# One sample sign test in stata forex

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To this null hypothesis, he will cast an alternate hypothesis If is true, more than half of the values of sample X will be less than 26 MPG. In other words, the sample shows low mileages — significantly less than 26 MPG. Look at this visual. Out of the eight data points, the number of positives is five, and the number of negative is three.

The number of positives, , is the test statistic for the sign test. Under the null hypothesis, follows a binomial distribution with a probability of 0. We consider this as the null distribution. The null distribution is the distribution of the probability that can be 0, 1, 2, …, or 8. For instance, Here is the full binomial table. Now, since the sample yields , p-value for this test is the probability of finding up to five positives —.

Since the p-value is greater than 0. If you noticed, the sign test only uses the signs of the data and not the magnitudes. So it is resistant to outliers. Sure enough, he finds this. He first establishes the null and the alternative hypothesis.

If is true, about half of the 24 values found in the sample will be greater than 36 MPG, and about half of them will be negative. If is true, more than half of the 24 values will be less than 36 MPG. Joe then plots the data on the number line to visually find the number of positive and negative signs.

He counts them as 7. There is one number that is exactly 36 MPG. He removes this from the sample and conducts the test with 23 data points. Since the p-value is less than , Joe has to reject the null hypothesis that the average mileage for the Hyundai Sonata Hybrid is 36 MPG.

He can. Here is the result of the t-test. The test statistic falls in the rejection region, i. He will reject the null hypothesis test. What about the outliers? The problem with outliers is that they can have a negative effect on the one-sample t-test, reducing the accuracy of your results. Fortunately, when using Stata to run a one-sample t-test on your data, you can easily detect possible outliers. Assumption 4: Your dependent variable should be approximately normally distributed.

Your data need only be approximately normal for running a one-sample t-test because it is quite "robust" to violations of normality, meaning that this assumption can be a little violated and still provide valid results. You can test for normality using the Shapiro-Wilk test of normality, which is easily tested for using Stata. In practice, checking for assumptions 3 and 4 will probably take up most of your time when carrying out a one-sample t-test. However, it is not a difficult task, and Stata provides all the tools you need to do this.

In the section, Test Procedure in Stata , we illustrate the Stata procedure required to perform a one-sample t-test assuming that no assumptions have been violated. First, we set out the example we use to explain the one-sample t-test procedure in Stata. Stata Example A lecturer wants to determine how students' test anxiety is affected by the use of a hypnotherapy programme. As such, the lecturer plans to carry out a study where 40 students are split randomly into two equal groups: one group of 20 students who receive the hypnotherapy programme and a second group of 20 students who do not receive the hypnotherapy programme.

Then, before all 40 students sit an exam, the lecturer measures their test anxiety. To measure the difference in test anxiety between the two groups of students, the lecturer could then use an independent t-test. However, before the lecturer carries out this study, he wants to make sure that the 40 students taking part have test anxiety levels that are considered to be 'normal'.

Let's imagine that a score of 8. Lower scores indicate less test anxiety and higher scores indicate greater test anxiety. Therefore, the test anxiety of all 40 participants is measured and a one-sample t-test is used to determine whether this sample is representative of a normal population i. The test anxiety scores are recorded in the variable, TestAnxiety. Stata Test Procedure in Stata In this section, we show you how to analyze your data using a one-sample t-test in Stata when the four assumptions in the previous section, Assumptions , have not been violated.

You can carry out a one-sample t-test using code or Stata's graphical user interface GUI. After you have carried out your analysis, we show you how to interpret your results. First, choose whether you want to use code or Stata's graphical user interface GUI. Using our example where the dependent variable is TestAnxiety and the hypothesized value is 8. Published with written permission from StataCorp LP. You can see the Stata output that will be produced here. You will be presented with the t tests mean-comparison tests dialogue box: Published with written permission from StataCorp LP.

Keep the One-sample option in the —t-tests— area, as shown below: Published with written permission from StataCorp LP. Select the dependent variable, TestAnxiety, from within the Variable name: drop-down box, and enter the value of the hypothesized mean, 8. You will end up with a screen similar to the one below: Published with written permission from StataCorp LP.

If you want to change the value of the confidence interval, enter the new value or use the pre-defined values in the Confidence level: box e. The output that Stata produces is shown below. Stata Output of the one-sample t-test in Stata If your data passed assumption 3 i.

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