To be sure, smartphones, for example, are designed with elements like push notifications to hold the attention of users. However, users can easily adjust these settings, and they are hardly an innovation of modern technology (books often end chapters mid-scene for the same reason).
A BOOK IS NOT AN ALGORITHM. A CLIFF HANGER IN A BOOK IS NOT VIBRATING AGAINST YOUR SKIN WHEN YOU ARE MOST LIKELY TO HAVE POOR COGNITIVE RESISTANCE USING INFORMATION FROM SENSOR DATA. THIS IS AN INSANE COMPARISON.
And certainly not in ways that happen to coincidentally flatter people’s preexisting moral conceits.
Framing "People are concerned about smart phone usage" as a "moral conceit" is a such a manipulative rhetorical technique. Acting like the entire realm of concern is prudish pearl clutching, and not based on any real world experience, is so fucking offensive. Writing as if this topic is entirely one sided and without nuances to consider is fucking insane.
explainer
A "neuroscience" (substack) article that constantly states things definitively without providing any support. Would not want this ideologically skewed blowhard anywhere near my brain.
Technology, such as video games or social media, simply doesn’t influence dopamine receptors the way illicit substances do.
This is a a really strange standard to require. "Media technology effects the brain differently than substances that are ingested or injected" is a sentence so stupidly obvious. You're saying absolutely nothing and framing it as revelatory. Embarrasing
confirmed in humans:
Excessive playing of computer games like some other behaviors could lead to addiction. Addictive behaviors may induce their reinforcing effects through stimulation of the brain dopaminergic mesolimbic pathway. The status of dopamine receptors in the brain may be parallel to their homologous receptors in peripheral blood lymphocytes (PBLs). Here, we have investigated the mRNA expression of dopamine D3, D4 and D5 receptors in PBLs of computer game addicts (n = 20) in comparison to normal subjects (n = 20), using a real-time PCR method. The results showed that the expression level of D3 and D4 dopamine receptors in computer game addicts were not statistically different from the control group. However, the expression of the mRNA of D5 dopamine receptor was significantly down-regulated in PBLs of computer game addicts and reached 0.42 the amount of the control group. It is concluded that unlike with drug addiction, the expression levels of the D3 and D4 dopamine receptors in computer game addicts are not altered compared to the control group. However, reduced level of the D5 dopamine receptor in computer game addicts may serve as a peripheral marker in studies where the confounding effects of abused drugs are unwanted.
You might have noticed another big problem, too: the period of the hospitalisations (2001-13) hardly covers any of the “smartphone era” from 2010 onwards that people are worried about, and contains a lot of data from before modern social media was even a glint in Mark Zuckerberg’s eye.
The ideological bias in this article is so annoying. You're perfectly happy to cite a study used in the early days of facebook because statistical correction wiped out significance in that study, but when a different study from the same period of time finds an effect it's responsible and important to discredit the findings on those grounds.
, ad.subaccount3 AS pacing , ad.subaccount4 AS cohort_name
For software engineering prework students, ad.subaccount3 is returning the cohort name. ad.subaccount4 is returning null.
in this repo
this bug me
our
your
s
remove the s
the
typo
rating
rated
largly
typo
different
difference
to
be
rating
rated
# Set label to the string "Below"
this is missing on the master branch
append
appended
to
typo
restauransts
typo
value
values
rating
rated
# Set the
add else hint
# Set
add else hint
# Import matplotlib import matplotlib.pyplot as plt
cut
review ~100 more
have ~100 more
mean
abv_avg_rev_center
Above Average Rating Restaurants.
restaurants with an above average rating
Below Average Rating Restaurants
restaurants with a below average rating
above
below
they
the
ratings
review counts
ratings
review counts
Calculate the mean review count for above average restaurants.
calculate mean review counts for both groups
for
cut
observation_type = None # How many observations are there num_observations = None # Isolate the first observation first_observation = None
move first observation before oberservation type
Affinity Channels to Join
Can we add some language clarifying what an "Affinity" channel is?
+
Can we change the + to a comma?
flatiron-school.slack.com Slack workspace.
Visually, this looks confusing...
Can we change this to a hyper link, which just the name of workspace as the visible text?
New Instructions for Logging Into GitHub
Missing logo
Slack Info
Missing logo
axi-staging-repo
axi-da-project2
Identify and remove outliers, then redo the analysis
Swap bullets 1 and 2
statsmodels
statsmodels
making our baseline model without any additional transformation.
creating a simple linear regression model.
from sklearn.metrics import r2_score, mean_squared_error
Add # Run cell without changes
that was calculated in the previous task.
Now that you've calculate the mean of list_price, create a list with the same length as the list_price column where every value in the list is the calculated mean.
the list
this new list
interval
*intervals
method
*function
Loading the Data
Add connectors in ERD diagram
Level Up: Project Enhancements
Add a bullet point for "Log the target variable and see if that improves the model assumptions."
models
I think this is meant as "Identify lego sets with high or low value"
# Replace None with appropriate text
Change to a markdown answer
Before we can make a final assessment of our model, we need to compare its metrics with the baseline model created in step one, and we need to check the assumptions of linear regression.
Now that the model parameters have been interpreted, the model must be assessed based on predictive metrics and whether or not the model is meeting the assumptions of linear regression.
Once the model has been fit, the coefficient for the intercept, the independent variable, p-values, and the coefficient confidence intervals should be interpeted. We should ask ourselves whether or not the relationship our model is finding seems plausible.
Now that the model has been fit, you should interpret the model parameters.
Specifically: