Comparison of test results between Stats tester and R

 

1. The Example data is entered by tapping the Example button of the Stats tester app.
2. When you tap the Calc button in the Stats tester, the p-value will be displayed at the end of the output window.
3. If you're using R language on a Mac or Windows PC : Copy the R code in the box, paste it to R screen of your PC to the right of the prompt (>), and press the return key. The p-value will appear at the end of the first line of the R result.

 

3. [One-Sample t-Test (to Specified Mean)]

Example
  A : 1, 3, 2, 5, 6, 11
  Specified Mean : 8
Stats tester
  p = 0.073464 (2 tails)
R
  Code:

    A <- c(1,3,2,5,6,11)
    mu <- 8
    t.test(A, mu=8, alt="two.sided")

  Result
    p = 0.0734649 (2 tails)

 

4. [Two-Sample t-Test (Student's and Welch's t-Tests)]

Example
  A : 1, 2, 3, 4, 5
  B : 11, 13, 15, 17
Stats tester
  Student p = 0.000097 (2 tails)
  Welch p = 0.000846 (2 tails)
R
  Code:

    A <- c(1, 2, 3, 4, 5)
    B <- c(11, 13, 15, 17)
    t.test(A, B, alt="two.sided",var.equal=T) # (Student)
    t.test(A, B, alt="two.sided",var.equal=F) # (Welch)

  Result
    Student p = 0.000097 (2 tails)
    Welch p = 0.0008459 (2 tails)

 

5. [Paired-Sample t-test]

Example
  A : 22, 20, 31, 25
  B : 11, 15, 28, 20
Stats tester
  p = 0.040519 (2 tails)
R
  Code:

    A <- c(22, 20, 31, 25)
    B <- c(11, 15, 28, 20)
    t.test(A, B, paired=T, alt="two.sided")

  Result
    p = 0.04052 (2 tails)

 

6. [One-Way Analysis of Variance (ANOVA)]

Example
  A : 13, 12, 11, 11
  B : 9, 8, 9, 7, 10
  C : 12, 14, 11, 13
  D : 13, 14, 13, 15
Stats tester
  p = 0.000059
R
  Code:

    vx <-c(13, 12, 11, 11, 9, 8, 9, 7, 10, 12, 14, 11, 13, 13, 14, 13, 15 )
    fx=factor(rep(c("A", "B", "C", "D"), c(4, 5, 4, 4)))
    anova(aov(vx~fx))

  Result
    p = 5.874e-05

 

7. [Linear Regression and Test for Pearson Correlation Coefficient]

Example
  A : 1, 4, 4, 6, 8
  B : 5, 8, 7, 10, 11
Stats tester
  p = 0.003436 (2 tails)
R
  Code:

    A <- c(1, 4, 4, 6, 8)
    B <- c(5, 8, 7, 10,11)
    cor.test(A, B, method="pearson")

  Result
    p = 0.003436 (2 tails)

 

8. [Shapiro-Wilk Test (Normality) and Q-Q Plot]

Example
  A : 1, 2, 3, 3.9, 4.2, 4.5, 5, 5.4, 5.6 ,5.8, 6.2, 6.4, 6.6, 7, 7.5, 7.8, 8.1, 9, 10, 11
Stats tester
  p = 0.999942
R
  Code:

    A <- c(1, 2, 3, 3.9, 4.2, 4.5, 5, 5.4, 5.6 ,5.8, 6.2, 6.4, 6.6, 7, 7.5, 7.8, 8.1, 9, 10, 11)
    shapiro.test(A)

  Result
    p = 0.999942

 

9. [Chi-Square Test (2X2 Independence)]

Example
  110, 90
  88, 112
Stats tester
  p = 0.035720 (Continuity corrected)
  p = 0.027799 (Continuity not corrected)
R
  Code:

    table <- matrix(c(110, 90, 88, 112), ncol=2, byrow=T)
    chisq.test(table, correct=T)
    chisq.test(table, correct=F)

  Result
    p = 0.03572 (Continuity corrected)
    p = 0.027799 (Continuity not corrected)