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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.

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, what does the relationship look like?

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)

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

Yes there is a positive relationship.

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

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)

Question 4:Three things you learned from this tutorial

I learned: this class is hard, rmarkdown is picky, and parenthesis are important

Are there any other kinds of exploratory analysis you can perform using R? If so, please include your analysis and results in your final report.

# 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

5. Visit the avocado dataset available in week 2, raise one question you could possibly answer with the data, and explain how a stakeholder could benefit from your proposed analysis.

Do organic avocados cost more than regular avocados and is there a price variance by region?

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