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Note: this analysis was performed using the open source software R and Rstudio.

library(readr)
ad_sales <- read_csv('https://raw.githubusercontent.com/utjimmyx/regression/master/advertising.csv')
## New names:
## Rows: 200 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (6): ...1, X1, TV, radio, newspaper, sales
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
plot(sales ~ TV, data = ad_sales)

plot(sales ~ radio, data = ad_sales)

This is the end of part 1 for my exploratory analysis.

library(ggplot2)
head(ad_sales)
## # A tibble: 6 × 6
##    ...1    X1    TV radio newspaper sales
##   <dbl> <dbl> <dbl> <dbl>     <dbl> <dbl>
## 1     1     1 230.   37.8      69.2  22.1
## 2     2     2  44.5  39.3      45.1  10.4
## 3     3     3  17.2  45.9      69.3   9.3
## 4     4     4 152.   41.3      58.5  18.5
## 5     5     5 181.   10.8      58.4  12.9
## 6     6     6   8.7  48.9      75     7.2
ggplot(data = ad_sales, aes(x = TV)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

This is the end of part 2 for my exploratory analysis.

Question 1

There is a positive relationship between x and y. As radio increases, so does sales. # Question 2 A coefficient is the relationship betweeen independent and dependent variable. There is a positive relationship between the two variables. As TV increases, sales also increases. # Question 3 Is there a relationship the price and the product? There are some limitations such as a linear relationship # Question 4 I dont think you can plot the relationship between radio advertising and sales. # Question 5 library(ggplot2)

References

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