Explaining advantages of latin square design

Question

Question 1) Explain the following;

(i) Multiple correlation coefficient.

(ii) Partial correlation coefficient.

Also give appropriate examples.

Question 2) Describe the following:

(i) Multiple linear regression equation in three variables.

(ii) Standardized Regression Coefficient.

Question 3) Describe the analysis of trend by the method of least squares.

Question 4) What do you understand by two-way classified data? Give suitable examples.

Question 5) Explain a Latin Square Design. Give suitable example. Write down its advantages.

Question 6) Describe a 22 factorial experiment with 3 replications.

Question 7) Write the explanatory note on Discriminant Analysis.

Question 8) Explain in detail principal component method.

Question 9)a) Calculate least square regression equation of X1 on X2 and X3 using the data given below:

X1    13    26    39    52    65
X2    2      4      6      8    10
X3    3       6     9    12    15

b) Given r₁₂ = 0.6, r₁₃ = 0.7 and r₂₃ = 0.8 Determine R_1.23 and r_12.3.

Question 10)a) Calculate seasonal indices for the data given in the following table using the method of simple averages.

Year    Q1     Q2    Q3    Q4
2006    8      12    15      9
2007    10    17    22     11
2008    11    19    21      9
2009    7     12     17      6

b) Describe the method of moving averages in measuring the trend in time-series data.

Question 11)a) Explain the analysis of data obtained form a Randomized Block Design.

b) Describe the following in detail:

(i) Duncan’s Multiple Range Test.

(ii) Tukey’s Test.

(ii) Least Significance Difference Test.

Question 12)a) Stating a model describe the analysis of a split-plot-design.

b) Define a BIBD. Given a suitable example. Also write its ANOVA table.

Question 13)a) Explain Fisher’s descriminant function.

b) Explain the different steps involved in reducing multidimensional data by the method of factor analysis.