Assume following model:

$Sales_i = a +b_i*Price*Dummy+Advertsing*Dummy$

$Sales_i$ represent the sales in retailer i (either 1 or 2) and $Dummy$ is a dummy variable giving value 1 to retailer 2 and 0 to retailer 1.

Basically, I want to test formally whether the effects of price and advertising on the sales in retailer 1 differs significantly from the effects of price and advertising on sales in store 2.

I thought I could do this using an F-test. So I fitted a model $Sales = a+b_1*Price+b_2Advertising$ (no distinction between retailers here) and requested an anova table:

anova(model1, model2)

I am not sure if this is the right way to test this. Also, the anova table did not provide any P-value, so I don't know whether te reject or accept the F-test.

Can somebody please help me solving this?

  • $\begingroup$ Do you mean "b*price + advertising" or "b*(price + advertising)"? $\endgroup$ Commented Dec 10, 2014 at 15:10
  • $\begingroup$ I appoligize. I mean $Sales = a+b_1*Price + b_2*advertising$ $\endgroup$
    – Pieter
    Commented Dec 10, 2014 at 17:39
  • $\begingroup$ The model in your post does not look correct. There is no error term and also no parameters describing price and ad effects for retailer 1. $\endgroup$
    – Michael M
    Commented Dec 10, 2014 at 18:06

1 Answer 1


A minimum working example is helpful; e.g., I don't understand what precisely the "sales" and the "price" variables measure. Also, it seems to me that the models you specify are incorrect, as you specify interaction terms without including main-effect terms of the same variables. So my interpretation of your question, which may not make sense given the real data, is that you want to fit a model that contains up to a three-way interaction of "price", "advertising" and "store", e.g.,

$$\begin{align} \widehat{sales} = a &+ b_1 price + b_2 advertising + b_3 store_1 + b_4 (price \times advertising)\\&+ b_5 (price \times store_1) + b_6 (advertising \times store_1)\\&+ b_7 (price \times advertising \times store_1)\,. \end{align}$$

Here, $store_1$ refers to the second level of your factor "store", which is coded as $1$ in R. This model could be fitted in R as lm(sales ~ price * advertising * store, my.data), where my.data refers to the data frame in long format — one variable per column, one observation per row — that contains the variables included in the model. Passing the model to summary() will give you significance tests ($t$ ratios) of the coefficient estimates. If you also want an $F$ test of the the highest-order interaction term in the model, you can pass the model to drop1() and specify test = "F", like so:

my.mod <- lm(sales ~ price * advertising * store, my.data)
summary(my.mod)           # For "t" ratios for coefficient estimates
drop1(my.mod, test = "F") # For "F" ratio for three-way interaction term

drop1() compares the full alternative-hypothesis model to a second model that does not include, in this case, the three-way interaction term $b_7(price \times advertising \times store_1)$ and computes the changes in fit.

Here is a complete, albeit meaningless, example:

my.data <- data.frame("sales" = rnorm(100, 32.30),
                      "price" = rnorm(100, 6.99),
                      "advertising" = rnorm(100, 9.50),
                      "store" = factor(rep(c(".a", ".b"), each = 50))    )
my.mod <- lm(sales ~ price * advertising * store, my.data)
drop1(my.mod, test = "F")

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