I am doing a T test for this problem and trying to see how to do it and which t test is best. Here is the data
Infractions of two distict groups of freshman and sophmore
fresman
7
4
8
7
4
8
9
7
Sophmore
5
5
4
5
6
4
5
3
Solution:-
Freshman | Sophmore | ||
Mean | 6.75 | Mean | 4.625 |
Standard Error | 0.647798 | Standard Error | 0.323899 |
Median | 7 | Median | 5 |
Mode | 7 | Mode | 5 |
Standard Deviation | 1.832251 | Standard Deviation | 0.916125 |
Sample Variance | 3.357143 | Sample Variance | 0.839286 |
Kurtosis | -0.49181 | Kurtosis | 0.420824 |
Skewness | -0.81286 | Skewness | -0.48772 |
Range | 5 | Range | 3 |
Minimum | 4 | Minimum | 3 |
Maximum | 9 | Maximum | 6 |
Sum | 54 | Sum | 37 |
Count | 8 | Count | 8 |
State the hypotheses. The first step is to state the null hypothesis and an alternative hypothesis.
Null hypothesis: u1 = u 2
Alternative hypothesis: u1 u2
Note that these hypotheses constitute a two-tailed test. The null hypothesis will be rejected if the difference between sample means is too big or if it is too small.
Formulate an analysis plan. For this analysis, the significance level is 0.05. Using sample data, we will conduct a two-sample t-test of the null hypothesis.
Analyze sample data. Using sample data, we compute the standard error (SE), degrees of freedom (DF), and the t statistic test statistic (t).
SE = sqrt[(s12/n1) +
(s22/n2)]
SE = 0.7243
DF = 14
t = [ (x1 - x2) - d ] / SE
t = 2.934
where s1 is the standard deviation of sample 1, s2 is the standard deviation of sample 2, n1 is the size of sample 1, n2 is the size of sample 2, x1 is the mean of sample 1, x2 is the mean of sample 2, d is the hypothesized difference between the population means, and SE is the standard error.
Since we have a two-tailed test, the P-value is the probability that a t statistic having 14 degrees of freedom is more extreme than -2.934; that is, less than -2.934 or greater than 2.934.
Thus, the P-value = 0.011
Interpret results. Since the P-value (0.011) is less than the significance level (0.05), we have to reject null hypothesis.
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