Chemistry-
General
Easy
Question
Which of the following is not a disporportionation reaction?
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)
![](data:image/png;base64,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)
- All
The correct answer is: ![](data:image/png;base64,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)
Related Questions to study
maths-
If f(
) =
, then
=
If f(
) =
, then
=
maths-General
maths-
If A =
and A–1 =
then value of a and c is -
If A =
and A–1 =
then value of a and c is -
maths-General
maths-
A skew symmetric matrix S satisfies the relation S2 + I = 0, where I is a unit matrix, then SS´ is -
A skew symmetric matrix S satisfies the relation S2 + I = 0, where I is a unit matrix, then SS´ is -
maths-General
maths-
If the matrix
is orthogonal, then
If the matrix
is orthogonal, then
maths-General
maths-
If
and
are two matrices such that the product AB is the null matrix then
is equal to
If
and
are two matrices such that the product AB is the null matrix then
is equal to
maths-General
maths-
If the lines of regression are 3x+12y=19 and 3y+9x=46 then
will be
If the lines of regression are 3x+12y=19 and 3y+9x=46 then
will be
maths-General
maths-
If regression coefficient of y on x is
and that of x on y is
and the acute angle between the two lines is
, then the value of
is
If regression coefficient of y on x is
and that of x on y is
and the acute angle between the two lines is
, then the value of
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maths-General
maths-
The coefficient of correlation for the following data will be
![](data:image/png;base64,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)
The coefficient of correlation for the following data will be
![](data:image/png;base64,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)
maths-General
maths-
For the following data
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/503587/1/975057/Picture22.png)
the value of r will be
For the following data
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/503587/1/975057/Picture22.png)
the value of r will be
maths-General
maths-
The coefficient of correlation for the following will be approximately
![](data:image/png;base64,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)
The coefficient of correlation for the following will be approximately
![](data:image/png;base64,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)
maths-General
maths-
If correlation between x and y is r, then between y and x correlation will be
If correlation between x and y is r, then between y and x correlation will be
maths-General
maths-
If r=-0.97, then
If r=-0.97, then
maths-General
maths-
If r=0.2, then linear correlation will be necessarily
If r=0.2, then linear correlation will be necessarily
maths-General
maths-
Coefficient of correlation between x and y for the following data will be approximately
![](data:image/png;base64,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)
Coefficient of correlation between x and y for the following data will be approximately
![](data:image/png;base64,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)
maths-General
maths-
Coefficient of correlation from the following data will be
![](data:image/png;base64,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)
Coefficient of correlation from the following data will be
![](data:image/png;base64,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)
maths-General