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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?

Your answer here

The relationship is positive

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?

Your answer here

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

###Your answer here

###Question 4:Three things you learned from this tutorial ###Your answer here