You are given data in the 'Data' tab about Tweets that include the hashtags #COVID and/or #coronavirus and about new COVID-19 cases. For each of 20 consecutive weeks, you have the the daily average (mean) numbers of such Tweets and the total number of reported new cases.

You must modify the first two weeks of data as follows. You will fill the missing Tweets cells with 4,000,000 plus the last six digits of your student number - in week 1, in normal order (4,000,000 + X₁X₂X₃X₄X₅X₆); in week 2, in reverse order (4,000,000 + X₆X₅X₄X₃X₂X₁). For example, if your student number were 200123456, the first week would be 4,123,456 and the second week would be 4,654,321. You will fill the Cases cells with 2,000,000 plus the last six digits of your student number - in week 1, in normal order (2,000,000 + X₁X₂X₃X₄X₅X₆); in week 2, in reverse order (2,000,000 + X₆X₅X₄X₃X₂X₁).

1.Create chart showing average daily tweets (vertical) versus the week (horizontal) (8 marks), making sure that the chart is appropriately labelled.

2.Add a linear trend-line (5 marks). It should also be appropriately labelled.

3.In the 'Questions' tab, Answer the following questions about your first chart:

a.Based on your linear regression line, what is the projected number of tweets in week 21? Your answer should be rounded to the nearest integer. (2 marks)

b.Is the correlation between tweets and time (weeks) positive or negative? Is it strong or weak? (2 marks)

c.To two decimal places, what percentage of the variation in the number of tweets is explained by the passage of time? (2 marks)

4.Create new chart showing average daily tweets (horizontal) versus the number of cases (vertical) (8 marks), making sure that the chart is appropriately labelled.

5.Add a linear trend-line (5 marks). It should also be appropriately labelled.

6.Answer the following questions:

a.What is the projected number of COVID-19 cases in week 21? Your answer should be rounded to the nearest integer. (3 marks)

b.This chart suggests that one variable is independent (cause), and the other is dependent (result). Which variable does this chart suggest is independent? (1 mark)

c.In this chart, is the correlation positive or negative, strong or weak? Does the direction of causality make sense to you? (2 marks)

d.How much of the variation in the dependent variable is explained by the variation in the independent variable? Could there be any other reasons for this correlation? (3 marks)

7.Comment.

a.As a data analyst, what does this statistical evidence suggest to you? (3 marks)

b.How could you explain this data to an audience, in words and charts? (4 marks)

c.Does your analysis lead to any actionable measure or have any predictive value? (2 marks)

use this data: https://github.com/mehmetgnc00/covid-data/blob/main/Inf%20Tech%20pt2%20COVID-19%20Time%20Series%20BDAT1005-21W.pdf

student number:200467577