Question:

Please answer the following question clearly, thank you.

1. Quarterly party bookings at Terrace Events been recorded as follows.

a) Compute the seasonal quarterly indexes given the regression line: y = 3 + 4t YEAR Quarter t Yt y Ratio Quarters

2019 1 4

2 7

3 14

4 8

2020 1 5

2 3

3 10

4 6

b) Use the seasonal quarterly index to de-seasonalize the time series.

YEAR Quarter t Yt SQL Seasonally adjusted time series

2019 1

2

3

4

2020 1

2

3

4

c) Use the regression line: y= 3 + 4t and the seasonal quarterly indexes to forecast Terrace Events' party bookings for all quarters of 2021.

YEAR Quarter t y SQL Forecast

2021 1

2

3

4

SP20 MATH 202 MW 9:15a-10:55a Homework: Recommended Problems for Section 4.1 Score: 0 of 1 pt 1 of 17 (0 complete) 4.1.1 What is the difference between univariate data and bivariate data? Choose the correct answer below. O A. In univariate data, the data are qualitative, In bivariate data, the data are quantitative. O B. In univariate data, there is one mean. In bivariate data, there are two means O C. In univariate data, a single variable is measured on each individual In bivariate data, two variables are measured on each individual. O D. In univariate data, there are only positive values and zeros. In bivariate data, there are positive values, negative values, and zeros. Click to select your answer and then click Check Answer Clear All All parts showing Aariate Normal Distribution: Correlation Correlation of Bivariate Normal Random Variables If X and Y have a bivariate normal distribution with joint probability density function fxr(x, y, Gx, Gy, Hx, Hy, P), the correlation between X and Y is p. (5.23) For Bivariate Normal Random Variables Zero Correlation Implies Independence If X and Y have a bivariate normal distribution with p = 0, then X and Y are independent. (5.24)8. Suppose that X and Y have a bivariate normal distribution with Var[X ] = 1, Var[Y] = 2, E[X] = 3, E[Y] = 7, and pxy = 0.5. Compute the following. Hint: Use Equation 5.22, definition of correlation, and the properties of conditional distributions of normal random variables. This should only take a minute to finish!. . E[X2] . Cov[X, Y] . EY|X] when X = 3 . Var[X|Y] when Y = 1