---
title: "STAT 516 Lec 03 code"
format: html
---


SC apartment data:
```{r}
link <- url("https://gregorkb.github.io/data/scapts.csv")
scapts <- read.csv(link)
head(scapts)
plot(scapts)
```


```{r}
lm_out <- lm(log(price) ~ nbath + nbed + log(sqft) + city + pets, data = scapts)

summary(lm_out)
```


Confidence intervals:

```{r}
confint(lm_out)
```


Confidence interval for price of apartment with certain attributes:

```{r}
newdata = data.frame(city = "Charleston", nbath = 1,
                     nbed = 1, sqft = 900, pets = "yes")
# predict(lm_out,newdata = newdata, int = "conf")

# confidence interval
exp(predict(lm_out,newdata = newdata, int = "conf"))

# prediction interval
# predict(lm_out,newdata = newdata, int = "pred")
exp(predict(lm_out,newdata = newdata, int = "pred"))
```

The anova table below is not exactly what we want..
```{r}
anova(lm_out)
```


Include an interaction between sqft and city

```{r}

lm2_out <- lm(log(price) ~ city + nbath + nbed + log(sqft) + pets + city:log(sqft), data = scapts)

summary(lm2_out)
```


```{r}

plot(lm2_out,which = 2)
plot(lm2_out,which = 1)
plot(lm2_out,which = 4)

```