Microsoft Excel provides a set of analytical tools – Analysis ToolPak – that you can use to easily and rapidly perform complex statistical or engineering analysis. You specify the data and parameters for analysis, appropriate statistical or engineering macro functions used in the tool and display the results in an output table. Some tools generate charts in addition to the output table also.

Related worksheet functions Excel provides many other statistical, financial and technical worksheet functions. Some of the statistical functions are built, others you will have access to when you install the Analysis ToolPak. Develop data analysis tools Analysis ToolPak includes the tools described below. You access them by clicking on the Data Analysis Tools menu. If the Data Analysis command is not available, you must download the Analysis ToolPak.

Using the analytical tools Anova you can make different types of variance analysis. Which tool you use depends on how many factors and samples you have from the populations you want to test.

Anova: Single Factor

With this tool, do a simple analysis of datavariansen between two or more samples. The analysis tested the hypothesis that each sample is taken from the same underlying probability distribution against the alternative hypothesis that the underlying probability distribution is not the same for all samples. If there are only two samples, you can use the worksheet function, TTEST. However, if you have more than two samples, you should use the Single Factor Anova instead because generalizations can be made with the TTEST.

Anova: Two-Factor With Replication

This analysis tool is useful when data can be classified into two different dimensions. In an experiment to measure plant height is given seedlings fertilizers of different types (eg A, B and C). The plants are placed even in places with different temperatures (low and high). For each of these six possible pairs {fertilizer and temperature}, we have an equal number of observations of plant height. Using Anova tool we can test the following:

1st If plant height for the different fertilizers come from the same underlying population. The temperature is ignored in this analysis.

2nd If plant height for the different temperature levels from the same underlying population. Fertilizer ignored in this analysis.

3rd When we took into account the effects of different fertilizers in step 1 and the different temperatures in steps 2 retrieved six samples that meet all value pairs of {fertilizer and temperature} from the same population. The alternative hypothesis is that the effects achieved on the basis of specific pairs {fertilizer and temperature} in addition to the differences that are based on only fertilizer or only temperature.

Setting the input area for the tool Anova

Anova: Two-Factor Without Replication This analysis tool is useful when data are classified into two different dimensions in the case of Two-Factor With Replication. When this tool is used, we assume that there is only one observation for each pair (for example, each {fertilizer, temperature} pairs in the example above). Using this tool, you can apply the tests in steps 1 and 2 of the case Anova: Two-Factor With Replication but have not yet enough data to use the test in step 3.

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