Data analysis

Logic Sheet can be used to perform all kinds of data analysis tasks, including ANOVA, or Analysis of variance, correlation, covariance, and descriptive statistics.

Logic Sheet can be used to perform all kinds of data analysis tasks, including ANOVA, or Analysis of variance, correlation, covariance, and descriptive statistics.

One-way ANOVA

One-way ANOVA, or single-factor ANOVA, is used to determine whether there are any statistically significant differences between the means of several unrelated groups.

Input range: Select the input range of your data. A range represents a single cell or a group of adjacent cells in your spreadsheet. For example, A1:D10 is shown below.

Grouped by: Choose how the data is grouped, it can either be in columns or in rows.

Data include labels: Check this if the input range you have selected includes data labels. Please make sure you don’t include non-numeric cells if you uncheck this box.

Alpha value: Alpha(α), or the significance level, is the probability of rejecting the null hypothesis when it is true. For example, an alpha value of 0.05 means that a 5% risk of concluding that a difference exists when there is no actual difference. In ANOVA, the alpha value is used to calculate the F crit value in the output.

Output range: Set the cell range from which the ANOVA table will be displayed. Make sure there is enough space for output data. For example: A12. The output will take at least 15 rows and 7 columns. It could be larger depending on the size of the input. (See example above)

Two-way ANOVA without replication

Two-way ANOVA, or two-factor ANOVA, is used to analyze the difference between the means of more than two groups. It is also used to estimate how two independent variables, in combination, affect a dependent variable.

Input range: Select the input range of your data. A range represents a single cell or a group of adjacent cells in your spreadsheet. For example, A1:D10 is shown below.

Grouped by: Choose how the data is grouped, it can either be in columns or in rows.

Data include labels: Check this if the input range you have selected includes data labels. Please make sure you don’t include non-numeric cells if you uncheck this box.

Alpha value: Alpha(α), or the significance level, is the probability of rejecting the null hypothesis when it is true. For example, an alpha value of 0.05 means that a 5% risk of concluding that a difference exists when there is no actual difference. In ANOVA, the alpha value is used to calculate the F crit value in the output.

Output range: Set the cell range from which the ANOVA table will be displayed. Make sure there is enough space for output data. For example: A12. The output will take at least 17 rows and 7 columns. It could be larger depending on the size of the input. (See example above)

Correlation

Correlation is used to find the correlation coefficient between two or more variables.

Input range: Select the input range of your data. A range represents a single cell or a group of adjacent cells in your spreadsheet. For example, A1:D10 is shown below.

Grouped by: Choose how the data is grouped, it can either be in columns or in rows.

Data include labels: Check this if the input range you have selected includes data labels. Please make sure you don’t include non-numeric cells if you uncheck this box.

Output range: Set the cell range from which the correlation results will be displayed. Make sure there is enough space for output data. For example: A12. The output will take at least 3 rows and 3 columns. It could be larger depending on the size of the input. (See example above)

Covariance

The Covariance tool quantifies the relationship between two sets of values.

Input range: Select the input range of your data. A range represents a single cell or a group of adjacent cells in your spreadsheet. For example, A1:D10 is shown below.

Grouped by: Choose how the data is grouped, it can either be in columns or in rows.

Data include labels: Check this if the input range you have selected includes data labels. Please make sure you don’t include non-numeric cells if you uncheck this box.

Output range: Set the cell range from which the covariance results will be displayed. Make sure there is enough space for output data. For example: A12. The output will take at least 3 rows and 3 columns. It could be larger depending on the size of the input. (See example above)

Descriptive statistics

Descriptive statistics describe the basic features of the data. Features include Mean, Standard Error, Median, Mode, Standard Deviation, Sample Variance, Kurtosis, Skewness, Range, Confidence Level, and more.

Input range: Select the input range of your data. A range represents a single cell or a group of adjacent cells in your spreadsheet. For example, A1:D10 is shown below.

Grouped by: Choose how the data is grouped, it can either be in columns or in rows.

Data include labels: Check this if the input range you have selected includes data labels. Please make sure you don’t include non-numeric cells if you uncheck this box.

Output range: Set the cell range from which the covariance results will be displayed. Make sure there is enough space for output data. For example: A12. The output will take at least 18 rows and 2 columns. It could be larger depending on the size of the input. (See example above)

Kth Largest: Returns the k’th largest number in the given group of numbers. For example, if the group of numbers are [7, 10, 4, 3, 20, 15], and when Kth Largest = 3, the output will be 10.

Kth Largest: Returns the k’th smallest number in the given group of numbers. For example, if the group of numbers are [7, 10, 4, 3, 20, 15], and when Kth Largest = 3, the output will be 7.

Confidence Level for Mean: The confidence level represents the theoretical long-run frequency (i.e., the proportion) of confidence intervals that contain the true value of the unknown population parameter. For example, 90% of confidence intervals computed at the 90% confidence level contain the parameter, 95% of confidence intervals computed at the 95% confidence level contain the parameter. The confidence level is selected before examining the data. Most commonly, a 95% confidence level is used.