![]() ![]() ![]() Double click the 2.29 that now appears on the graph.In Show reference lines at X values, enter 2.29.We need to use the t-value for our sample mean that appears in the 1-sample t output (2.29). The trick here is that the x-axis still uses t-values despite displaying the energy costs. Let’s add a reference line to show where our sample mean falls within the sampling distribution and critical region. Simply click each t-value once and press the Delete key. To cleanup the x-axis, I had to delete the t-values that were still showing from before. I use rounded values to keep the x-axis tidy. Enter the energy cost values that you calculated as shown below. Click the Labels tab of the dialog box that appears.Click the Show tab and check the Low check box for Major ticks and Major tick labels.In Major Tick Position, choose Number of Ticks and enter 9.Next, we need to replace the t-values with the energy cost equivalents. Zero is the null hypothesis value, which is 260. We need to calculate the energy cost values for all of the t-values that will appear on the x-axis (-4 to +4).įor example, a t-value of 1 equals 290.8 (260 + (1*30.8). We’ll use the null hypothesis value that we entered in the dialog box (260) and the SE Mean value that appears in the 1-sample t-test output (30.8). Transforming the t-values to energy costs for a distribution centered on the null hypothesis mean requires a simple calculation:Įnergy Cost = Null Hypothesis Mean + (t-value * SE Mean) To do this, we need to transform the x-axis scale from t-values to energy costs. The t-value for our sample mean is 2.29 and it falls within the critical region.įor my blog posts, I thought displaying the x-axis in the same units as our measurement variable (energy costs) would make the graph easier to understand. This graph shows the distribution of t-values for a sample of our size with the t-values for the end points of the critical region. In Define Shaded Area By, select Probability and Both Tails.In Minitab, choose: Graph > Probability Distribution Plot > View Probability.So, that’s 24 degrees of freedom for our sample of 25. For a 1-sample t-test, the degrees of freedom equals the sample size minus 1. To create a graphical equivalent to a 1-sample t-test, we’ll need to graph the t-distribution using the correct number of degrees of freedom. How to Graph the Two-Tailed Critical Region for a Significance Level of 0.05 We’ll perform the regular 1-sample t-test with a null hypothesis mean of 260, and then graphically recreate the results. The data for this example is FamilyEnerg圜ost and it is just one of the many data set examples that can be found in Minitab’s Data Set Library. If you’d like more information about the formulas that are involved, you can find them in Minitab at: Help > Methods and Formulas > Basic Statistics > 1-Sample t. To create the following graphs, we’ll use Minitab’s probability distribution plots in conjunction with several statistics obtained from the 1-sample t output. Understanding Hypothesis Tests: Confidence Intervals and Confidence Levels.Understanding Hypothesis Tests: The Significance Level and P Values. ![]() Understanding Hypothesis Tests: Why We Need to Use Hypothesis Tests.If you’d instead like to gain a better understanding of the concepts behind the graphs, please see the following posts: It’s a fairly technical and task-oriented post designed for those who need to create the graphs for illustrative purposes. This post focuses entirely on the steps required to create the graphs. In this series, I create a graphical equivalent to a 1-sample t-test and confidence interval to help you understand how it works more intuitively. This is a engage post for a series of blog posts about understanding hypothesis tests. ![]()
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