Each row represents a case, data from one individual
Total participants that were included: 1,691
sex- Categorical Variable
marital- Categorical Variable
smoke- Categorical Variable
age- Numerical Continuous Variable
gross income- Numerical Discrete
amtWeekends- Numerical Discrete
amtWeekedays- Numerical Discrete
Children are the population of interest between 5 and 15 years old. The sample size is 160.
Since we are unaware whether or not the sample was chosen randomly, the results can’t be genearlized. Casual relationships can’t be established since the sample population is mainly partiipants.
Based on this study, we can’t conclude that dementia is caused by smoking since the sample wasn’t random. As a suggestion, other factors such as alcohol abuse and genetics should’ve been looked at as well.
I don’t think this statement is justified because this would be considered an observational study as opposed to a true experiment since this wasn’t a random experiment. One can say that both are co-related, where lack of sleep can increase the chance of bullying.
This study is an experiment
The group which is instructed to excercise is the treatment group and the control group is the group who is refrained from excercising.
Yes, this study makes use of blocking based on age.
No, this study doesn’t make use of blinding.
Yes, a casual link can be made from this study however, and the results can be generalized since there is a random samplilng and random assignments.
This is a good study however, I would make some suggestions. For one, the study should have both groups examined multiple times throughout the study instead of only twice. Also, measuring compliance would be another suggestion.
scores <- c(57, 66, 69, 71, 72, 73, 74, 77, 78, 78, 79, 79, 81, 81, 82, 83, 83, 88, 89, 94)
boxplot(scores)
summary(scores)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 57.00 72.75 78.50 77.70 82.25 94.00
a.Symmetrical distributed, (box plot 2)
b.Uniformly distributed, (box plot 3)
c.Right Skew distributed, (box plot 1)
This is a Symmetric distribution and would use the mean and standard deviation as indicators.
This is a Right skewed distribution and would use median and IQR as indicators.
This is a Right skewed distribution with a lot of outliers and would use median and IQR as indicators.
No, survival isn’t independent of whether or not the patient got a transplant
The box plots are suggesting that the heart transplant will increase the survival rate.
Control Group:
(30/34 *100) = 88% patients died
Treatment Group:
(45/69 * 100) = 65% patients died
Claim being tested is that a heart transplant will increase longevity for a very sick patient.
The results are suggestive for the transplant program’s effectiveness since the difference between the 100 simulations is centered near 0. The heart transplant improves survival rate.