] 1.4106 0.9 -2.6253 5.4465 False

(50, 60] (70, 80] 0.4197 0.9 -3.9537 4.7931 False

(50, 60] (80, 90] 1.6539 0.9 -2.8299 6.1377 False

(50, 60] (90, 100] -2.1346 0.9 -7.6896 3.4204 False

(60, 70] (70, 80] -0.991 0.9 -5.0792 3.0973 False

(60, 70] (80, 90] 0.2433 0.9 -3.9628 4.4494 False

(60, 70] (90, 100] -3.5452 0.4859 -8.8786 1.7881 False

(70, 80] (80, 90] 1.2342 0.9 -3.2967 5.7651 False

(70, 80] (90, 100] -2.5543 0.8772 -8.1474 3.0388 False

(80, 90] (90, 100] -3.7885 0.4813 -9.4683 1.8913 False

urbgrps vs. lifexpectancy

Descriptive statistical analysis

C->Q bar graph

ANOVA-F test

OLS Regression Results

==============================================================================

Dep. Variable: lifeexpectancy R-squared: 0.406

Model: OLS Adj. R-squared: 0.380

Method: Least Squares F-statistic: 15.32

Date: Tue, 07 Jul 2020 Prob (F-statistic): 4.76e-17

Time: 15:25:26 Log-Likelihood: -644.54

No. Observations: 188 AIC: 1307.

Df Residuals: 179 BIC: 1336.

Df Model: 8

Covariance Type: nonrobust

===================================================================================================================

coef std err t P>|t| [0.025 0.975]

-------------------------------------------------------------------------------------------------------------------

Intercept 62.4438 0.521 119.810 0.000 61.415 63.472

C(urbgrps)[T.Interval(10, 20, closed='right')] -1.7713 2.041 -0.868 0.387 -5.800 2.257

C(urbgrps)[T.Interval(20, 30, closed='right')] 0.2834 1.548 0.183 0.855 -2.772 3.338

C(urbgrps)[T.Interval(30, 40, closed='right')] -1.7116 1.548 -1.106 0.270 -4.767 1.343

C(urbgrps)[T.Interval(40, 50, closed='right')] 4.5547 1.615 2.820 0.005 1.367 7.742

C(urbgrps)[T.Interval(50, 60, closed='right')] 7.0269 1.518 4.629 0.000 4.031 10.022

C(urbgrps)[T.Interval(60, 70, closed='right')] 10.4459 1.283 8.140 0.000 7.914 12.978

C(urbgrps)[T.Interval(70, 80, closed='right')] 13.4776 1.548 8.706 0.000 10.423 16.533

C(urbgrps)[T.Interval(80, 90, closed='right')] 13.9092 1.694 8.212 0.000 10.567 17.252

C(urbgrps)[T.Interval(90, 100, closed='right')] 16.2290 1.841 8.816 0.000 12.597 19.861

==============================================================================

Omnibus: 8.034 Durbin-Watson: 1.953

Prob(Omnibus): 0.018 Jarque-Bera (JB): 8.379

Skew: -0.515 Prob(JB): 0.0152

Kurtosis: 2.902 Cond. No. 7.24e+15

==============================================================================

Warnings:

[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

[2] The smallest eigenvalue is 4.02e-30. This might indicate that there are

strong multicollinearity problems or that the design matrix is singular.

lifeexpectancy

urbgrps

(0, 10] nan

(10, 20] 60.672500

(20, 30] 62.727136

(30, 40] 60.732182

(40, 50] 66.998450

(50, 60] 69.470652

(60, 70] 72.889647

(70, 80] 75.921364

(80, 90] 76.353000

(90, 100] 78.672733

Post-hoc test

Multiple Comparison of Means - Tukey HSD, FWER=0.05

=========================================================

group1 group2 meandiff p-adj lower upper reject

---------------------------------------------------------

(10, 20] (20, 30] 2.0546 0.9 -6.56 10.6693 False

(10, 20] (30, 40] 0.0597 0.9 -8.555 8.6743 False

(10, 20] (40, 50] 6.3259 0.3698 -2.4394 15.0913 False

(10, 20] (50, 60] 8.7982 0.0384 0.2499 17.3464 True

(10, 20] (60, 70] 12.2171 0.001 4.1569 20.2774 True

(10, 20] (70, 80] 15.2489 0.001 6.6342 23.8635 True

(10, 20] (80, 90] 15.6805 0.001 6.7344 24.6266 True

(10, 20] (90, 100] 18.0002 0.001 8.7032 27.2973 True

(20, 30] (30, 40] -1.995 0.9 -9.2327 5.2428 False

(20, 30] (40, 50] 4.2713 0.6536 -3.1452 11.6878 False

(20, 30] (50, 60] 6.7435 0.0826 -0.4151 13.9022 False

(20, 30] (60, 70] 10.1625 0.001 3.5944 16.7307 True

(20, 30] (70, 80] 13.1942 0.001 5.9565 20.432 True

(20, 30] (80, 90] 13.6259 0.001 5.9966 21.2551 True

(20, 30] (90, 100] 15.9456 0.001 7.9077 23.9835 True

(30, 40] (40, 50] 6.2663 0.1729 -1.1502 13.6828 False

(30, 40] (50, 60] 8.7385 0.0054 1.5798 15.8971 True

(30, 40] (60, 70] 12.1575 0.001 5.5893 18.7256 True

(30, 40] (70, 80] 15.1892 0.001 7.9514 22.4269 True

(30, 40] (80, 90] 15.6208 0.001 7.9916 23.2501 True

(30, 40] (90, 100] 17.9406 0.001 9.9026 25.9785 True

(40, 50] (50, 60] 2.4722 0.9 -4.8671 9.8115 False

(40, 50] (60, 70] 5.8912 0.1431 -0.8734 12.6558 False

(40, 50] (70, 80] 8.9229 0.0065 1.5064 16.3394 True

(40, 50] (80, 90] 9.3546 0.0068 1.5555 17.1536 True

(40, 50] (90, 100] 11.6743 0.001 3.475 19.8735 True

(50, 60] (60, 70] 3.419 0.7444 -3.0619 9.8999 False

(50, 60] (70, 80] 6.4507 0.1144 -0.7079 13.6094 False

(50, 60] (80, 90] 6.8823 0.1057 -0.6719 14.4366 False

(50, 60] (90, 100] 9.2021 0.011 1.2353 17.1689 True

(60, 70] (70, 80] 3.0317 0.8683 -3.5364 9.5999 False

(60, 70] (80, 90] 3.4634 0.8057 -3.5339 10.4606 False

(60, 70] (90, 100] 5.7831 0.2689 -1.6576 13.2238 False

(70, 80] (80, 90] 0.4316 0.9 -7.1976 8.0609 False

(70, 80] (90, 100] 2.7514 0.9 -5.2866 10.7893 False

(80, 90] (90, 100] 2.3197 0.9 -6.0725 10.7119 False

---------------------------------------------------------

Result

Research Question 01

We see that F-value of the ANOVA-F test is 3.625 and the corresponding p-value is 0.000634, which is less than 0.05. Thus the chance of wrongly rejecting the null hypothesis (H0) is very less. Hence we can reject the null hypothesis and accept the alternate hypothesis, i.e. we conclude that alcconsumption depends on urban-rate.

From results of the post-hoc test, we see that the mean per-capita alcohol consumption is unequal for the following groups:

· [20-30] and [60-70]

· [30-40] and [60-70]

· [20-30] and [80-90]

Research Question 02

We see that F-value of the ANOVA-F test is 15.32 and the corresponding p-value is 4.76e-17, which is less than 0.05. Thus the chance of wrongly rejecting the null hypothesis (H0) is very less. Hence we can reject the null hypothesis and accept the alternate hypothesis, i.e. we conclude that lfe-expectancy depends on urban-rate.

From results of the post-hoc test, we see that the mean per-capita alcohol consumption is unequal for the following groups:

· [50-60] and [90-100]

· [20-30] and [90-100]

· [20-30] and [80-90]

· And many more

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awesomesriram-blog1 · 6 years ago

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Assignment_3 Income with consumption of alcohol correlation coefficient

incomeperperson and alcconsumption, the correlation coefficient (r) is approximately 0.30 with a p-value of 0.0001.

relationship is statistically significant.

weak and positive

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connective-environments-fall2019 · 6 years ago

Text

Welcome

Welcome to the Connective-Environments Fall 2019 blog. We will be using this blog in addition to the Instagram accounts as the main repository to document the awesome things you will be designing and to document your progress.

In order to post in this blog, you will need to join the group through the invitation you received, and create your account if you don’t have one yet. Couple rules of good practice:

1. Keep the blog’s wall neat.

To add a “read more” break in Rich Text format, press Enter so that you create a new line and the following four icons will appear:

Click on the last of the four (the one with the three dots).

You can read more about this here.

2. Use consistent tags.

Use the following tags when you write:

#assignments, #assignment_1, #assignment_2, #assignment_3, #assignment_4, #final_paper, #links, #your_current_team_name, etc., and generally any other tag that others can use too.

Since you will be switching teams throughout the semester, use a tag with your current team’s name in order to group posts according to each team.

3. Document your work for others

#announcements #dimitris papanikolaou

0 notes

courserarajat · 5 years ago

Text

Week 3

https://github.com/drogova/ml-da_wesleyan_university/blob/master/assignment_3.ipynb

0 notes

connective-environments-fall2020 · 4 years ago

Text

Assignment 3: Research

The main feature of my project is the concept of audio visualization. There are many ways to go about the process of visualizing audio, so I feel that its important to understand them, therefore one can choose to most successful method.

Audio Equalization

One of the most common types of audio visualization is the equalizer. This type of visualization comes as a result of a tool used in music production. The basic premise is to take in the frequency of sound and separate it into linear “bands’. The purpose being to adjust the amplitude of audio signals at particular frequencies.

Spectrogram

The spectrogram is a visual representation of the range of frequencies of a signal as it varies over time. This mostly takes the form of a graph with two axis, one representing time with the other representing frequency, the third measured dimension of amplitude is represented by the color of the graph. This description is rather loose, meaning that by just changing the axis or using either 2D or 3D display has the potential to change the representation in many ways.

Milkdrop

Milkdrop is a program that creates a constantly shifting visual environment that reacts to audio input by utilizing beat detection and interpellation. By cycling through multiple presets, the environment created is always in a state of flux.

#assignment_3 #spencer_bauer

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essayyard-blog · 8 years ago

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HRM 500. Original Work Only. No Plagiarism.!!!!!!

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aniquevv-blog · 10 years ago

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Ja of Nee?

Het is weer bijna oktober en dat betekent dat de Donorweek er weer aan komt. Dit jaar zal hij plaats vinden van 12 tot en met 18 oktober. De Donorweek is onderdeel van JAofNEE.nl en is een campagne om het donorschap onder de aandacht te brengen en daarmee meer donoren te werven. De campagne heeft letterlijk als doel “mensen ertoe te bewegen om elkaar, maar eigenlijk vooral zichzelf de vraag te stellen of zij al donor zijn”(Rijksoverheid, 2014). Hiermee wordt er geprobeerd om meer mensen ertoe te brengen zich als donor te registreren. Tijdens de Donorweek hebben 46.205 personen zich geregistreerd, waarvan 81% donor wil zijn. Daarmee is de doelstelling om een stijging tijdens de campagne te behalen van de hoeveelheid mensen dat zich registreert als donor geslaagd (Rijksoverheid, 2014).

De Donorweek en JAofNEE.nl zijn beiden een initiatief van de overheid en het richt zich op Nederlanders van 18 jaar en ouder die (nog) niet geregistreerd zijn en geen principiële bezwaren hebben tegen orgaandonatie. De campagne richt zich secundair op het algemeen publiek van 18 jaar en ouder. Een groot gedeelte van de secundaire doelgroep is tijdens de Donorweek van 2014 bereikt, namelijk 80% (Rijksoverheid, 2014). Zo’n groot gedeelte wordt bereikt, aangezien er veel verschillende media voor de campagne worden gebruikt, zoals tv- en radiospotjes, banners, websites en sociale media (Facebook, Twitter, Instagram, Linkedin, Youtube en Google+). Doordat er zoveel (sociale) media wordt gebruikt, zullen er ook mensen buiten de doelgroep, onder de 18 jaar, worden bereikt. Dat ook deze kinderen worden bereikt is niet zo heel erg, want iedereen kan zich vanaf 12 jaar als donor registreren. De primaire doelgroep is in mindere mate bereikt dan de secundaire doelgroep, 72% van de niet-geregistreerde Nederlanders van 18 jaar of ouder is bereikt (Rijksoverheid, 2014). Daarnaast wordt campagne door de niet-geregistreerden lager beoordeeld dan gemiddeld, met een 6.9. De Rijksoverheid (2014) denkt dat dit komt doordat deze groep niet geïnteresseerd is in het onderwerp en hierdoor lastiger is te bereiken. Daarnaast kunnen deze mensen de boodschap al te vaak hebben gezien, het wordt al sinds 2008 onder de aandacht gebracht, waardoor ze ongevoelig zijn geworden voor de boodschap. Dit laatste fenomeen noemen Cho & Salmon (2007) desensibilisatie, waarbij het publiek dus onverschillig wordt voor de boodschap.

Mijn blog heeft natuurlijk als hoofdonderwerp sociale media, waardoor ik ook het sociale media gebruik van de campagne zal bekijken. Er worden helaas niet veel, of in elk geval te weinig, niet-geregistreerde mensen bereikt via sociale media. Aan de hand van een nameting is te zien dat na de Donorweek alleen via Facebook een toename is van het aandeel niet-geregistreerden dat iets over orgaandonatie heeft gezien. Op Twitter, Instagram en Linkedin is een niet significante stijging te zien(Rijksoverheid, 2014).

Doordat er weinig mensen bereikt worden via sociale media, zou ik de bedenkers van de JAofNEE campagne het advies willen geven om zich hier meer op te richten. Daarnaast denk ik dat ze zich meer op de jongere niet-geregistreerden moet focussen, aangezien de meeste oudere niet-geregistreerden al ongevoelig zijn geraakt zoals ik hierboven al beschreef. Deze twee adviezen vallen erg goed samen, aangezien veel jongeren op sociale media zijn terug te vinden. Daarnaast heb ik nog een aantal adviezen hoe de sociale ingezet kunnen worden tijdens de campagne. Er zal onder andere gebruik gemaakt moeten worden van social proof. Dit fenomeen noemt Cialdini (2001) als een van zijn zes principes van beïnvloeding en het wijst naar de neiging om een actie als meer geschikt te zien wanneer anderen het ook doen. Deze neiging bestaat helemaal als we onzeker zijn over een beslissing. Daarom zou er tijdens de campagne duidelijk gemaakt moeten worden hoeveel mensen er al zijn geregistreerd (tijdens de Donorweek). Daarnaast zou er een optie op Facebook en Linkedin moeten komen waarbij mensen kunnen aangeven dat ze donor zijn, zoals dit vroeger ook op Hyves het geval was tijdens de Donorweek. Wat ze op het moment wel al goed doen is dat bekende mensen in de Donorweek hun verhaal vertellen rondom het doneren van hun organen.

Ook zou er tijdens de campagne nog een principe van Cialdini (2001) gebruikt moeten worden, namelijk liking. Dit betekent dat we sneller worden beïnvloed worden door mensen die we leuk vinden of die gelijk aan ons zijn. Daarom zouden de makers van de JAofNEE campagne aangrijpende verhalen moeten delen op Instagram en Facebook van mensen die een donor zoeken, dit zouden dan bijvoorbeeld jongeren moeten zijn waarmee andere jongeren zich kunnen identificeren.

Als deze adviezen in de aankomende campagne worden toegepast, dan zal het bereik via sociale media stijgen en daarmee ook het aantal donoren.

Bronnen:

Cialdini, R. B. (2001). Harnessing the science of persuasion. Harvard Business Review, 79(9), 72-81.

Cho, H. & Salmon, C. T. (2007). Unintended effects of health communication campaigns. Journal of Communication, 57, 293-317

Rijksoverheid (2014). Orgaandonatie 2014 (P02) - Eindrapportage campagne-effectonderzoek. Geraadpleegd op file:///C:/Users/HP/Downloads/rapport%20ja%20nee%20campagne.pdf

#assignment_3 #social_media #ja_of_nee #donor

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connective-environments-fall2020 · 5 years ago

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Assignment 3: Precedent

In doing research for the start of this assignment, I've come across a few examples that I feel might be of some use. In this post I will go into detail on some of them.

Project 1: Reactive Sparks

youtube

This is a project located in Germany, it consists of a series of screens situated along a popular roadway. By using camera tracking, the screens are able to display the movement energy of passing cars visually as horizontal lines on the screen. As the car moves past it individually triggers each screen in sequence. The light produced by the strips is dependent on the amount of cars on the road, meaning that early mornings with minimal traffic will produce a dim light, while the high traffic of the afternoon and night will produce a bright light.

Project 2: In Order to Control

vimeo

This project consists of two parts, a projection on the ground which is meant to be interrupted by the participants, and a projection on the wall that displays the interruption of the participants. The projection takes the form of a series of text that is a discussion of ethics and morality. This allows it to combine an interactive experience with a deeper understanding of the meaning behind the art.

Project 3: Natures Rhythm

youtube

This project is part of a larger installation called the Digital Light Canvas, which is a large scale screen and sculptural piece situated within a public space. This projects consists of a collection of lights acting as if they are fluid, these lights can be impacted by the movement of the people through the space. This movement is also translated to the hanging sculptural piece in the form of color changing lights, this provides opportunities for people adjacent to the space to read the movement of people moving through the space.

#Assignment_3 #Spencer_Bauer

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connective-environments-fall2020 · 5 years ago

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Welcome

Welcome to the Connective-Environments Fall 2019 blog. We will be using this blog

as the main repository to document the awesome things you will be designing and to document your progress.

In order to post in this blog, you will need to join the group through the invitation you received, and create your account if you don’t have one yet. Couple rules of good practice:

1. Keep the blog’s wall neat.

To add a “read more” break in Rich Text format, press Enter so that you create a new line and the following four icons will appear:

Click on the last of the four (the one with the three dots).

You can read more about this here.

2. Use consistent tags.

Use the following tags when you write:

#assignments, #assignment_1, #assignment_2, #assignment_3, #assignment_4, #final_paper, #links, #your_current_team_name, etc., and generally any other tag that others can use too.

Since you will be switching teams throughout the semester, use a tag with your current team’s name in order to group posts according to each team.

3. Document your work for others

#announcements #dimitris_papanikolaou

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shskpadhy · 5 years ago

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Assignment_3

Week 03

Making Data Management Decissions

Dataset : GapMinder

Variables chosen : ‘alcconsumption’, ‘urbanrate’ and ‘lofeexpectancy’

Program

1. # Assignment_3

4. import pandas

5. import numpy

7. #importing data

8. data = pandas.read_csv('Dataset_gapminder.csv', low_memory=False)

10. #printing number of rows and columns

11. print ('Rows')

12. print (len(data))

13. print ('columns')

14. print (len(data.columns))

15.

16. #------- Variables under consideration------#

17. # alcconsumption

18. # urbanrate

19. # lifeexpectancy

20.

21. # Setting values to numeric

22. data['urbanrate'] = data['urbanrate'].convert_objects(convert_numeric=True)

23. data['alcconsumption'] = data['alcconsumption'].convert_objects(convert_numeric=True)

24. data['lifeexpectancy'] = data['lifeexpectancy'].convert_objects(convert_numeric=True)

25. data2 = data

26.

27. # alconsumption

28.

29. # initial F.D.

30. print ('alcconsumption : alcohol consumption per adult (age 15+) in litres')

31. c1_min = data['alcconsumption'].min()

32. c1_max = data['alcconsumption'].max()

33. print ('min and max value of alcconsumption : ')

34. print (c1_min)

35. print (c1_max)

36. # Step 1 : Setting aside missing data (not required)

37. # Step 2 : coding missing data (not required)

38. # Step 3 : creating secondary variables (not required)

39. # Step 4 : Grouping Values within individual variables

40. data2['alcgrps']=pandas.cut(data2.alcconsumption,[0,2.5,5.0,7.5,10.0,12.5,15.0,17.5,20.0,22.5, 25])

41. print ('F.D. of groups of values of variable alcconsumption :')

42. c1_grp = data2['alcgrps'].value_counts(sort=False, dropna=False)

43. print (c1_grp)

44. print ('Percentage (with NaN set aside) :')

45. p1_grp = data2['alcgrps'].value_counts(sort=False, dropna=True, normalize=True)

46. print (p1_grp)

47.

48. # urbanrate

49.

50. # Initial F.D.

51. print ('---------------------------')

52. print ('urbanrate : Percentage of population living in urban areas')

53. c2_min = data2['urbanrate'].min()

54. c2_max = data2['urbanrate'].max()

55. print ('min and max values : ')

56. print (c2_min)

57. print (c2_max)

58. data2['urbgrps']=pandas.cut(data2.urbanrate,[0,10,20,30,40,50,60,70,80,90,100])

59. c2_grp = data2['urbgrps'].value_counts(sort=False, dropna=False)

60. print ('F.D. of groups of values of variable urbanrate :')

61. print (c2_grp)

62. print ('Percentage (with NaN set aside) : ')

63. p2_grp = data2['urbgrps'].value_counts(sort=False, dropna=True, normalize=True)

64. print (p2_grp)

65.

66. # lifeexpectancy

67.

68. # Initial F.D.

69. print ('---------------------------')

70. print ('lifeexpectancy : years in avg a new born baby would live in current situation')

71. c3_min = data2['lifeexpectancy'].min()

72. c3_max = data2['lifeexpectancy'].max()

73. print ('min and max values : ')

74. print (c3_min)

75. print (c3_max)

76. data2['lifgrps']=pandas.cut(data2.lifeexpectancy,[0,50,60,70,80,90,100])

77. c3_grp = data2['lifgrps'].value_counts(sort=False, dropna=False)

78. print ('F.D. of groups of values of variable lifeexpectancy :')

79. print (c3_grp)

80. print ('Percentage (with NaN set aside) : ')

81. p3_grp = data2['lifgrps'].value_counts(sort=False, dropna=True, normalize=True)

82. print (p3_grp)

Output

Rows

213

columns

alcconsumption : alcohol consumption per adult (age 15+) in litres

min and max value of alcconsumption :

0.03

23.01

F.D. of groups of values of variable alcconsumption :

(0.0, 2.5] 45

(2.5, 5.0] 36

(5.0, 7.5] 31

(7.5, 10.0] 30

(10.0, 12.5] 21

(12.5, 15.0] 13

(15.0, 17.5] 8

(17.5, 20.0] 2

(20.0, 22.5] 0

(22.5, 25.0] 1

NaN 26

Name: alcgrps, dtype: int64

Percentage (with NaN set aside) :

(0.0, 2.5] 0.240642

(2.5, 5.0] 0.192513

(5.0, 7.5] 0.165775

(7.5, 10.0] 0.160428

(10.0, 12.5] 0.112299

(12.5, 15.0] 0.069519

(15.0, 17.5] 0.042781

(17.5, 20.0] 0.010695

(20.0, 22.5] 0.000000

(22.5, 25.0] 0.005348

Name: alcgrps, dtype: float64

---------------------------

urbanrate : Percentage of population living in urban areas

min and max values :

10.4

100.0

F.D. of groups of values of variable urbanrate :

(0.0, 10.0] 0

(10.0, 20.0] 13

(20.0, 30.0] 22

(30.0, 40.0] 24

(40.0, 50.0] 22

(50.0, 60.0] 24

(60.0, 70.0] 34

(70.0, 80.0] 24

(80.0, 90.0] 21

(90.0, 100.0] 19

NaN 10

Name: urbgrps, dtype: int64

Percentage (with NaN set aside) :

(0, 10] 0.000000

(10, 20] 0.064039

(20, 30] 0.108374

(30, 40] 0.118227

(40, 50] 0.108374

(50, 60] 0.118227

(60, 70] 0.167488

(70, 80] 0.118227

(80, 90] 0.103448

(90, 100] 0.093596

Name: urbgrps, dtype: float64

---------------------------

lifeexpectancy : years in avg a new born baby would live in current situation

min and max values :

47.794

83.39399999999999

F.D. of groups of values of variable lifeexpectancy :

(0.0, 50.0] 9

(50.0, 60.0] 29

(60.0, 70.0] 38

(70.0, 80.0] 92

(80.0, 90.0] 23

(90.0, 100.0] 0

NaN 22

Name: lifgrps, dtype: int64

Percentage (with NaN set aside) :

(0, 50] 0.047120

(50, 60] 0.151832

(60, 70] 0.198953

(70, 80] 0.481675

(80, 90] 0.120419

(90, 100] 0.000000

Name: lifgrps, dtype: float64

Description

1. The values of the variables were grouped and F.D. (Frequency Distribution) of them are provided in output.

2. Frequency distributions:

a. alcconsumption : There are 26 unknown (NaN) values. The number (distribution) of countries (individuals) goes on decreasing consistently as the per capita alcohol consumption increases.

b. urbanrate : There are 10 missing (NaN) values. No country has less tan 10% urbanrate and 9.3596% countries have urban rate greater than 90% and less than or equal to 100%. The F.D. does not follow a consistent (increasing or decreasing) curve as alcconsumption.

c. lifeexpectancy : There are 22 missing values. 4% countries have lifeexpectancy less than and equal to 50%. The number of countries increases as the lifeexpectancy increases util 70-80 years. Then the number of countries distributed in intervals with increasing values of ifeexpectancy goes on decreasing.

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