Question 1(2 points) Saved Dr. Friedman is testing for the difference in mean pounds lost among the groups - a new weight loss drug, a placebo and the marketing leading drug. She sets the alpha level...

Question 1(2 points)

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Dr. Friedman is testing for the difference in mean pounds lost among the groups - a new weight loss drug, a placebo and the marketing leading drug. She sets the alpha level for a two-tailed test at .05. She expects the pounds lost in a month in the 3 groups to be 15, 0 and 10 with a standard deviation of 5. She will have equal numbers in each group. To determine the power of her analysis, she still needs to specify :

Question 1 options:

	Probability of a Type I error
	Total sample size
	The variance
	An independent t-test

Question 2(2 points)

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Dr. Krishnan is assessing the impact of gender and severity of disease (rated on a o to 100 scale) on units of insulin prescribed per day. Two linear regression models are computed to predict units of insulin with the following results:

InsulinUnits = 55.5 + .8Gender

InsulinUnits = 68.5 + .4Gender + .05Severity

Do these results show evidence of confounding?

Question 2 options:

	Yes, because the regression coefficient for gender has changed substantially
	No, because gender is still included in the equation
	Yes, because the mean is different between the two equations
	Only if gender is no longer statistically significant

Question 3(2 points)

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Computing a linear multiple regression with anxiety as a dependent variable, we obtain the following results.

Multiple regression analysis results

Independent Variable	Regression Coefficient	T	p-value
Intercept	5.277	32.52	.0001
Age	-.051	-36.38	.0001
Gender	.295	5.48	.0001
Healthy Living Index	.027	6.30	.0001

Which statement about these results is true?

Question 3 options:

	Age has the greatest predictive value, with the largest absolute t-value
	Healthy living has the greatest predictive value, with the largest t-value
	Both a and c
	Gender and healthy living are both more important than age as predictors, because the regression coefficients are positive

Question 4(2 points)

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Dr. Elder finds that women who have regular mammograms and have a specific gene for breast cancer survive significantly longer than women who do not have regular mammograms. For women who donothave the gene, there is no difference in survival rates between the mammogram and no mammogram conditions. This is an example of

Question 4 options:

	Type II error
	multiple logistic regression
	statistical power
	statistical interaction, also known as effect modification

Question 5(2 points)

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Give an example of two variables that would be expected to have a strong,positivecorrelation.

Question 5 options:

Question 6(2 points)

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Give an example of two variables you would expect to have anegativecorrelation.

Question 6 options:

Question 7(2 points)

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I have a sample of 5,000 patients and want to predict whether or not a patient develops Coronary Heart Disease with the following predictors: age, gender, weight and number of cigarettes smoked per day. The best technique to use would be:

Question 7 options:

	Multiple logistic regression
	Simple linear regression
	Multiple linear regression
	A nonparametric test

Question 8(2 points)

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Computing a linear multiple regression with anxiety as a dependent variable, we obtain the following results.

Multiple regression analysis results

Independent Variable	Regression Coefficient	T	p-value
Intercept	5.277	32.52	.0001
Age	-.051	-36.38	.0001
Gender	.295	5.48	.0001
Healthy Lifestyle Index	.027	6.30	.0001

Which of these is a significant predictor of anxiety?

Question 8 options:

	Age
	Gender
	Healthy Living Index
	All of the above

Question 9(2 points)

I am conducting a study of the effectiveness of a new drug vs the current standard of care. I want my probability of a Type II error to be no higher than 10%. Therefore, the lowest power I can accept is: Please give your answer as a number.

Question 9 options:

Question 10(6 points)

The following results are from a logistic regression with diabetes as the dependent variable

Explain the relationship between these variables and diabetes. Specifically state which, if any, of the variables are statistically significant predictors. State the odds ratio for each variable, whether it is significant and which of the three variables is the best predictor of diabetes. How do you know this?

Question 10 options:

Question 11(2 points)

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Non-parametric tests are used instead of parametric tests when

Question 11 options:

	Assumptions about a normal distribution cannot be met
	Sample size is small
	The dependent variable of interest is time to event
	Both A and B