what quartile 1 and quartile 3? And how to t-test?
Easiest way to learn is search wikipedia quartile and t-test
Quartile 1 is the 25th percentile (25% of the scores are lower), while the quartile 3 is the 75th percentile. Quartile 2 is the median.
I searched Google under the key words "t-test" to get these possible sources:
http://en.wikipedia.org/wiki/Student's_t-test
http://www.socialresearchmethods.net/kb/stat_t.php
http://ccnmtl.columbia.edu/projects/qmss/t_about.html
http://web.uccs.edu/lbecker/SPSS/ttest.htm
In the future, you can find the information you desire more quickly, if you use appropriate key words to do your own search. Also see http://hanlib.sou.edu/searchtools/.
I hope this helps a little more. Thanks for asking.
Quartiles are statistical measures that divide a dataset into four equal parts. Quartile 1 (Q1) represents the lower quartile, and Quartile 3 (Q3) represents the upper quartile.
To calculate the quartiles, follow these steps:
1. Arrange the dataset in ascending order.
2. Determine the position of the quartiles:
- Q1 position: (n+1) * 0.25
- Q3 position: (n+1) * 0.75
Here, "n" represents the number of data points in the dataset.
3. If the position is an integer value, take the respective value in the dataset as the quartile. If it is a fractional value, calculate the average of the data points at the two closest positions.
For example, let's say we have a dataset: [2, 4, 6, 8, 10, 12, 14, 16, 18, 20].
1. Sort the data: [2, 4, 6, 8, 10, 12, 14, 16, 18, 20].
2. Calculate the positions:
- Q1 position: (10 + 1) * 0.25 = 2.75 (between the 2nd and 3rd value)
- Q3 position: (10 + 1) * 0.75 = 8.25 (between the 8th and 9th value)
3. Calculate the quartiles:
- Q1: (6 + 8) / 2 = 7
- Q3: (16 + 18) / 2 = 17
So, in this example, Q1 is 7 and Q3 is 17.
Now, moving on to your second question about t-tests. A t-test is a statistical method used to determine if there is a significant difference between the means of two groups.
To perform a t-test, you can follow these steps:
1. Set up the null hypothesis (H0) and alternative hypothesis (HA) for your study. H0 assumes that there is no significant difference between the two groups, while HA assumes there is.
2. Collect data from both groups and calculate their means (x̄1 and x̄2) and standard deviations (s1 and s2).
3. Determine the level of significance (α) you want to use (e.g., 0.05).
4. Calculate the test statistic using the formula:
t = (x̄1 - x̄2) / sqrt((s1^2/n1) + (s2^2/n2))
Here, n1 and n2 are the sample sizes of each group.
5. With the test statistic, you can compare it to the critical value from the t-distribution table using the degrees of freedom (df = n1 + n2 - 2). If the test statistic is more extreme than the critical value, you reject the null hypothesis and conclude that there is a significant difference between the two groups. If not, you fail to reject the null hypothesis.
It's important to note that there are different types of t-tests (e.g., independent t-test, paired t-test), so the steps may vary depending on your specific scenario. Also, many statistical software packages can automatically calculate t-tests for you, which can be more convenient than performing the calculations manually.