#InternetUsage

[softwaresupdate]

This infographic will help the users get complete information on the perfect internet usage policy. To know more, you can visit: https://empmonitor.com/blog/internet-usage-policy/

[technest360]

Desktop Vs Mobile internet usage www.youtube.com/technest360 . . . @technest360 @technest360 @technest360 . . . #technest360 #arjun_mavnoor #tech #technology #youtube #youtuber #techyoutuber #twitter #instagram #instagrammer #techinhindi #india #techie #indianyoutuber #technews #desktop #desktopinternet #internetusage #usage #mobileusage #mobileinternetusage https://www.instagram.com/p/CcHb_79JRXC/?igshid=NGJjMDIxMWI=

[digivishnu]

सुप्रभात ..... #marathi #oneminute #maharashtra_business #marathiboy #maharashtra #maharashtrian #internetusage #marathimulga #marathimulgi #marathisuvichar #punekar #facebookstatistics #marathiculture #youtubevideoa #instagramstories #yavatmal #businessmajha #facebookmessages #oneminuteoninternet #oneminutecover #zoommeeting #udyojak #marathibusinessman #maharashtrian #mimarathi #marathiinspirations #pune #marathimanus https://www.instagram.com/p/CO_49-dlYQw/?utm_medium=tumblr

[computermanit]

A change in working practices, and an increase in internet usage may mean your internet speeds and quota are no longer suitable. Making a quick phone call to your internet provider to see if you can increase your internet speed may relieve the frustration of slow internet connections.

[rapidit]

Internet Explorer comes standard with PCs but these days there are other browsers that allow you to browse the internet at a faster speed. Consider downloading Chrome or Firefox to your desktop for optimised internet usage.

[nova-idea-marketing]

A mobilos látogatók átveszik az uralmat a web felett?

[techrest1]

5G Impact Over 4G 🔥🔥🔥

[rodriguefouafou]

#internetusage #statistics #africaopportunities. Internet growth in Africa is 10,000% since the year 2000 and it’s still only 35% penetration. 219% growth in North America with 95% penetration since 2000

[surveycircle]

Tweeted

Participants needed for online survey! Topic: "Cyberbullying and cybervictimization experiences" https://t.co/LSw4sBPuHU via @SurveyCircle #InternetUsage #SelfEsteem #PerceivedSocialSupport #cybervictimization #survey #surveycircle https://t.co/L9H7hd5xWo

— Daily Research @SurveyCircle (@daily_research) Apr 11, 2023

[digivishnu]

#marathi #oneminute #maharashtra_business #marathiboy #maharashtra #maharashtrian #internetusage #marathimulga #marathimulgi #marathisuvichar #punekar #facebookstatistics #marathiculture #youtubevideoa #instagramstories #yavatmal #businessmajha #facebookmessages #oneminuteoninternet #oneminutecover #zoommeeting #udyojak #marathibusinessman #maharashtrian #mimarathi #marathiinspirations #pune #marathimanus (at Mumbai, Maharashtra) https://www.instagram.com/p/CNq4xcXlptD/?igshid=1lcmo07p2hlca

[computermanit]

A change in working practices, and an increase in internet usage may mean your internet speeds and quota are no longer suitable. Making a quick phone call to your internet provider to see if you can increase your internet speed may relieve the frustration of slow internet connections.

[rapidit]

Internet Explorer comes standard with PCs but these days there are other browsers that allow you to browse the internet at a faster speed. Consider downloading Chrome or Firefox to your desktop for optimised internet usage.  If you need any help, we offer 24 hour computer support, so don’t hesitate to get in touch.

[data-analytical]

K-Means Cluster Analysis

I really didn't enjoy this assignment. It is so involved and there's so little data in my dataset, it is not a terribly good example of the method in action. Also, the sample script doesn't work out of the box, so it was hard initially, to run and compare. Here goes...

I ran a K-Means Cluster Analysis to look at underlying sub-groups of countries based on their similar responses through the following variables:

polityscore

incomeperperson

alcconsumption

breastcancerper100th

co2emissions

employrate

internetuserate

oilperperson

relectricperperson

suicideper100th

urbanrate

All clustering variables were standardized to have a mean of 0 and a standard deviation of 1.

I did NOT use the SurveySelect Procedure, to split out test/training data, because my initial dataset is only 56 rows (after missing data deletion).

A series of k-means cluster analyses were conducted on the training data specifying k=1-9 clusters, using Euclidean distance. The variance in the clustering variables that was accounted for by the clusters (r-square) was plotted for each of the nine cluster solutions in an elbow curve to provide guidance for choosing the number of clusters to interpret.

Figure 1. Elbow curve of r-square values for the nine cluster solutions.

The elbow curve was very inconclusive, suggesting that the 2, 3, 5 and 6-cluster solutions might be interpreted. Keeping in mind that I have very little data to cluster (so I cannot be certain about anything), I opted to investigate the 3-cluster solution.

I ran Canonical Discriminant Analysis reducing my 11 variables to a more manageable number.

Figure 2. Plot of the first two canonical variables for the clustering variables by cluster. Means procedure.

The lack of data reveals itself in the chart. Cluster 2 only appears to have contained only 2 data points! However, clusters 1 and 3 did show a degree of clustering. Cluster 1 was more densely packed than 3, but clearly, both displayed variance within themselves. Both clusters appeared to be distinct. That said, results could be very different, if I had more data to work with!

Because low amount of observations, I feel that a two cluster analysis would be the most effective in this case. However, because cluster 2 has almost no data within it, I am forced to use 3.

The Clustering Means Analysis on the 3 cluster solution shows the following highlights:

Cluster1

Very low (negative) levels of income, alcoholconsumptionm breastcancer, internetusage, oilperperson, retailelectricperperson and urbanization.

Very high levels of employment.

Cluster2

Omitted - because of the low amount of data in the cluster

Cluster3

Very low (negative) levels of income, alcoholconsumptionm breastcancer, internetusage, oilperperson, retailelectricperperson and urbanization.

Very high levels of incomepeperson, breastcancerper100th, co2 emission, electricity.

Moderate to low levels of political score, alcoholconsumption and suicide.

Figure 3. Means Chart.

Whilst, once again it would be dangerous to draw too many conclusions from such a low dataset, Cluster 3 seems to be a little more comparable to a developed country, perhaps with better medical care (or access to it), which might extend the average persons life.

In order to externally validate the clusters, an Analysis of Variance (ANOVA) was conducting to test for significant differences between the clusters on lifeexpectancy.

Figure 4. ANOVA Chart and tables.

I used a tukey test used for post hoc comparisons between the clusters. Results indicated significant differences between the clusters on lifeexpectancy (F(2, 53)=17.21, p<.0001).

Countries in cluster 3 had the highest lifeexpectancy (mean=2.36, sd=0.78), and cluster 1 had the lowest (mean=2.67, sd=0.76).

Here is the code that I used to produce this analysis...

If I had more data, I could probably use this model with more confidence. Because the model tends to expect uniform and more sizable clusters of data (and my data clearly was not), I would be unhappy making any major decisions, based upon this analysis.

[clickify]

Latest Global Internet Usage Statistics and Trends for 2020

What are the Latest Global Internet Usage Statistics and Trends for 2020? 📈 - https://bit.ly/internetUsage

[mrdhital]

Creating Graphs for your data

For this Assignment, I used the same gapminder dataset i have been using throughout the duration of this course; as well continued visualization on the same variables for which the frequency distribution was Analyzed last week. 

Below is the program used for visualization and inferences.

Below is the bar graph for the internetuserate for the countries in the gapminder dataset. We can see that the distrubution is right skewed which tells us that the frequency of countries with higher internetusepercent is low as it falls towards the tail.

Below is the bar graph for the secondary variable created named Income_alc_ratio which gives us the ratio between the alcohol consumption and income. We can see the distrubution is also right skewed which tells us that above described ratio is away from the mean and hence the proportional relationship between the alcohol_consumption and income is very weak. 

Finally, association between two variables is explored throught bivariate analysis of internetusage rate and polity score of the country in the gapminder dataset. Resulting scatter plot does not have data points along the linear line and hence does not establish postive or negative relationship between the two variables.