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The Cox-Stuart test is defined as a little powerful test (power equal to 0.78), but very robust for the trend analysis. It is therefore applicable to a wide variety of situations, to get an idea of the evolution of values obtained. The proposed method is based on the binomial distribution. In R there is no function to perform a test of Cox-Stuart, so now we see the logical steps that are the basis of test and finally we can write the function ourself.

You want to assess whether there is an increasing or decreasing trend of the number of daily customers of a restaurant. We have the number of customers in 15 days:
Customers: 5, 9, 12, 18, 17, 16, 19, 20, 4, 3, 18, 16, 17, 15, 14

To perform the test of Cox-Stuart, the number of observations must be even. In our case we have 15 observations. Delete, therefore, the observation at position (N+1)/2 (here the observation with value = 20):

customers = c(5, 9, 12, 18, 17, 16, 19, 20, 4, 3, 18, 16, 17, 15, 14)

length(customers)
[1] 15

cust_even = customers[ -(length(customers)+1)/2 ]
length(cust_even)
[1] 14

Now we have 14 observations, and we can then proceed. Divide the observations into two vectors, the first containing the first half of the measures, and the second the second half:

fHalf = cust_even[1:7]
sHalf = cust_even[8:14]

fHalf
[1]  5  9 12 18 17 16 19

sHalf
[1]  4  3 18 16 17 15 14

Now subtract, value by value, the content of the two vectors:

difference = fHalf - sHalf

difference
[1]  1  6 -6  2  0  1  5

Now consider only the signs of the contents of the vector difference

signs = sign(difference)

signs
[1]  1  1 -1  1  0  1  1

A difference has value 0 and therefore also in the vector with the signs there is a value equal to 0. This must be eliminated:

signs = signs[ signs != 0 ]

signs
[1]  1  1 -1  1  1  1

We obtained six differences, and then six signs. Now we have to count the number of positive-signs and the number of negative-signs:

pos = signs[signs > 0]
neg = signs[signs < 0]

length(pos)
[1] 5

length(neg)
[1] 1

Now we choose the number of signs that is smaller. In this case we choose the number of negative signs (1). We compute the probability to obtain x = 1 successes on N = 6 experiments, each of which yields success with probability p = 0.5 (binomial distribution):

pbinom(1, 6, 0.5)
[1] 0.109375

The value so calculated is higher than 0.05 (we choose a significance level of 95%). Therefore there is no significant trend (which would have been in decline since the number of negative signs is minor).
If the value was less than 0.05, we accepted the hypothesis of a significant trend.

Now try to fit a regression model, and observe the p-value of the slope: the coefficient b is not significant.

customers = c(5, 9, 12, 18, 17, 16, 19, 20, 4, 3, 18, 16, 17, 15, 14)
days <- c(1:length(customers))
model <- lm(customers ~ days)
summary(model)

Call:
lm(formula = customers ~ days)

Residuals:
Min      1Q  Median      3Q     Max
-11.090  -2.173   1.352   3.967   6.467

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  11.3048     3.1104   3.634  0.00303 **
days          0.2786     0.3421   0.814  0.43014
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.724 on 13 degrees of freedom

Here is the code to perform a Cos-Stuart test, written by me.

cox.stuart.test =
function (x)
{
method = "Cox-Stuart test for trend analysis"
leng = length(x)
apross = round(leng) %% 2
if (apross == 1) {
delete = (length(x)+1)/2
x = x[ -delete ]
}
half = length(x)/2
x1 = x[1:half]
x2 = x[(half+1):(length(x))]
difference = x1-x2
signs = sign(difference)
signcorr = signs[signs != 0]
pos = signs[signs>0]
neg = signs[signs

We can now use the function just created:

customers = c(5, 9, 12, 18, 17, 16, 19, 20, 4, 3, 18, 16, 17, 15, 14)
cox.stuart.test(customers)

Cox-Stuart test for trend analysis

data:
Decreasing trend, p-value = 0.1094