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Chapter 13: Spreadsheet Application
240
Linear Regression
t
Test
In a spreadsheet, input paired-variable data (page 237 (c) or (d)) as test data.
1301
To perform a Linear Regression
t
Test on the paired-variable data
shown in the nearby screenshot
χ
2
Test
For test data, input data of at least one row and two columns (observed matrix) into the spreadsheet. You can
see an actual example (
0704
) of how this is done using the Statistics application in Chapter 7 of this manual.
χ
2
GOF Test
For test data, input two lists (observed list and expected list) into the spreadsheet. This is equivalent to inputting
two rows of data of
n
lines. You can see an actual example (
0705
) of how this is done using the Statistics
application in Chapter 7 of this manual.
One-Way ANOVA
For test data, input data (Factor A) into the spreadsheet that is
n
rows
×
2 columns up to
n
×
6 columns. You
can see an actual example (
0706
) of how this is done using the Statistics application in Chapter 7 of this
manual.
Two-Way ANOVA
When (Factor A count) × (Factor B count) =
m
, prepare test data that is
n
rows ×
m
columns. For example, when there are Factor A items and one Factor B
items, input six columns of data into the spreadsheet. For example, select
A1:F3 on the spreadsheet and run the wizard. This will automatically assign
the cell references shown below to each factor.
Factor B1
Factor B2
Factor B3
Factor A1
A1:A3
B1:B3
C1:C3
Factor A2
D1:D3
E1:E3
F1:F3
1302
To perform the same calculation as Statistics application example
0707
using the Spreadsheet
application
Distribution Calculations
For details about the different types of distribution calculations, see “Distributions” (page 145).
u
To perform a distribution calculation and paste the results into the spreadsheet
1. Enter the single-variable or paired-variable data into the spreadsheet, and then select the range of cells
where it is located.
• The following shows the types of data that correspond to each type of distribution calculation, and the
variables that will be assigned the range of selected data.
Distribution Type
Data
Variable Assignment
Probability Density
Single-variable
x
Cumulative Distribution
Paired-variable
Lower, Upper
Inverse Cumulative Distribution
Single-variable
prob