As described in One Sample Hypothesis Testing, the confidence interval is given by

estimated mean ± critical value ∙ std error

Alternatively by Property 1 of F Distribution, we can use the following as the critical value

We could have used as the estimate of the variance for the *j*th group in the calculation of the standard error, but since it is assumed that the variances of all the groups are equal, *MS _{W}* is an estimate of group

*j*based on a larger sample than , and so by the Law of Large Numbers,

*MS*provides a better estimate of the

_{W}*j*th group variance than .

**Example 1**: Find the confidence intervals for each of the methods in Example 3 of Basic Concepts for ANOVA.

To do this we use the ANOVA data analysis tool found in the Real Statistics Resource Pack. Enter **Ctrl-m** and then double click on **Analysis of Variance. **Next select **Anova: one factor **from the dialog box that appears. as in Figure 0.

**Figure 0 – Analysis of Variance dialog box**

The dialog box shown in Figure 1 is now displayed.

**Figure 1 – Dialog box for Single Factor Anova**

Enter A3:D11 in the **Input Range**, select **Excel format with column headings **as the **Input Format**, select the **ANOVA** option and click on **OK**. The output appears as shown in Figure 2.

**Figure 2 – Output of Real Statistics ANOVA data analysis tool**

The confidence intervals are given in the range M7:N10. E.g. the confidence interval for Method 1 is calculated as follows:

*t _{crit} *= TINV(

*α*,

*df*) = TINV(.05, 25) = 2.06

_{W}Confidence interval = *x̄ _{i}* ±

*t*

_{crit }∙

*s.e.*= 61.29 ± 2.06∙ 5.03 = (50.92, 71.65)

Note that most of the rest of the output in Figure 2 is similar to that found in the standard Excel data analysis tool (see, for example, Figure 5 of Basic Concepts for ANOVA). The entry for RMSSE is explained in Effect Size for ANOVA, while the entry for Omega Sq is explained in Other Measures of Effect Size for ANOVA.

**Example 2**: Conduct ANOVA for the data in the range A3:B31 of Figure 3 (only the first 20 elements are shown). This time the data is stored in what we will call **stacked** or **standard format**. Here the first column contains group names and the second column contains the corresponding scores. The data can be listed in any order.

**Figure 3 – ANOVA for input in standard format**

To conduct the analysis, click on cell D3 (where the output will start), enter **Ctrl-m** and select **Analysis of Variance** and press the **OK** button. Next select **Single Factor Anova **from the dialog box that appears. A dialog box will then appear similar to that shown in Figure 1. This time enter A3:B31 in the **Input Range**, select **Standard format **as the **Input Format**, deselect **Columns/row headings included with data**, select the **ANOVA** option and click on **OK**. The output appears in the rest of Figure 3.

Note that the data analysis tool first converts the input data from standard format to the usual Excel Anova format with column headings (range D5:G13). Since this data is the same as that used in Example 1, the ANOVA results are the same as shown in Figure 3.

**Real Statistics Functions**: The Real Statistics Resource Pack contains the following two array functions for converting between Single Factor Anova format and standard format.

**StdAnova1**(R1) = takes the data in R1 which is in standard format and outputs an array with the same data in Single Factor Anova format.

**Anova1Std**(R1): takes the data in R1 which is in Single Factor Anova format and outputs an array with the same data in standard format.

**Observation**: Referring to Figure 3, StdAnova1(A3:B31) yields the results shown in D5:G13. If you highlight the range S5:T35 and enter the array formula Anova1Std(D5:G3), the result is shown in Figure 4.

**Figure 4 – Data in standard format**

Note that the result is the same as range A3:B31, but in sorted order. Also note that any extra entries are filled in with #N/A.

**Real Statistics Functions**: In One-way ANOVA Basic Concepts we described the functions SSW, SSBet and SSTot, which can be used for data in Excel format. The following functions provide the same functionality for data in standard format.

**SSWStd**(R1, *col*) = *SS _{W}*

**SSBetStd**(R1, *col*) = *SS _{Bet }*

**SSTotStd**(R1, *col*) =* SS _{Tot}*

Here the first column of range R1 contains the names of the factor levels and the other columns contain data values. The column number *col* contains the data for the one-way ANOVA that we want to perform. If *col* is omitted it defaults to 2 (the second column).

For the data in Figure 4, the formula =SSWStd(S5:T33) yields the value 4429.137, as seen in cell J17 of Figure 3.

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Hi Charles,

Could you please state the exact form of confidence interval for a ~difference of means~ between two groups? For instance, in your e.g. between method1 and method2; what would be the t statistic, the df, the relevant n value for the s.e statistic? Is it like in an independent t test? When df=n1+n2-2 etc.? I’m trying to figure it out with the numbers and seem to get lost.. SPSS claims the mean difference between those groups is -16.589, which I obviously understand, but the 0.05 CI is [-35.537,2.3592] (one side equals to 18.948).

Would highly appreciate your explanation.

Best regards and many thanks in advance,

Yohay.

Hi Yohay,

The referenced webpage explains how to calculate the confidence interval for the mean of each single method. This is like a one sample t test.

You are asking about the confidence interval for a difference between group means. Since the groups are independent, this is like a two independent samples t test. This is considered a follow up to a ANOVA, and is discussed on a different webpage, namely the webpage http://www.real-statistics.com/one-way-analysis-of-variance-anova/planned-comparisons/. (You need to consider the comparison where one method gets comparison coefficient +1 and the other gets comparison coefficient -1). Please read that webpage and let me know whether it answers your question.

Charles

Zaiontz:

i really like your array formulas–great adds! i might be misunderstanding something about them, however: For, say,

nrows where there are, say, 3 missing datapoints, i thinkStdAnova1(R1)givesndatapoints with 3 zeroesinsteadof (n-3) datapoints with 3 nulls. Is that correct? If so, how do we get around this?Nate,

Before using the StdAnova1 function or Anova data analysis tool, you could eliminate missing data by using the DELROWBLANK function (assuming the missing data are blanks). Alternatively you can use the Reformatting a Data Range by Rows data analysis tool (accessible by pressing Ctrl-m).

Charles

Suppose we take the data in the standard format and compute the squared deviations from the overall mean divided by the overall sample variance to obtain an “expected” series. Then do the same using observed sample means and variance for each group as appropriate to obtain an observed series. If we now apply the Ch-sq. goodness of fit test to the two series, would that be equivalent to ANOVA? Would it make sense, anyway?

Have you tried doing this?

I just tried and the results are completely different.

Charles

Dear Charles, many thanks for this awesome software.

I have a question related to the calculation of the confidence intervals.

Performing the ANOVA in Minitab or JMP it can be seen, that the intervals are slightly different (narrower) to the one calculated with your add-on. Can you (or someone other) give me an explanation of this behavior.

Thanks Heinrich

Heinrich,

Since I am not familiar with Minitab or JMP, can you send me an Excel file with your data and the results you have received using Excel? Also please send me the results you got from Mintab and JMP. I will then try to figure what is happening.

You can get my email address from the Contact Us webpage.

Charles

Dear Charles,

I have data from fetilizer trail on vegetable, five levels of fertilizer are my treatments, replicated three times. The data was taken on three growth parameters (number of leaves, stem girth and plant height) and two yield parameters (leaf weight and stem weight). My question is:

1. Do I use one-way or two way ANOBA?

2. Should I analyse the whole data once or one by one (i.e to analyse data on number of leaves alone, then stem girth, and so on or analyse them together?

Thank you

Gumel,

1. It really depends on what hypotheses you want to test. Based on your description, my may also consider Manova

2. Again it depends on what hypotheses you want to test.

Charles