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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")
Yes, there is a relationship between x and y. As x goes up so does y.
library(readr)
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)
Yes there is a positive relationship.
There is a positive relationship
plot(sales ~ radio, data = ad_sales,
pch = 16, col = "blue",
main = "Sales vs. Radio Advertising",
xlab = "Radio advertising spend",
ylab = "Sales")
abline(lm(sales ~ radio, data = ad_sales), col = "red", lwd = 2)
I learned: this class is hard, rmarkdown is picky, and parenthesis are important
# Multiple regression
fit_multi <- lm(sales ~ TV + radio + newspaper, data = ad_sales)
summary(fit_multi)
##
## Call:
## lm(formula = sales ~ TV + radio + newspaper, data = ad_sales)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.8277 -0.8908 0.2418 1.1893 2.8292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.938889 0.311908 9.422 <2e-16 ***
## TV 0.045765 0.001395 32.809 <2e-16 ***
## radio 0.188530 0.008611 21.893 <2e-16 ***
## newspaper -0.001037 0.005871 -0.177 0.86
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.686 on 196 degrees of freedom
## Multiple R-squared: 0.8972, Adjusted R-squared: 0.8956
## F-statistic: 570.3 on 3 and 196 DF, p-value: < 2.2e-16
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