Kind of Relationship can be Inferred from Summary Statistics - Assignment Help

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Task:

1. In Section 1, what kind of relationship can be inferred from summary statistics regarding ACT composite score and SAT total score? Which visualisations make this relationship apparent? [0.5/5]
2. Based on the box plots presented in Section 1, what is the relationship between parental level of education and parental income? Using table visualisation, find and show the entire rows that correspond to the outliers regarding parental income whose parents have a master's degree. [0.5/5]
3. Using an example, explain the importance of scaling features so that their magnitudes are comparable when computing distances. [0.5/5]
4. In Section 1, the distance matrix visualisation is not very informative. However, it is still possible to infer that the average distance between students whose parents only have some high school education and students whose parents have a master's degree is larger than the average distance between students whose parents only have some high school education. Explain how this inference is possible from the visualisation. [0.5/5]
5. In Section 2, increase the number of evenly spaced numbers from 10 to 100 for both axes and observe the corresponding heat map created through nearest neighbour interpolation. Read about this interpolation method and explain what you observed. [0.5/5]
6. The function load_wine from sklearn.datasets can be used to load the wine dataset into a DataFrame by using the command df = load_wine(as_frame=True)['frame'].
6.1. Load the wine dataset.Which feature is categorical? Compute the frequency of each value of the categorical feature. [0.5/5]
6.2. Compute univariate and multivariate summaries for all the numerical features. Group observations by the categorical feature and compute the corresponding median for each remaining numerical feature. [0.5/5]
6.3. Group observations by the categorical feature and create one box plot of alcohol for each group. [0.5/5]
6.4. Create a scatter plot for the pair of distinct numerical features with the highest correlation. [0.5/5]
6.5. Exclude the categorical feature, standardize the numerical features, and display a projection obtained by multidimensional scaling. Color the points by the categorical feature. [0.5/5]

 

Assignment 2 [Part 2/2]
1. A data warehouse for a music streaming company consists of the dimensions song, user, time (time and date of when the user listened to a song), and the two measures count (how many times a user listened to the song) and fee (fee paid by the streaming company to the artist every time a user listens to that song).
1. Draw a schema diagram for the above data warehouse using either a star, snowflake, or fact constellation schema. [0.5/5]

2. Starting with the base cuboid [time, user, song], what specific OLAP operations should be performed in order to list the total fee collected for a given song for a given month of a given year (e.g., October 2020)? [1/5]

2. Suppose that we have access to a data cube that contains information on rainfall for specific regions. The data cube has dimensions region, precipitation, and time.
1. Assuming that we would like to compute the total amount of rainfall for a given region and month, which data cube measure would we use? To which category of data cube measures does this particular measure fall into? [0.25/5]

 

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