Data Statistics in Calc

Use the data statistics in Calc to perform complex data analysis

To work on a complex statistical or engineering analysis, you can save steps and time by using Calc Data Statistics. You provide the data and parameters for each analysis, and the set of tools uses the appropriate statistical or engineering functions to calculate and display the results in an output table.

Sampling

Create a table with data sampled from another table.

To access this command...

Choose Data - Statistics - Sampling


Sampling allows you to pick data from a source table to fill a target table. The sampling can be random or in a periodic basis.

Note Icon

Sampling is done row-wise. That means, the sampled data will pick the whole line of the source table and copy into a line of the target table.


Data

Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

Sampling Method

Random: Picks exactly Sample Size lines of the source table in a random way.

Sample size: Number of lines sampled from the source table.

Periodic: Picks lines in a pace defined by Period.

Period: the number of lines to skip periodically when sampling.

Example

The following data will be used as example of source data table for sampling:

A

B

C

1

11

21

31

2

12

22

32

3

13

23

33

4

14

24

34

5

15

25

35

6

16

26

36

7

17

27

37

8

18

28

38

9

19

29

39


Sampling with a period of 2 will result in the following table:

12

22

32

14

24

34

16

26

36

18

28

38


Descriptive Statistics

Fill a table in the spreadsheet with the main statistical properties of the data set.

To access this command...

Choose Data - Statistics - Descriptive Statistics


The Descriptive Statistics analysis tool generates a report of univariate statistics for data in the input range, providing information about the central tendency and variability of your data.

Note Icon

For more information on descriptive statistics, refer to the corresponding Wikipedia article.


Data

Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Example

The following data will be used as example

A

B

C

1

Maths

Physics

Biology

2

47

67

33

3

36

68

42

4

40

65

44

5

39

64

60

6

38

43

7

47

84

62

8

29

80

51

9

27

49

40

10

57

49

12

11

56

33

60

12

57

13

26


The following table displays the results of the descriptive statistics of the sample data above.

Column 1

Column 2

Column 3

Mean

41.9090909091

59.7

44.7

Standard Error

3.5610380138

5.3583786934

4.7680650629

Mode

47

49

60

Median

40

64.5

43.5

Variance

139.4909090909

287.1222222222

227.3444444444

Standard Deviation

11.8106269559

16.944681237

15.0779456308

Kurtosis

-1.4621677981

-0.9415988746

1.418052719

Skewness

0.0152409533

-0.2226426904

-0.9766803373

Range

31

51

50

Minimum

26

33

12

Maximum

57

84

62

Sum

461

597

447

Count

11

10

10


Analysis of Variance (ANOVA)

Produces the analysis of variance (ANOVA) of a given data set

To access this command...

Choose Data - Statistics - Analysis of Variance (ANOVA)


ANOVA is the acronym for ANalysis Of VAriance. This tool produces the analysis of variance of a given data set

Note Icon

For more information on ANOVA, refer to the corresponding Wikipedia article.


Data

Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Type

Select if the analysis is for a single factor or for two factor ANOVA.

Parameters

Alpha: the level of significance of the test.

Rows per sample: Define how many rows a sample has.

Example

The following data will be used as example

A

B

C

1

Maths

Physics

Biology

2

47

67

33

3

36

68

42

4

40

65

44

5

39

64

60

6

38

43

7

47

84

62

8

29

80

51

9

27

49

40

10

57

49

12

11

56

33

60

12

57

13

26


The following table displays the results of the analysis of variance (ANOVA) of the sample data above.

ANOVA - Single Factor

Alpha

0.05

Groups

Count

Sum

Mean

Variance

Column 1

11

461

41.9090909091

139.4909090909

Column 2

10

597

59.7

287.1222222222

Column 3

10

447

44.7

227.3444444444

Source of Variation

SS

df

MS

F

P-value

F-critical

Between Groups

1876.5683284457

2

938.2841642229

4.3604117704

0.0224614952

3.340385558

Within Groups

6025.1090909091

28

215.1824675325

Total

7901.6774193548

30


Correlation

Calculates the correlation of two sets of numeric data.

To access this command...

Choose Data - Statistics - Correlation


The correlation coefficient (a value between -1 and +1) means how strongly two variables are related to each other. You can use the CORREL function or the Data Statistics to find the correlation coefficient between two variables.

A correlation coefficient of +1 indicates a perfect positive correlation.

A correlation coefficient of -1 indicates a perfect negative correlation

Note Icon

For more information on statistical correlation, refer to the corresponding Wikipedia article.


Data

Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Example

The following data will be used as example

A

B

C

1

Maths

Physics

Biology

2

47

67

33

3

36

68

42

4

40

65

44

5

39

64

60

6

38

43

7

47

84

62

8

29

80

51

9

27

49

40

10

57

49

12

11

56

33

60

12

57

13

26


The following table displays the results of the correlation of the sample data above.

Correlations

Column 1

Column 2

Column 3

Column 1

1

Column 2

-0.4029254917

1

Column 3

-0.2107642836

0.2309714048

1


Covariance

Calculates the covariance of two sets of numeric data.

To access this command...

Choose Data - Statistics - Covariance


The covariance is a measure of how much two random variables change together.

Note Icon

For more information on statistical covariance, refer to the corresponding Wikipedia article.


Data

Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Example

The following data will be used as example

A

B

C

1

Maths

Physics

Biology

2

47

67

33

3

36

68

42

4

40

65

44

5

39

64

60

6

38

43

7

47

84

62

8

29

80

51

9

27

49

40

10

57

49

12

11

56

33

60

12

57

13

26


The following table displays the results of the covariance of the sample data above.

Covariances

Column 1

Column 2

Column 3

Column 1

126.8099173554

Column 2

-61.4444444444

258.41

Column 3

-32

53.11

204.61


Exponential Smoothing

Results in a smoothed data series

To access this command...

Choose Data - Statistics - Exponential Smoothing


Exponential smoothing is a filtering technique that when applied to a data set, produces smoothed results. It is employed in many domains such as stock market, economics and in sampled measurements.

Note Icon

For more information on exponential smoothing, refer to the corresponding Wikipedia article.


Data

Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Parameters

Smoothing Factor: A parameter between 0 and 1 that represents the damping factor Alpha in the smoothing equation.

Example

The following table has two time series, one representing an impulse function at time t=0 and the other an impulse function at time t=2.

A

B

1

1

0

2

0

0

3

0

1

4

0

0

5

0

0

6

0

0

7

0

0

8

0

0

9

0

0

10

0

0

11

0

0

12

0

0

13

0

0


The resulting smoothing is below with smoothing factor as 0.5:

Alpha

0.5

Column 1

Column 2

1

0

1

0

0.5

0

0.25

0.5

0.125

0.25

0.0625

0.125

0.03125

0.0625

0.015625

0.03125

0.0078125

0.015625

0.00390625

0.0078125

0.001953125

0.00390625

0.0009765625

0.001953125

0.0004882813

0.0009765625

0.0002441406

0.0004882813


Moving Average

Calculates the moving average of a time series

To access this command...

Choose Data - Statistics - Moving Average


Note Icon

For more information on the moving average, refer to the corresponding Wikipedia article.


Data

Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Parameters

Interval: The number of samples used in the moving average calculation.

Example

The following table has two time series, one representing an impulse function at time t=0 and the other an impulse function at time t=2.

A

B

1

1

0

2

0

0

3

0

1

4

0

0

5

0

0

6

0

0

7

0

0

8

0

0

9

0

0

10

0

0

11

0

0

12

0

0

13

0

0


Results of the moving average:

Column 1

Column 2

#N/A

#N/A

0.3333333333

0.3333333333

0

0.3333333333

0

0.3333333333

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

#N/A

#N/A


Paired t-test

Calculates the paired t-Test of two data samples.

To access this command...

Choose Data - Statistics - Paired t-test


A paired t-test is any statistical hypothesis test that follows a Student's t distribution.

Note Icon

For more information on paired t-tests, refer to the corresponding Wikipedia article.


Data

Variable 1 range: The reference of the range of the first data series to analyze.

Variable 2 range: The reference of the range of the second data series to analyze.

Results to: The reference of the top left cell of the range where the test will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Example

The following table has two data sets.

A

B

1

28

19

2

26

13

3

31

12

4

23

5

5

20

34

6

27

31

7

28

31

8

14

12

9

4

24

10

0

23

11

2

19

12

8

10

13

9

33


Results for paired t-test:

The following table shows the paired t-test for the data series above:

paired t-test

Alpha

0.05

Hypothesized Mean Difference

0

Variable 1

Variable 2

Mean

16.9230769231

20.4615384615

Variance

125.0769230769

94.4358974359

Observations

13

13

Pearson Correlation

-0.0617539772

Observed Mean Difference

-3.5384615385

Variance of the Differences

232.9358974359

df

12

t Stat

-0.8359262137

P (T<=t) one-tail

0.2097651442

t Critical one-tail

1.7822875556

P (T<=t) two-tail

0.4195302884

t Critical two-tail

2.1788128297


F-test

Calculates the F-Test of two data samples.

To access this command...

Choose Data - Statistics - F-test


A F-test is any statistical test based on the F-distribution under the null hypothesis.

Note Icon

For more information on F-tests, refer to the corresponding Wikipedia article.


Data

Variable 1 range: The reference of the range of the first data series to analyze.

Variable 2 range: The reference of the range of the second data series to analyze.

Results to: The reference of the top left cell of the range where the test will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Example

The following table has two data sets.

A

B

1

28

19

2

26

13

3

31

12

4

23

5

5

20

34

6

27

31

7

28

31

8

14

12

9

4

24

10

0

23

11

2

19

12

8

10

13

9

33


Results for F-Test:

The following table shows the F-Test for the data series above:

Ftest

Alpha

0.05

Variable 1

Variable 2

Mean

16.9230769231

20.4615384615

Variance

125.0769230769

94.4358974359

Observations

13

13

df

12

12

F

1.3244637524

P (F<=f) right-tail

0.3170614146

F Critical right-tail

2.6866371125

P (F<=f) left-tail

0.6829385854

F Critical left-tail

0.3722125312

P two-tail

0.6341228293

F Critical two-tail

0.3051313549

3.277277094


Z-test

Calculates the z-Test of two data samples.

To access this command...

Choose Data - Statistics - Z-test


Note Icon

For more information on Z-tests, refer to the corresponding Wikipedia article.


Data

Variable 1 range: The reference of the range of the first data series to analyze.

Variable 2 range: The reference of the range of the second data series to analyze.

Results to: The reference of the top left cell of the range where the test will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Example

The following table has two data sets.

A

B

1

28

19

2

26

13

3

31

12

4

23

5

5

20

34

6

27

31

7

28

31

8

14

12

9

4

24

10

0

23

11

2

19

12

8

10

13

9

33


Results for z-Test:

The following table shows the z-Test for the data series above:

z-test

Alpha

0.05

Hypothesized Mean Difference

0

Variable 1

Variable 2

Known Variance

0

0

Mean

16.9230769231

20.4615384615

Observations

13

13

Observed Mean Difference

-3.5384615385

z

#DIV/0!

P (Z<=z) one-tail

#DIV/0!

z Critical one-tail

1.644853627

P (Z<=z) two-tail

#DIV/0!

z Critical two-tail

1.9599639845


Chi-square test

Calculates the Chi-square test of a data sample.

To access this command...

Choose Data - Statistics - Chi-square Test


Note Icon

For more information on chi-square tests, refer to the corresponding Wikipedia article.


Data

Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

Grouped By

Select whether the input data has columns or rows layout.

Example

The following table has two data sets.

A

B

1

28

19

2

26

13

3

31

12

4

23

5

5

20

34

6

27

31

7

28

31

8

14

12

9

4

24

10

0

23

11

2

19

12

8

10

13

9

33


Results for Chi-square Test:

Test of Independence (Chi-Square)

Alpha

0.05

df

12

P-value

2.32567054678584E-014

Test Statistic

91.6870055842

Critical Value

21.0260698175


Please support us!