I'm comparing the regressions of death counts (y) versus time (x), between 2 sexes. Reading summary.lm() gives me a set of regressions:

For females: [deaths = (-101.98 * year) + 241813.24]

For males: [deaths = (778.9 * year) - 1,526,000]

```
#summary of the female model
summary.lm(model.USdiabetes_F)
#summary of the male model
summary.lm(model.USdiabetes_M)
#which returns...
```

When I plug those regressions into Desmos or MathPapa, they intersect at x = 2006.87. But I also used an rstudio function provided by a user in this thread.

```
# Linear model Intercept function
lmIntx <- function(fit1, fit2, rnd=2) {
b1<- fit1$coefficient[1] #y-int for fit1
m1<- fit1$coefficient[2] #slope for fit1
b2<- fit2$coefficient[1] #y-int for fit2
m2<- fit2$coefficient[2] #slope for fit2
if(m1==m2 & b1==b2) {print("Lines are identical")
} else if(m1==m2 & b1 != b2) {print("Lines are parallel")
} else {
x <- (b2-b1)/(m1-m2) #solved general equation for x
y <- m1*x + b1 #plug in the result
data.frame(x=round(x, rnd), y=round(y, rnd))
}
```

```
lmIntx(lm(rawUSdiabetes_M$FactValueNumeric~rawUSdiabetes_M$Period),
lm(rawUSdiabetes_F$FactValueNumeric~rawUSdiabetes_F$Period))
#which gave me...
```

x = 2007.41, y = 37287.32

Please tell me what's going on; it would be much appreciated.

### Data

For reproduction purposes, here's my data. Bc how the data is arranged, in practice I subset male and female deaths into separate datasets (thus filenames like "model.USdiabetes_F").

Period / FactValueNumeric / sexCat

2019 38340 Female

2019 49433 Male

2018 37867 Female

2018 47795 Male

2017 37505 Female

2017 46703 Male

2016 36566 Female

2016 44129 Male

2015 36580 Female

2015 43122 Male

2014 35645 Female

2014 41262 Male

2013 36160 Female

2013 40098 Male

2012 35901 Female

2012 38933 Male

2011 36093 Female

2011 38719 Male

2010 34155 Female

2010 35895 Male

2009 34256 Female