Question

explain the transformation needed to convert the following data to a linear data set.

{(1, 0.98), (2, 1.39), (3, 1.71), (4, 1.98), (5, 2.22), (6, 2.43)}

Answer 1

@skinny23 i know you posted this, but did you ever get the answer?

Answer 2

@nincompoop @Compassionate @shamil98 any of you gents able to help this lady

Answer 3

Hi, jesselaurenx3

You are given x-y values. You want to plug these in on a dot-graph, or scatter plot. After that, look at the results. A linear set is on a one dimensional plain (i.g., a graph.)

After that, you want to draw a line of best fit. This is something you'll have to do on your own, just take your x-y and plug them into a chart.


Regards,



OpenStudy Ambassador, OpenStudy Staff.

Answer 4

Is that the answer? i thought it was asking for how to change the numbers to make it linear? in that case i have no idea how to do that. @Compassionate

Answer 5

Hi,

A linear transformation is a transformation of the form X' = a + bX. If a measurement system approximated an interval scale before the linear transformation, it will approximate it to the same degree after the linear transformation. Other properties of the distribution are similarly unaffected. For example, if a distribution was positively skewed before the transformation, it will be positively skewed after.

The symbols in the transformation equation, X'i = a + bXi, have the following meaning. The raw score is denoted by Xi, the score after the transformation is denoted by X'i, read X prime or X transformed. The "b" is the multiplicative component of the linear transformation, sometimes called the slope, and the "a" is the additive component, sometimes referred to as the intercept. The "a" and "b" of the transformation are set to real values to specify a transformation.

The transformation is performed by first multiplying every score value by the multiplicative component "b" and then adding the additive component "a" to it. For example, the following set of data is linearly transformed with the transformation X'i = 20 + 3*Xi, where a = 20 and b = 3.

Linear Transformation - a=20, b=3

X


X' = a + bX

The score value of 12, for example, is transformed first by multiplication by 3 to get 36 and then this product is added to 20 to get the result of 56.

The effect of the linear transformation on the mean and standard deviation of the scores is of considerable interest. For that reason, both, the additive and multiplicative components, of the transformation will be examined separately for their relative effects.


Source: http://www.psychstat.missouristate.edu/introbook/sbk15.htm