# What stats tests can be used to find whether the increase/decrease in counts yearly are difference statistically?

I have read posts about the similar problems in the forum and they are suggesting using regression to see if there is any significant increases/decrease. I have data for 3 years and their counts i.e. the number of student attending X school. I would like to know if the increase in the admission number is statistically significant. This is what I did:

x = data.frame("year" = c(1,2,3), "count" = c(100,120,150))
reg = lm(count ~., x)
summary(reg)


The results are as follow:

    Call:
lm(formula = count ~ ., data = x)

Residuals:
1      2      3
1.667 -3.333  1.667

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)   73.333      6.236   11.76   0.0540 .
year          25.000      2.887    8.66   0.0732 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.082 on 1 degrees of freedom
Multiple R-squared:  0.9868,    Adjusted R-squared:  0.9737
F-statistic:    75 on 1 and 1 DF,  p-value: 0.07319


The results indicate that there is no statistically significant between years. Am I doing this correctly? and how can I compare between year 1 and year 3?

Thank you,

• The results here do not show 'no statistical difference between years'. They only show that year is not a statistically significant predictor of student counts. The number of student attending in a given year is now a known constant and the difference between years 1 and 3 was 50 students. perhaps you should reconsider what hypothesis you are actually trying to test. – Ryan Apr 23 at 16:02

You can use an independent two-sample $$t$$-test to determine if the count in year three is significantly higher than in years one and two combined.

Break your dataset into two samples: one containing the first two years and the other containing the third year. From sample one you can estimate the mean and standard deviation. From sample two you can only estimate the mean. Therefore we should assume that variability is the same from year to year (just as in your regression model).

For sample one we have $$\bar{x}_1 = \dfrac{1}{2} \left( 100 + 120 \right) = 110$$ and $$s_1^2 = (100 - 110)^2 + (120 - 110)^2 = 200.$$ From sample two we have $$\bar{x}_2 = 150.$$ The pooled standard deviation is just $$s_p = \sqrt{200}.$$

Our $$t$$-statistic is then $$\dfrac{110 - 150}{\sqrt{200} \sqrt{\frac{1}{2} + 1}} = -2.30 \dots$$ The R command 2*pt(-2.31, df = 1) returns a p-value of 0.26 in the two-sided test, so it seems not to be a statistically significant increase.

Edit:

As pointed out by whuber in the comments, the choice of which years to compare must not be influenced by the respective counts in those years. Otherwise the $$t$$-test is invalid.

• Why not compare the last two values to the first one? Or the outer two to the middle one? The arbitrariness of these choices suggests there is some problem with this approach. – whuber Apr 23 at 15:40
• In the question it was asked whether years 1 and 3 can be compared. Without knowing the variance they can't. We can however compare one of the years with the other two. I chose the last year since it is the most extreme observation and hence the most likely candidate for a significant difference. – Estacionario Apr 23 at 15:52
• By selecting the mode of comparison based on the data, you have changed the sampling distribution of the test statistic and therefore altered the p-value. Your results are invalid. – whuber Apr 23 at 15:55
• You are correct: this is a form of p-hacking. Should I delete my answer? – Estacionario Apr 23 at 16:57
• It's an interesting answer, so you might consider modifying it or qualifying it instead of deleting it. – whuber Apr 23 at 17:45