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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).
plot(y3 ~ x2, data = anscombe, pch = 16)
abline(lm(y3 ~ x3, anscombe), col = "grey20")
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)
###Your answer here
###Question 4:Three things you learned from this tutorial ###Your answer here