Question

Dr. Krauze wants to see how cell phone use impacts reaction time. To test this, Dr. Krauze conducted a study where participants are randomly assigned to one of two conditions while driving: a cell phone or no cell phone. Participants were then instructed to complete a driving simulator course where reaction times (in milliseconds) were recorded by how quickly they hit the breaks in response to a dog running in the middle of the road during the course. Below are the data. What can Dr. Krauze conclude with an α = 0.01?

cell phone	no cell phone
235 250 239 243 232	232 238 227 228 227

If appropriate, compute the CI. If not appropriate, input "na" for both spaces below.
[  ,  ]

e) Compute the corresponding effect size(s) and indicate magnitude(s).
If not appropriate, input and/or select "na" below.
d =  ;  ---Select--- na trivial effect small effect medium effect large effect
r² =  ;  ---Select--- na trivial effect small effect medium effect large effect

f) Make an interpretation based on the results.

Cell phone use results in significantly slower reaction time than no cell phone use.Cell phone use results in significantly faster reaction time than no cell phone use.    There is no significant reaction time difference between cell phone use or no cell phone use.

Answer 1

we use minitab to solve this question

Ho:There is no significant reaction time difference between cell phone use or no cell phone use.

against

Ha:There is significant reaction time difference between cell phone use or no cell phone use.

solution:

open minitab enter data of cell phone and no cell phone in one column in second column code respective groups as

with cell phone=1 and no cell phone =2

go to top ribbon choose stat => one way ANOVA => responce as 1st column and factor as second column then ok

OUTPUTE:

Here p value=0.038 > =0.01

Then we fail to recect Ho and conclude that

There is no significant reaction time difference between cell phone use or no cell phone use.

Effect size=

=

=0.4340

Using , 43% of the total variance is accounted for by the treatment effect.

Effect size is large.