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November 08, 2025, 03:57:47 am

Author Topic: Sentence Templates for the responses tomorrow  (Read 1070 times)  Share 

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Cort

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Sentence Templates for the responses tomorrow
« on: November 03, 2013, 05:21:43 pm »
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Ahai,

I'm wondering if anyone has a set of structured responses/templates they use/have for the Further Exam tomorrow. By "templates" I mean what you'll typically include when one of the quests asks you to compare or describe.

For example, one template is when they ask to describe the slope of the graph. My response is normally like this?:

"For every increase in (variable x), (variable y) is increased by (value of gradient/m)".

Or, similarly, when it asks for the correlation.

(r^2 X100) % of the variation in (variable y) can be explained by the variation in (variable x).

Anyone else have any structured or "go-to" responses they have?

Cheers.
I actually have no idea what I'm saying or talking about.

souka

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Re: Sentence Templates for the responses tomorrow
« Reply #1 on: November 03, 2013, 05:30:29 pm »
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I do have one for matrices.

"Matrix __ gives the total ____ (revenue, amount, neko, dumplings) for the _______Variables______"

But its a given.

"Order of matrix __ is (__x__) and the order of matrix __ is (__x__). Thus ____ is not defined because the number of columns of (First matrix) is not equal to the number of rows in (second matrix)"

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sjayne

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Re: Sentence Templates for the responses tomorrow
« Reply #2 on: November 03, 2013, 08:15:12 pm »
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Just check the 'template' is reasonable in the context of the question
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arthurjl

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Re: Sentence Templates for the responses tomorrow
« Reply #3 on: November 03, 2013, 08:41:41 pm »
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I think what you interpreted for correlation is actually the coefficient of determination

coefficient of determination: "(r^2%) of the variation in (variable y) can be explained by the variation in (variable x)"

correlation coefficient: "As the (variable x) increases (variable y) increases"

You might have to also interpret:

y-intercept: "when the (variable x) is zero the (variable y) has the value (y-intercept)

seasonal indices: "(insert month) was ...% (above/below) the average (monthly/yearly/quarterly turnover)"
« Last Edit: November 03, 2013, 08:56:54 pm by arthurjl »

Yacoubb

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Re: Sentence Templates for the responses tomorrow
« Reply #4 on: November 03, 2013, 08:47:42 pm »
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I do have one for matrices.

"Order of matrix __ is (__x__) and the order of matrix __ is (__x__). Thus ____ is not defined because the number of columns of (First matrix) is not equal to the number of rows in (second matrix)"

You don't actually have to write all of that out.

I'd be succinct and to-the-point and write:

Number of columns of the first matrix (is not equal to) the number of rows of the second matrix.

^^ That would be sufficient for explaining why a product matrix is undefined.

Also, if you were to describe how unique solutions exist, calculate the determinant, and then show det=44(is not equal to 0), and so, unique solutions for the simultaneous equations exist.

Core:

* There is a clear pattern to the non-random scattering of the points. <-- this is what I'd write for a residual analysis of a non-linear relationship scatterplot.

* There is no clear pattern to the random scattering of points <-- residual analysis for a linear relationship.

* Upwards (increasing) trend OR downwards (decreasing) trend. <-- explaining time series trends.

* Seasonal indices:
e.g. ice-cream sales 1.7 in summer
Ice cream sales in summer are 70% above the average seasonal ice cream sales for 2012.

When describing a relationship between two numerical scatterplots in terms of direction, form and strength, be straightforward and just say: strong, positive, linear relationship. Don't waffle on.

When describing which is the best data transformation (for negative relationship)
~ This transformation yields an r value closest to -1.
~ This transformation yields an r2 value closest to 1.

When describing which is the best data transformation (for positive relationship)
~ This transformation yields an r value closest to 1.
~ This transformation yields an r2 value closest to 1.

Your intercept, slope & coefficient of determination written explanations are fine :)