which of the following is a true statement for a regression analysis?

-regression analysis is applied only to a non-linear model

-regression analysis is applied only to a multiplicative model

-regression analysis is applied only to a linear model

-regression analysis is applied only to an exponential model

an experiment with more than one manipulated factor is called

-simple random design

-non-randomized block design

-factorial design

-none of the above

I able 2. Lillietors tests for normality. Dependent variable is "Change in BMI." Factor variable is Sex. Sex = Female Lilliefors (Kolmogorov-Smirnov) normality test data: Change.in.BMI D= 0.073632, p-value =0.7142 Sex = Male Lilliefors (Kolmogorov-Smirnov) normality test data: Change,in.BMI D = 0.04, p-value = 0.9555 Table 3. Levene's tests for homogeneity of variance. Dependent variable is "Change in BMI." Factor variable is Sex. DF F value PC(>F) group 0. 003 0. 9564 98 Refer to Table 2 and Table 3. What single conclusion would you draw from the information in these 2 tables together? You would reject the null hypotheses. Your data meet both statistical assumptions for a t-test. Your data are not normally distributed. The means of your data are different.2. A Markov chain with state space {1, 2, 3} has transition probability matrix 0.0 0.3 0.1 [0\": 0.3 0.3 0.4 0.4 0.1 0.5 (a) Is this Markov chain irreducible? Is the Markov chain recurrent or transient? Explain your answers. (1)) What is the period of state 1? Hence deduce the period of the remaining states. Does this Markov chain have a limiting distribution? (0) Consider a general three-state Markov chain with transition matrix P11 P12 P13 1? = P21 332-2 1023 P31 P32 P33 Give an example of a specic set of probabilities PM for which the Markov chain is not irreducible (there is no single right answer to this, of course !). Define and explain the relevance each of the following terms in a regression analysis a. Multicollinearity b. Rz c. Rz adjusted d. Predicted Values of the dependent response e. Residuals f. Sums of Squares Total (for regression) g. Sums of Squares Error (or Sums of Squares Residual in regression analysis) h. Multiple regression