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

Objective

The objective of this tutorial is to explain how bivariate analysis works.This analysis can be used by marketers to make decisions about their pricing strategies, advertising strategies, and promotion stratgies among others.

Bivariate analysis is one of the simplest forms of statistical analysis. It is generally used to find out if there is a relationship between two sets of values (or two variables). That said, it usually involves the variables X and Y (statisticshowto.com).

Dataset - We will be using two online datasets available in R for this tutorial

plot(y3 ~ x2, data = anscombe, pch = 16)
abline(lm(y3 ~ x3, anscombe), col = "grey20")

Question 1:is there a relationship between x and y? If so, how does the relationship look like?

Yes, there is definitely a relationship between x and y. The relationship is linear and positive because we can observe that most of the points stick close to the straight line and move upward. There is an obvious extreme outlier though the disrupts the linear relationship.

library(readr)
library(readr)
ad_sales <- read_csv('https://raw.githubusercontent.com/utjimmyx/regression/master/advertising.csv')
## Warning: Missing column names filled in: 'X1' [1]
## Warning: Duplicated column names deduplicated: 'X1' => 'X1_1' [2]
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   X1 = col_double(),
##   X1_1 = col_double(),
##   TV = col_double(),
##   radio = col_double(),
##   newspaper = col_double(),
##   sales = col_double()
## )
plot(sales ~ TV, data = ad_sales)

Question 2:Is there a relationship between TV advertising and Sales? If so, how does the relationship look like?

Yes, there is a relationship. It is a positive linear relationship in that the more they spend on tv advertisement the more sales they get.

Question 3:Can you plot the relationship between radio advertising and Sales? If so, how does the relationship look like?

Yes, see the plot below for the relationship between radio advertising and sales. The relationship is linear and positive, and tells us that the more they spend on radio advertising the more sales they get.

library(readr)
library(readr)
ad_sales <- read_csv('https://raw.githubusercontent.com/utjimmyx/regression/master/advertising.csv')
## Warning: Missing column names filled in: 'X1' [1]
## Warning: Duplicated column names deduplicated: 'X1' => 'X1_1' [2]
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   X1 = col_double(),
##   X1_1 = col_double(),
##   TV = col_double(),
##   radio = col_double(),
##   newspaper = col_double(),
##   sales = col_double()
## )
plot(sales ~ radio, data = ad_sales)

Question 4:Three things you learned from this tutorial

I learned how to properly plot a bivariate analysis using the R language, how to look at a scatter plot and understand the difference between a strong and weak relationship, and lastly I was shown how useful this analysis can be when you have the right kind of data to work with.

References

Bivariate Analysis Definition & Example https://www.statisticshowto.com/bivariate-analysis/#:~:text=Bivariate%20analysis%20means%20the%20analysis,the%20variables%20X%20and%20Y.

https://www.sciencedirect.com/topics/mathematics/bivariate-data