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In this section, we install and load the necessary packages.
In this section, we import the necessary data for this lab.
You are hired as a Business Analyst to help the manager to find out if spending millions of dollars on Super Bowl ads creates social media buzz.
As a second step, carry out descriptive analysis on superbowl_commmercials.csv data. There are some questions for each sections that you need to answer.
Task 1, compute the mean, variance, and standard deviation of Cost for each Brand before the Super Bowl and after the Super Bowl, using the relevant R functions. Hint: you may use dplyr functions groupby() and summarise().
superbowl %>%
group_by(Brand) %>%
summarise(mean=mean(Estimated.Cost),
sd=sd(Estimated.Cost),
variance=var(Estimated.Cost))
## # A tibble: 10 × 4
## Brand mean sd variance
## <chr> <dbl> <dbl> <dbl>
## 1 Bud Light 3.63 2.76 7.60
## 2 Budweiser 4.58 2.81 7.91
## 3 Coca-Cola 6.17 2.40 5.75
## 4 Doritos 4.29 2.92 8.53
## 5 E-Trade 2.81 1.17 1.37
## 6 Hyundai 5.58 3.88 15.0
## 7 Kia 9.00 3.68 13.5
## 8 NFL 11.7 10.6 111.
## 9 Pepsi 4.51 2.28 5.19
## 10 Toyota 8.35 4.03 16.3
Task 2 Create a histogram to show the distribution of Super Bowl advertising cost and YouTube likes.Do you observe any extreme values?
hist(superbowl$Estimated.Cost)
hist(superbowl$Youtube.Likes)
Questions:
The NFL has the highest mean cost and e-trade has the lowest variance. Doritos has the extreme value in youtube likes.
Task 1, compute the covariance and correlation between Cost and Youtube.Likes in superbowl dataset.
cov(superbowl$Estimated.Cost,superbowl$Youtube.Likes)
## [1] NA
cor(superbowl$Estimated.Cost,superbowl$Youtube.Likes)
## [1] NA
Task 2,Create a scatter plot showing the relationship between Super Bowl advertising cost and YouTube Likes.
plot(superbowl$Estimated.Cost,superbowl$Youtube.Likes)
Then, answer the following questions. Please write your answers after Response:
Questions: