R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

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 explanatory analysis.

#question 1 Yes, there is a relationship between the two. Both x and y have a positive relationship.

#question 2 A coefficient represents the strength and direction of a relationship between two variables. For linear regression, the coefficient quantifies how much the dependent variable, sales, is expected to change when the independent variable, TV advertising increases by one unit. The scatter plot shows an upward trend, meaning as TV ad spending increases, sales also tend to increase.

#question 3 With simple regression analysis, we can address marketing questions like how advertising spend affects sales, how pricing impacts demand, and how website traffic influences conversions. However, its limitations include only considering one predictor at a time, assuming a linear relationship, and not accounting for external factors like competition or seasonality. But is limited by only considering one factor, assuming linearity, and ignoring external influences.

#question 4 cor(ad_sales\(radio, ad_sales\)sales) model <- lm(sales ~ radio, data = ad_sales) summary(model)

#question5 library(ggplot2)

ggplot(ad_sales, aes(x = radio, y = sales)) + geom_point(color = “blue”) + labs(title = “Radio Advertising vs Sales”, x = “Radio Ad Spend”, y = “Sales”) + theme_minimal()