Before completing the Tasks, please work through each of the sub-pages for Tutorial 2 as these will help complete the task below.

For the task below we will be using the following dataset:

Skoczylis, Joshua, 2021, "Extremism, Life Experiences and the Internet", https://doi.org/10.7910/DVN/ICTI8T, Harvard Dataverse, Version 3.

You will also use the Twitter dataset you have created.

Select the Strain_Resilient_score and generate a table with the following statistics:

• The Mean

• The Median

• Standard Deviation 

• the Quartiles and IQR

In addition to the above generate a Boxplot and a histogram

Once you have done the above, see how the statistics change when you split the data by Gender.

Select the Political_View_Race_supior variable and get the relevant statistics.

• What type of variable is this? 

• Get the relevant measures of central tendency and measures of dispersion.

Now generate a relevant graph (feel free to use Survey Plot, if applicable)

Once you have done the above, see split this variable using the Living_Arrangments variable. Generate a graph to visually display the data.

For this task, use the Twitter dataset you created in the last tutorial. Select the Follower and Tweets variables and generate a table with all of the relevant descriptive statistics.

Now use compute/transform to create a new variable to convert each of the two variables (Follower and Tweets) into z-scores.

• Select a random number from the Followers variable - what is the corresponding z-score? 

• Select a random number from the Tweets variable - what is the corresponding z-score? 

Using the z-scores from above find out how what the likelihood is that others in your data having the same amount of followers/send the same amount of tweets.

Use either this table here or the distrACTION module in Jamovi (all you have to do is add the Mean, and the SD into the fields and your follower/Tweets as x1). Learn how to use dist