Excel p value calculation function

p(t<=t)

Single-tailed. p(t<=t)

Twin tails. This is the p-value, that is, the p-value is very small, and it is the p-value that rejects the null hypothesis.

1. Open Excel** and enter the two sets of data you need.

2. Select a blank**, select the function fx, and select the category: statistics.

3. Select ttest, click on the first set of values, and drag the mouse to select the first set of box data. Again, click on the second set of values to select the second set of box data.

4. Enter "2" for both the mantissa and the type, and click to confirm. If p is less than, it means that the two sets of data are very significantly different.

5. If the p-value indicates that the two sets of data are significantly different. If the p-value is greater than that, it means that there is a non-significant difference between the two sets of data.

1. Create Excel, which contains two columns of data, namely, 1 in the first column, 2 columns, 2 <>, 1 columns, and 2 columns, and insert sample data respectively, column 1 and column 2 <>

3. In cell H2, insert the function, select 'Other Functions', <4, select the function category as the first companion 'Statistics', the function name is 'ttest', < 5, the first group of data, select 'D2:D7', the second group of numbers is clear, select 'E2:E7', the mantissa is written as 2, click the OK button, and <>

6. The contents of cell H2 are, =ttest(d2:d7,e2:e7,2,2), and the p value is '

In the "Excel Options", there is an "Add-in" item, which can be used to view and manage the add-in, including the "Analysis Tool Library". Double-click on it to open 1 window, select what you want, and click "OK".

p(t<=t)

One-tailed p(t<=t).

This is the p-value, that is, the p-value is very small, and it is the p-value that rejects the null hypothesis.

The detailed operation steps are as follows:

1. If it is Office2010, select the file, click the option to open, and find a "Analysis Tool Library" after the add-in, and click "Go";

2. If it is Office2003, click Tools to open the add-in;

3. Use the "Analysis Tool Library" and click "OK";

4. In the "Data" menu, find "Data Analysis";

5. Click Select to start the analysis.

I don't study statistics, but I learned a little bit when I was studying jmp some time ago, through your steps, I found that it is problematic to calculate your free weight in this way, in fact, the degree of freedom should be (r-1)*(c-1), so I think you should first convert the data into the form of a contingency table, as shown in the figure, only then can you calculate the p value you want with the correct degrees of freedom:

1. Create Excel, which contains two columns of data, namely, Column 1, Column 2, Column 2, Column 1, Column 2, and insert sample data respectively, Column 1 and Column 2

3. In cell H2, insert the function and select 'Other Functions', 4. Select the function category as 'Statistics', the function name as 'ttest', 5. The first set of data, select 'D2:D7', the second group of data, select 'E2:E7', the mantissa is written as 2, click the OK button, 6. The content of H2 cell is, =ttest(D2:D7,E2:E7,2,2), and the P value is '

Hypothesis testing is an important part of inferential statistics.

Hypothesis testing with professional statistical software such as SAS and SPSS is commonly used in hypothesis testing, and the p-value method (Probability, PR) is commonly used in hypothesis testing, which is due to the fact that it is easier to apply to computer software.

According to the significance test, the p-value obtained by the statistical significance test method is generally significant with p < and p < is very significant, which means that the probability that the difference between samples is caused by sampling error is less than or.

In fact, the p-value does not give any importance to the data, only the probability of an event occurring. It is false to say that the differences between samples are greater at p < than at p <.

The statistics show PR > f, which can also be written as pr(>f), p = p or p = p.

The following is a list of how the p-value is calculated.

1) The p-value is:

1) A probability, a probability of an observed sample and more extreme cases occurring on the assumption that the null hypothesis is true.

2) Reject the minimum significance level of the null hypothesis.

3) The level of significance observed (of the instance).

4) Indicates the degree of support for the null hypothesis and is another method used to determine whether the null hypothesis should be rejected.

5) Note: The p-value is not the probability that the null hypothesis is true when the sample result is given, but the probability that the sample result will appear when the null hypothesis is true.

2) Calculation of p-value:

In general, x is used to denote the test statistic, and when h0 is true, the value c of the statistic can be calculated from the sample data, and the p value can be calculated according to the specific distribution of the test statistic x. Specifically:

The p-value of the test on the left is the probability that the test statistic x is less than the sample statistic c, i.e., p = p

The p-value of the test on the right is the probability that the test statistic x is greater than the sample statistic c: p = p

The p-value of the two-sided test is 2 times the probability that the test statistic x falls within the tail region of the sample statistic c is the endpoint: p = 2p (when c is at the right end of the distribution curve) or p = 2p (when c is at the left end of the distribution curve). If x follows a normal and t distribution, its distribution curve is symmetric with respect to the vertical axis, so its p value can be expressed as p = p.

Once the p-value has been calculated, the given level of significance is compared to the p-value to make the conclusion of the test

If the p-value is >, the null hypothesis is rejected at the significance level.

If p-value, the null hypothesis is accepted at the significance level.

In practice, when = p-value, i.e., the value c of the statistic is just equal to the critical value, it is prudent to increase the sample size and re-sample the test.

Open Excel, switch to the worksheet containing the formula, and enter the formula as needed.

Switch to the "Formulas" tab and click the "Show Formulas" button.

When the "Show Formulas" button is pressed, the formulas in the cell will be displayed.

The calculation of the p-value should be divided into several cases. The specific calculation method is as follows:

1. The left test h0: 0 vs h1: <0p value is the probability that the test statistic is less than or equal to the test statistic calculated based on the actual observed sample data when = 0, that is, p value = p(z zc|).μ=μ0)

2. The test value h0: 0 vs h1: >0p on the right side is the probability that the test statistic is greater than or equal to the test statistic calculated from the actual observed sample data when = 0, that is, p value = p(z zc|).μ=μ0)

3. The two-sided test h0: = 0 vs h1: ≠0p value is the probability that when = 0, the test statistic is greater than or equal to the test statistic value calculated from the actual observed sample data, that is, p value = 2p(z |zc||μ=μ0)

You have to look for the number of statistical tests.

p=a(1+i)^-1+a(1+i)^-2+…+a(1+i)^(n-1)+a(1+i)^-n

p=a*[1-(1+i) -n] i

Among them, [1-(1+i) -n] i is the present value of the ordinary annuity of 1 yuan mu, also known as the present value coefficient of the megabisen annuity.

It is represented by symbols (p a, i, n).

Table A contains the annuity, P represents the present value, 10% in the question represents i, and 10 represents n