Discussion on the Prognosis of Cryptocurrency

Quantitative Social Science Using R: Text as Data, Geocoded Data, Network Data, or Data Suitable for Linear Regression
For this assessment, you will develop a research question and investigate it using data containing measures suitable for quantitative text analysis, spatial analysis, social network analysis, or linear regression. You are not required to use all of these methods; please select one (or more) appropriate methods to address your research question given the data you have chosen. You must do some essential things described below, but you are free to explore and analyse the data as you see fit, applying what you have learned in this course.
1. Describe your research question, as well as why you think it’s interesting. What do you hope to learn from the data?
2. Define at least one hypothesis that addresses your research question and can be tested empirically with your data (it should be a directional statement; e.g., younger people are more likely to be happy than older people). You may test additional hypotheses, but this is not required. Explain why you expect to find the hypothesized relationship (i.e., defend your hypothesis).
3. Describe your data. What type of data will you be using? Where did you get it from? How many observations are in the dataset? What are your key variables or measures, and how are they scaled/coded? What do low/high values on the scale mean? How well does this variable capture your outcome of interest? Did you create the variables or were they already contained in the dataset? Did you merge data from multiple sources or use a single dataset?
If using linear regression: your dependent variable (outcome) should be measured on a continuous or pseudo-continuous scale (i.e., 7 or more ordered points). Your independent variables may be measured on any type of scale (e.g., continuous, ordinal, or nominal) provided that you properly code them (e.g., nominal variables should be dummy coded).
Where can I find data?
In addition to databases that may be useful such as the UK Data Archive, Data.Gov.uk, Michigan’s ICPSR, and Harvard’s Dataverse, there are other open repositories like Kaggle, which may be of interest. And, of course, there’s always Google. Below are some specific suggestions regarding where to find data:
Textual Data: Project Gutenberg (books), Manifesto Project (political documents), LexisNexis Academic (TV and radio news transcripts), (screenplays)
Network Data: UCINET, Network Repository, UC Irvine Network Data Repository

Spatial Data: No specific datasets (search for geocoded or spatial data)
You may also use data from any of the lab exercises (provided you do not repeat the exact same analysis); the same rules apply for the textbook.
4. Describe your analytical approach. What analyses did you conduct and why? What are the strengths/weaknesses of this approach? If you used more than one method, then discuss how these methods compare to each other.
5. Substantively interpret your results. Make sure that you thoroughly discuss your findings and compare different outputs/statistics across approaches. For instance, if you analyse network data, you should discuss various statistics that measure centrality. Overall, what do the results tell you?
If using linear regression: estimate your regression model(s) using the ‘lm()’ function in R. Interpret the results from your regression model(s), beginning with the coefficients of your key predictors. What does a 1-unit change in this variable mean for your dependent variable? Are there any other predictors that have statistically significant or interesting results? Was your hypothesis supported by the data? How much variation in your outcome does your model explain? Make sure that you substantively interpret your findings so that a layperson could understand them (i.e., how large are the observed associations?).
6. Visualise your results. Again, discuss how your figure(s) help illustrate your main findings. Make sure that you label your figures with titles, axis labels, etc.
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This course is SOST70011 Introduction to Statistical Modelling
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