code?

Question 1

emmeans

Answer 1

provides the estimated means for each group

Question 2

lm()

Answer 2

calculates a linear model
DV ~ IV, data =
multiple predictors are separated by +

Question 3

slice()

Answer 3

shows a specified section of the data set
e.g. rows 1 - 6 when specify (1:6)

Question 4

summary()

Answer 4

provides a summary output of the model
provides values for coefficients, r^2, f tests , degrees of freedom

Question 5

round()

Answer 5

rounds values to a specified number of decimal places

Question 6

confint()

Answer 6

provides the confidence intervals of the model

Question 7

z_score()

Answer 7

standardises coefficients

Question 8

tab_model

Answer 8

formatted full results table

Question 9

head

Answer 9

shows the top rows of the dataset

Question 10

t.test()

Answer 10

does a t-test

Question 11

contrasts()

Answer 11

used when dummy coding the data when you have a factor variable in the model
selects the first group as the baseline

Question 12

contr.treatment()

Answer 12

specifies dummy coding

Question 13

in dummy coding in r, what does base =

Answer 13

the level number of the baseline you want (e.g. 2)

Question 14

how do you specify interactions in R

Answer 14

asterisk or :

Question 15

which of these - (*), (:), (+), (+ and *) does not specify full model results?

Answer 15

:

Question 16

scale = F is used to do what?

Answer 16

mean centre
scale(variable name, scale = F)

Question 17

probe_interactions

Answer 17

simple slopes
only works for catetgorical * continuous interactions and continuous * continuous predictors

Question 18

cat_plot()

Answer 18

visualises categorical interactions

Question 19

geom_smooth(method = loeess)

Answer 19

adds a loess line to plot

Question 20

crPlots()

Answer 20

component-residual plots
for multiple predictors
also known as partial residual plots

Question 21

hist()

Answer 21

histograms
plots frequency distribution of residuals

Question 22

residualPlot()

Answer 22

plots residuals vs predicted values

Question 23

rstudent()

Answer 23

studentised residuals excluding case outlier in diagnostics

Question 24

rstandard()

Answer 24

standardised residuals including case outlier in diagnostics

Question 25

hatvalues()

Answer 25

hat values - assesses leverage

Question 26

cooks.distance()

Answer 26

average distance the predicted y vaues will move if a given case is removed

Question 27

influence.measures()

Answer 27

DFFit, DFbeta, DFbetas

Question 28

covratio()

Answer 28

gives covratio values
influence on standard errors

Question 29

vif()

Answer 29

quantifies the extent to which standard errors are increased by predictor correlations
gives VIF value for each predictor

Question 30

anova()

Answer 30

applies the F-test for model comparison
- evaluates statistical significance of improvement in variance explained in an outcome with the addition of further predictors (incremental f test)

Question 31

AIC() / BIC()

Answer 31

compares specific models by comparing values and choose the model with the smaller value

Question 32

contr.sum()

Answer 32

changes constract scheme from default
contr.sum = sum to zero coding

Question 33

plot()

Answer 33

plots the model

Question 34

levels()

Answer 34

provides the levels of the dataset in the order they arise in

Question 35

contrast()

Answer 35

tests the effects you have specified

Question 36

pairs()

Answer 36

pairwise comparisons compares all levels of a given predictor with all levels of the other

Question 37

adjust =

Answer 37

adjusts the p-value and compares the adjusted p value to the original alpha

Question 38

Boot

Answer 38

takes the fitted model,
f = which bootstrap statistics to compute on each bootstrap sample, (default is f = coef returning the regression coefficients) ,
r = how many bootstrap samples to compute,
ncores = to perform calculations in parallel (default ncores = 1)

Question 39

glm()

Answer 39

runs a generalised linear model

Question 40

family =

Answer 40

in glm()
what family of probability distribution you want for DV (what type of variable is it)

Question 41

family = binomial

Answer 41

for a binary variable

Question 42

exp()

Answer 42

exponentiates the coefficients (converts log-odds to odds ratio)

Question 43

test = chisq

Answer 43

in anova, this performs a likelihood ratio test

Question 44

pwr.t.test

Answer 44

t test power calcuation
can be directional (alternative = less or greater)
or two sided (alternative = two.sided)

Question 45

pwr.r.test

Answer 45

correlations

Question 46

pwr.f2.test

Answer 46

for linear models

Question 47

code for coefficients of a model

Answer 47

coefficients(model)
coef(model)
model$coefficients
model$coef

Question 48

How to treat data as categorical data?

Answer 48

factor()

Question 49

data as continuous data?

Answer 49

numeric()

Question 50

plotMod$simplesslopes

Answer 50

provides simples slopes data and johnson-neyman plot

Question 51

plotMod$interactplot

Answer 51

provides a simples slopes plot for cat * cont or cont * cont interactions

Question 52

how to set control group as reference level

Answer 52

data$group <- relevel(data$group, ‘control’)

Question 53

geom_line()

Answer 53

connects points of each bar to the other in the same group (colour) on a cat_plot

Question 54

predict()

Answer 54

can be used to get predicted values of y from a model object

Question 55

how to get residual from a model

Answer 55

model$residuals
resid$model
residuals(model)
predict(model) - model$y

Question 56

group_by()

Answer 56

groups the data into the different groups you want to measure by