Lets assume that

```
set.seed(44)
deaths<- 10:1 + sample.int(3, 10, replace = T)
```

and

```
spent<- seq(100, 550, by = 50 )
```

The very first thing you want to do when you get your data is literally to look at it. This can be done relatively painlessly with

```
plot(spent, deaths)
```

which yields

So it looks like the more we spend, the less deaths there are. That makes sense. But how can we quantify that statement. Using `cor()`

will give us the correlation between the two variables `spent`

and `deaths`

.

```
cor(spent, deaths)
# [1] -0.9809581
```

So it looks like they are very strong (and negatively correlated.) One other simple method (that is closely related to `cor()`

) is to fit a linear model.

```
model<- lm(deaths~spent)
```

The `summary()`

call yields a lot of useful information about the model you just fit, the interpretation of which is beyond the scope of this post, but can be readily found with some quick Googling.

```
summary(model)
#Call:
#lm(formula = deaths ~ spent)
#Residuals:
# Min 1Q Median 3Q Max
#-0.89697 -0.51515 -0.05758 0.46364 1.01818
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 14.151515 0.539649 26.22 4.80e-09 ***
#spent -0.021697 0.001519 -14.29 5.62e-07 ***
#---
#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Residual standard error: 0.6898 on 8 degrees of freedom
#Multiple R-squared: 0.9623, Adjusted R-squared: 0.9576
#F-statistic: 204.1 on 1 and 8 DF, p-value: 5.622e-07
```

solved Determine if data is related in R [closed]