StatBK re-examination,NEEDS R-STUDIO APPILCATION
This is an indicative answer, incl. the R-code I have used with associated output. An answer does not have to include R-code and output unless explicitly requested.
Task 1
Part 1
Make three figures of EMG response: a histogram, a box plot and a QQ plot. Does it make sense to assume that the EMG response is normally distributed? Include the figures in the answer.
Part 2
First use the complete reflex datasets and then the reflex2 dataset where you removed the observation in row 42 to fit the two-sided ANOVA with interaction. Perform model checks for both models and arguments that, based on model checks, one would prefer not to include observation 42.
Theoretical Quantiles
Part 3
Take as a starting point the two-sided ANOVA without observation 42. Examine with a hypothesis test whether there is
Part 4
Determine an estimate of EMG_response for muscle M3 when in the Treatment group.
summary (m1.2)
Part 5
Determine an estimate of EMG_response for muscle M1 when in the Control group.
It can also be calculated from summary output, as 0.003473 because it is the reference group.
Part 6
What is the estimate for σ2
Assignment 2
We start by loading data.
Part 1
The average time it takes to decide on a verb is a real word and the average time it takes to decide on a noun is a real word is two samples. Are there paired or unpaired samples?
These are unpaired samples.
Part 2
Determine an estimate and a 95% confidence interval for how much longer it is expected to take to decide whether a noun is a correct word relative to a verb. Does the data suggest that Hypothesis 1 is true?
Part 3
Does the length of a word have a significant effect on how long it takes to decide if it is a real word?
Part 4
Create a new linear regression model without length, where hour is explained by written_freq:
Determine an estimate and 95% prediction interval for the expected value of hours for a word where written_freq
is 1.
Part 6
Make a one-sided ANOVA where factor (length) is used to explain written_to_spoken, and perform model checking for it.
Part 7
Use a hypothesis test to analyze whether there is a significant effect of factor (length) on written_to_spoken (you are welcome to use the one-sided ANOVA here, regardless of what you concluded from model control).
Part 8
What is the estimate of the expected ratio of the written to the spoken frequency for words of 5 letters? Also determine a 95% confidence interval for the estimate.
We can get the estimate from summary (m2.3):
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