Texas Instruments NS/CLM/1L1/B Reference Guide - Page 48

LinRegtTest, Catalog &gt, Output variable, Description

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LinRegtTest LinRegtTest X,Y[,Freq[,Hypoth]] Computes the linear regression line fit of the data point pairs, where y(k) = a + b·x(k) and tests the null hypotheses H0: b = 0 against one of the following three alternatives: Hypoth > 0 is Ha: s1 > s2 Hypoth = 0 is Ha: s1 ƒ s2 (default) Hypoth < 0 is Ha: s1 < s2 A summary of results is stored in the stat.results variable. (See page 76.) Output variable stat.RegEqn stat.a, stat.b stat.df stat.s stat.t stat.PVal stat.r stat.r2 stat.SESlope stat.Resid Description regression equation: a + b·x Regression line fit offset and slope parameter estimates Degrees-of-freedom Fit error standard deviation for y - (a + b·x) T-Statistic for slope significance Probability the alternate hypothesis is false Linear regression correlation coefficient Coefficient of determination SE Slope = s/sqrt(sum(x-bar)2) residuals of linear fit @List( ) @List(List1) ⇒ list Returns a list containing the differences between consecutive elements in List1. Each element of List1 is subtracted from the next element of List1. The resulting list is always one element shorter than the original List1. list4mat() list4mat(List [, elementsPerRow]) ⇒ matrix Returns a matrix filled row-by-row with the elements from List. elementsPerRow, if included, specifies the number of elements per row. Default is the number of elements in List (one row). If List does not fill the resulting matrix, zeros are added. Catalog > Catalog > Catalog > 42 TI-Nspire™ Reference Guide

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42
TI-Nspire™ Reference Guide
LinRegtTest
Catalog >
LinRegtTest
X
,
Y
[
,
Freq
[
,
Hypoth
]]
Computes the linear regression line fit of the data point pairs, where
y(k) = a + b
·
x(k) and tests the null hypotheses H0: b = 0 against one
of the following three alternatives:
Hypoth
> 0 is Ha:
s
1 >
s
2
Hypoth
= 0 is Ha:
s
1
ƒ
s
2 (default)
Hypoth
< 0 is Ha:
s
1 <
s
2
A summary of results is stored in the
stat.results
variable. (See page
76.)
Output variable
Description
stat.RegEqn
regression equation: a + b
·
x
stat.a, stat.b
Regression line fit offset and slope parameter estimates
stat.df
Degrees-of-freedom
stat.s
Fit error standard deviation for y - (a + b
·
x)
stat.t
T-Statistic for slope significance
stat.PVal
Probability the alternate hypothesis is false
stat.r
Linear regression correlation coefficient
stat.r
2
Coefficient of determination
stat.SESlope
SE Slope = s/sqrt(sum(x-bar)
2
)
stat.Resid
residuals of linear fit
@
List()
Catalog >
@
List(
List1
)
⇒
list
Returns a list containing the differences between consecutive
elements in
List1
. Each element of
List1
is subtracted from the next
element of
List1
. The resulting list is always one element shorter than
the original
List1
.
list
4
mat()
Catalog >
list
4
mat(
List
[
,
elementsPerRow
]
)
⇒
matrix
Returns a matrix filled row-by-row with the elements from
List
.
elementsPerRow
, if included, specifies the number of elements per
row. Default is the number of elements in
List
(one row).
If
List
does not fill the resulting matrix, zeros are added.