6. Which of the following is an example of a situation in which an independent samples t-test would be appropriate?

a. A comparison of the SAT scores of a group of 10 students who completed a special SAT preparation course compared to the normative performance over the past 2 decades of SAT.

b. A comparison of scores of participants in a memory study in which one group of participants learn the words in alphabetical order, the second group learn the words in order of word length, and the third group do so with the usual way they learn during class.

c. A comparison of participants' scores on a skills test before and after attending a training session for improving the skill.

d. Both a) and b) are viable options.

e. None of the above warrants the use of an independent samples t-test.

7. Two variables are significantly correlated with each other with two-tailed ? =.05. If a simple linear regression is built with one of them being the predictor and the other one being the dependent variable, what would be expected? 1) With two-tailed ? = .05, R-square will be significantly different from zero. 2) With two-tailed ? = .05, the intercept term will be significantly different from zero. 3) With two-tailed ? = .05, the unstandardized regression coefficient of the predictor will not be significantly different from zero.

a. 1) only.

b. 1) and 2) only.

c. 2) and 3) only.

d. All of the above.

e. None of the above

9. When we use a sample to estimate parameters about a population, we often need to make adjustment to the estimation of its ____________, because the sample estimation is often ___________ of the population's parameter.

a. Mean; Underestimation

b. Mean; Overestimation.

c. Variance; Underestimation.

d. Both a) and c) are correct.

e. None of the above.

Which of the following is a correct description on correlation and regression? Select one: a. In regression analysis, we assume both the dependent variable and the independent variables are random in some way. O b. In regression analysis, we assume that changes in the independent variables cause the changes in the dependent variable. O c. In regression analysis, we assume that changes in the dependent variables cause the changes in the independent variable. O d. In regression analysis, we treat the dependant variable and the independant variables in a completely symmetrical way.Which of the following is correct?(There may be one or multiple correct answers within the list below) Suppose that you are interested in estimating the ceteris paribus relationship between y and x]. You have collected data and run the following sample regression function, and obtained estimates of OLS coefficients. Di = Bo + Bixn + 2x12 Later on, you have realized that the true population function is: Vi = Bo + Bixatu Thus, you run the following sample regression function. Di = Bo + Bixn None of the other statements given in this list can be said without further information The coefficient estimate of x1 in multiple regression analysis can be smaller than the coefficient estimate of x1 in simple regression analysis. The coefficient estimate of x1 in multiple regression analysis will be equal to the coefficient estimate of x1 in simple regression analysis. The coefficient estimate of x1 in multiple regression analysis can be greater than the coefficient estimate of x1 in simple regression analysis.-12/02 3. [5 points] Suppose that the number of customers arriving at the Woori Bank follows a Poisson process with a rate of Aw = 0.5 per minute, the number of customers arriving at the Hana Bank follows a Poisson process with a rate of An = 0.2 per minute, and the two Poisson processes are independent. Let( We)be the waiting time to have the kth customer at the Woori Bank and /, be the waiting time to have the kth customer at the Hana Bank. Find the probability distribution of the random variable Wa H 2 and explain why.Recall the Poisson process that we covered during the lecture. We showed that if earthquakes happen following a Poisson process, the time interval between two consecutive earthquakes is exponentially distributed. This also implies that if an earthquake just happened, the time of the next one (1) earthquake follows exponential distribution. Now, we want to generalize this result. Suppose that earthquakes near San Francisco happens following a Poisson process 2 and an earthquake just happened. Find the probability that in the next 5 years, there are less than ten (10) earthquakes