Biostats Linear Regression

Question 1

Is the next step up after correlation. It is used when we want to predict the value of a variable based on the
value of another variable

Answer 1

Linear Regression

Question 2

The variable we want to predict is
called the dependent variable (or sometimes called as the ________________)

Answer 2

Outcome variable

Question 3

The variable we are using to predict the other variable’s value is called the independent variable, referred to as

Answer 3

Predictor variable

Question 4

We could use _________________ to understand whether exam
performance can be predicted based on revision time; whether cigarette consumption can be predicted based on smoking duration; and so forth.

Answer 4

linear regression

Question 5

If you have two or more independent
variables, rather than just one, you need to use

Answer 5

Multiple regression.

Question 6

Is closely related to correlation

Answer 6

Linear Regression

Question 7

focuses on using the relationship for prediction

Answer 7

Regression

Question 8

If the relationship is perfect, the prediction is also

Answer 8

perfect

Question 9

The vertical difference between the
data points and the predicted
regression line is known as the

Answer 9

Residuals

Question 10

These values are squared to remove the negative numbers and then summed to give

Answer 10

SSR

Question 11

The vertical difference between the
data points and the mean of the
outcome variable can be calculated.
These values are squared to remove
the negative numbers and then
summed to give the total sum of the
squares

Answer 11

SST (shows how good the mean value is as a model of the outcome scores.)

Question 12

The vertical difference between the
mean of the outcome variable and the
predicted regression line is now
determined. Again these values are
squared to remove the negative
numbers and then summed to give

Answer 12

model sum of squares (SSM) - how better the model is compared to just using the mean of the outcome variable.

Question 13

how better the model is compared to just using the mean of the outcome variable.

Answer 13

SSM

Question 14

Total sum of the squares

Answer 14

SST

Question 15

the larger the SSM the better the model is at predicting the _______ compared to the ________________. If this is accompanied by a _________ the model also has a small error

Answer 15

outcome, mean value alone, small SSR

Question 16

is similar to the coefficient of determination in correlation in that it shows how much of the variation in the outcome variable can be predicted by the predictor variable(s).

Answer 16

R2

Question 17

In regression, the model is assessed by the ___________ based on the improvement in the prediction of the _________ and the __________________. The larger the _______ the better the model.

Answer 17

F statistic, model SSM, residual error SSR, F value