What is p-value from Z table?

If your test statistic is positive, first find the probability that Z is greater than your test statistic (look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one). Then double this result to get the p-value.

How do you find the test statistic on a TI-Nspire?

Start a new calculator document on your TI-Nspire. Then, press b and select 6: Statistics followed by 7: Stat Tests. Select 2: t Test, and indicate that you will be using Stats as the data input method.

What is the test statistic formula?

Standardized Test Statistic Formula The general formula is: Standardized test statistic: (statistic-parameter)/(standard deviation of the statistic). The formula by itself doesn’t mean much, unless you also know the three major forms of the equation for z-scores and t-scores.

How to use the TI-Nspire stat test calculator?

2. Start a new document on your TI-Nspire, and add a calculator window. Press the b key and select 6: Statisticsfollowed by 7: Stat Tests. We’ll be using option 5: 1-Prop z Test. 3. Your calculator will prompt you for the following information: •p 0:Enter the numerical value of the population proportion that was used in your statements of H 0and H

How to calculate the amortization table in TI-Nspire?

From the Finance tab, select Amortization Table. This will paste the command to the Calculator screen (also available from Catalog with syntax shown opposite) The syntax refers to: NPmt – the number of payments you want displayed on the screen, starting from the first payment (the example shows 30.

When to reject the null hypothesis in TI-Nspire?

If the P-value is less than or equal to alpha, you will. reject the null hypothesis (H0) and conclude that the sample data support the alternative hypothesis. If the P-value is greater than alpha, you must fail to reject H0 and conclude that the sample data are not consistent with the alternative hypothesis.

What is the syntax for normcdf in TI-Nspire?

The syntax used is normCdf(lowerbound,upperbound,μ,σ). The resulting area corresponds to the probability of randomly selecting a value between the specified lower and upper bounds. You can also interpret this area as the percentage of all values that fall between the two specified boundaries.

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