#Question 1 My hypothesis is that running a display campaign will positively impact revenue, that increased daily spending will correlate with increased daily revenue, that a higher click-through-rate will lead to more transactions, and that a higher conversion rate will result in greater daily revenue. #Upload data

data <- read.csv("Display_data.csv") # Replace with your data file
head(data) # Inspect the first few rows
##   spend clicks impressions display transactions revenue  ctr con_rate
## 1 22.61    165        8672       0            2   58.88 1.90     1.21
## 2 37.28    228       11875       0            2   44.92 1.92     0.88
## 3 55.57    291       14631       0            3  141.56 1.99     1.03
## 4 45.42    247       11709       0            2  209.76 2.11     0.81
## 5 50.22    290       14768       0            3  197.68 1.96     1.03
## 6 33.05    172        8698       0            2  204.36 1.98     1.16

Below is a regression model of my hypothesis, comparing the impact of revenue that higher click-through-rate will lead to more transactions.

# Load ggplot2 library
library(ggplot2)

# Create the scatter plot with regression line using ggplot2
ggplot(mtcars, aes(x = wt, y = mpg)) + 
  geom_point(color = "pink") +  # Scatter plot
  geom_smooth(method = "lm", col = "lavender", size = 1.5) +  # Linear regression line
  labs(title = "Scatter Plot with Regression Line",
       x = "Revenue",
       y = "Clicks") +
  theme_minimal()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `geom_smooth()` using formula = 'y ~ x'

#Question 2 My hypothesis is no significant relationship between advertising campaign exposure and product purchase.

data <- read.csv("ab_testing1.csv") # Replace with your data file
head(data) # Inspect the first few rows
##   Ads Purchase
## 1   1      152
## 2   0       21
## 3   2       77
## 4   0       65
## 5   1      183
## 6   1       87

#Creating a regression model.

# Load ggplot2 library
library(ggplot2)

# Create the scatter plot with regression line using ggplot2
ggplot(mtcars, aes(x = wt, y = mpg)) + 
  geom_point(color = "lightblue") +  # Scatter plot
  geom_smooth(method = "lm", col = "lightgreen", size = 1.5) +  # Linear regression line
  labs(title = "Scatter Plot with Regression Line",
       x = "Ads",
       y = "Purchase") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'