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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')
## `curl` package not installed, falling back to using `url()`
## 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.
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

The scatter plot shows a clear positive relationship between X and Y. As X increases, Y tends to increase as well.

Question 2

The coefficient tells us how much sales will increase for each unit increase in TV advertising. Based on the scatter plot, there is a positive relationship: as TV advertising increases, sales also increase.

Question 3

Using simple regression analysis we can answer marketing questions like how TV advertising affects sales or how promotions impact customer behavior. However, it has limitations such as showing correlation but not causality. It can also be influenced by outliers and the quality of the data, which may lead to inaccurate results.