Toolkit 2 simple linear regression Flashcards

1
Q

What is the model structure of linear regression?

A

Y=3x

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2
Q

What is a bi variate data set?

A

When there is 1 x and 1 y variable

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3
Q

What is a multi variate data set?

A

1 y variable but multiple x variables

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4
Q

What is Linear Regression?

A

It is the concept of ordinary least squares in the evaluation of b0 and b1
It introduces the idea of samples as estimates of population
Interpretations of the Regression Table and the Analysis of variance table
residuals and concept of model specification
Catch all plots

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5
Q

How is the linear data set collected and shown?

A

Simple scatter plot

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6
Q

What is the correlation coefficient ?

A

the way of quantifying how close the scatter of points was to a linear form. -1

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7
Q

Where b0 and b1 are the best unbiased estimates of the true unknown values Y hat is e best estimate of the true Y. What is the equation?

A

y hat = b0 + b1X

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8
Q

What is the objective you aim to find looking at the linear equation scatter plot?

A

It is to find a line through the scatter that minimises this error for all points

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9
Q

How many lines are there for any given set of points that gives the minimum clue for the error which occurs in a linear regression scatter graph?

A

1

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10
Q

What computer programmes offer statistical software?

A

SPSS and others

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11
Q

What are the coefficients ?

A

These give the coefficients of the equation and can be used to plot the line through the centre of the data

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12
Q

What does the R value =?

A

The correlation coefficient

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13
Q

If the f value is greater than 4 and the associated p value less than 0.05 then what is the interpretation?

A

The model is statistically sound and it explains a significant amount of the variation of Y

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14
Q

Do large r squared and significant t and F value ensure the data is fitted well?

A

No. The results from regression are valid and have meanings only in so far as the assumptions concerning the residuals in the model are satisfied.

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15
Q

Should the residual values show any pattern or not ?

A

No

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16
Q

What is a residual?

A

For every observed value of Y there exists an estimated value of Y1 (yHAT) predicted by the line

17
Q

What is the difference. Eternal the observed and the estimated ?

A

This is the residual

18
Q

What is the catch all plot?

A

It exaggerates any pattern in the residuals. It can also be used in more advanced multiple regression models

19
Q

What will a well specified model show?

A

It will show NO pattern and will give an approximate ball of points on the catch all plot