I was a bit confused regarding the interpretation of

`bptest`

in R (`library(lmtest)`

). The null hypothesis of`bptest`

is that the residuals have constant variance. So, a p-value less than 0.05 would mean that the homoscedasticity assumption would have to be rejected. However on this website:I found the following results that confuse me:

`data: lmMod BP = 3.2149, df = 1, p-value = 0.07297`

A p-Value > 0.05 indicates that the null hypothesis(the variance is unchanging in the residual) can be rejected and therefore heterscedasticity exists. This can be confirmed by running a global validation of linear model assumptions (gvlma) on the lm object.`gvlma(lmMod) # validate if assumptions of linear regression holds true. # Call: gvlma(x = lmMod) Value p-value Decision Global Stat 15.801 0.003298 Assumptions NOT satisfied! Skewness 6.528 0.010621 Assumptions NOT satisfied! Kurtosis 1.661 0.197449 Assumptions acceptable. Link Function 2.329 0.126998 Assumptions acceptable. Heteroscedasticity 5.283 0.021530 Assumptions NOT satisfied!`

So why is it the case that a p-value>0.05 means you have to reject the null hypothesis, when in fact a p-value less than 0.05 indicates that you have to reject the null hypothesis?

**Answer**

This ought to be a typo on rstatistics.net. You are correct that the null hypothesis of the Breusch-Pagan test is homoscedasticity (= variance does not depend on auxiliary regressors). If the $p$-value becomes “small”, the null hypothesis is rejected.

I would recommend contacting the authors of rstatistics.net regarding this issue to see if they agree and fix it.

Moreover, note that `glvma()`

employs a different auxiliary regressor than `bptest()`

by default and switches off studentization. More precisely, you can see the differences if you replicate the results by setting the arguments of `bptest()`

explicitly.

The model is given by:

```
data("cars", package = "datasets")
lmMod <- lm(dist ~ speed, data = cars)
```

The default employed by `bptest()`

then uses the same auxiliary regressors as the model, i.e., `speed`

in this case. Also it uses the studentized version with improved finite-sample properties yielding a non-significant result.

```
library("lmtest")
bptest(lmMod, ~ speed, data = cars, studentize = TRUE)
## studentized Breusch-Pagan test
##
## data: lmMod
## BP = 3.2149, df = 1, p-value = 0.07297
```

In contrast, the `glvma()`

switches off studentization and checks for a linear trend in the variances.

```
cars$trend <- 1:nrow(cars)
bptest(lmMod, ~ trend, data = cars, studentize = FALSE)
## Breusch-Pagan test
##
## data: lmMod
## BP = 5.2834, df = 1, p-value = 0.02153
```

As you can see both $p$-values are rather small but on different sides of 5%. The studentized versions are both slightly above 5%.

**Attribution***Source : Link , Question Author : fornit , Answer Author : Achim Zeileis*