Week 8: Linear regression Flashcards

1
Q

What kind of variables are needed for linear regression?

A

2 continuous

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2
Q

What symbol is used for the correlation coefficient?

A

r

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3
Q

What does r vary between?

A

-1 and +1

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4
Q

What two pieces of information do you get from the correlation coefficient?

A

The direction (positive or negative)

The magnitude

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5
Q

What happens in a positive relationship?

A

As one variable goes up, so does the other

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6
Q

What happens in a negative relationship?

A

When one variable goes up, the other decreases

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7
Q

The regression line helps to…

A

Predict other scores

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8
Q

What is the regression equation?

A

= b (X) + a

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9
Q

What is b in the regression equation?

A

The slope - how much change in the outcome for each increase in the predictor

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10
Q

What do you multiply b with in the regression equation?

A

The value of the predictor variable

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11
Q

What is a in the regression equation?

A

The intercept - value of the outcome variable when the predictor is 0

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12
Q

How do you set up the data for a regression analysis in jamovi?

A

1x row per participant
1x column for each variable

all need to be continuous

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13
Q

What is the independent variable called in linear regression?

A

Predictor variable

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14
Q

What is the dependent variable called in linear regression?

A

Response or outcome variable

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15
Q

How do you begin the analysis in jamovi?

A

Regression - linear regression

DV box: put the variable in that you want to predict the outcome of
Covariates: continuous predictors - put predictor in

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16
Q

What fit measures and model coefficients do you ask for in a linear regression?

A

r
R squared
Adjusted r squared

ANOVA
Confidence intervals

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17
Q

What does r give you?

A

The correlation between the IV and DV

18
Q

What does Rsquared tell you?

A

The proportion of the variance for a dependent variable that’s explained by an independent variable

19
Q

Adjusted R squared

A

like R squared but takes into account smaller sample sizes

20
Q

How do you generate a scatter plot in jamovi?

A

Exploration - scatter plot

Predictor in X axis box
Outcome into Y axis

21
Q

Which table gives you the values for the regression equation?

A

The Model coefficients box

Also gives you the ability to give t tests for the slope, and the intercept

22
Q

Which line definitely needs to be significant in the model coefficients box?

A

The slope - has to be different to zero

Both should be but especially the slope

23
Q

Regression becomes more reliable when….

A

The sample size increases

24
Q

Correlation and regression differences?

A

Correlation gives you the strength of the relationship between two continuous variables

Regression builds a model of this relationship to allow the prediction of other scores

25
Q

What does it mean if the slope is not different to 0?

A

The two variables are independent - knowing a score of one does not tell us anything about the other

26
Q

Correlation does not equal…….

A

causation

27
Q

What is extrapolation and how does it relate to regression?

A

the action of estimating or concluding something by assuming that existing trends will continue

You can only use the regression model to make predictions within the data range

28
Q

What kind of equation is used to describe curvy data?

A

Polynomial equations

29
Q

What are the different kinds of polynomials?

A

Quadratic
Cubic
Quartic and so on

30
Q

What is the equation for a quadratic equation?

A

Y = intercept + (slope1x(X)) + (slope2x(Xsquared)

31
Q

Each increase in the polynomial tests indicates..

A

A bend in the data line

32
Q

How do you do a quadratic regression in jamovi?

A

You need to first create another variable - IVsquared

  • compute variable
  • fx: IV*IV

Linear - Linear regression
DV - as normal
Covariates - normal IV and squared IV

In model builder tab - put normal IV into block one and the squared version into block 2

33
Q

Output of a quadratic regression in jamovi?

A

Model fit measures: tells you about the quadratic model in comparison to linear

Model coefficients: can use to make new t-tests as well as the quadratic equation

Model comparisons: tells you how much better the quadratic is - deltaF (df1, df2) = F, p = p (can see if the difference is worthwhile by looking at statistical significance)
Can see increases in Rsquared

34
Q

If a more complex model isnt improving the fit of the data but it looks better, what is it really doing?

A

Fitting the noise instead

35
Q

AIC and BIC?

A

How much information is lost by the model

36
Q

RMSEA?

A

Root mean square error of approximation
How much model misfit
<0.05 is a good fit

37
Q

What 3 coefficients do you have with a quadratic model?

A

Constant
Linear coefficient
Quadratic coefficient

38
Q

What do variables have to be able to do a trend analysis?

A

Quantitative, not qualitative

39
Q

How can we use a one-way ANOVA for trend analysis?

A

If we get a significant ANOVA, can ask for polynomial contrasts in the contrasts menu

The output here will be a list of possible models - the ones that are significant tell us more about the data

40
Q

What is trend analysis?

A

Helps to determine whether a linear or higher-order trend is required to describe the IV - DV relationship

41
Q

The most complex possible trend to describe a line that passes through every group mean is…

A

= (the number of levels of the IV) - 1

42
Q

What if the intercept = 0?

A

The slope is zero and the line is horizontal