Problem 3The end-of-month balance values of a mutual savings bank were studied byformulating a time-series regression model. By defining y: the end-of-month t balance (inthousands of dollars); x: the composite bond rates in month t, where t = 1,..., 36, and aqualitative variable to describe seasonal components, the following model was proposed:[1, if month jy₁ = B₁ + B₁x₁ + B₂ t + B3M2 + B4 M3 +...+ B₁3 M₁2 +&, where M₁ = -for j = 2, 3,..., 12 correspond to February, March, ..., December, respectively. January is thebase month. The solution to the model is shown below:Y = 427.1 +1.706 t - 11.98 X+0.71 M_Feb - 1.81 M_Mar-0.87 M_Apr-1.40 M_May - 1.03 M_Jun- 3.21 M_Jul - 4.97 M_Aug - 6.63 M_Sep - 5.59 M_Oct-4.16 M_Nov -0.87 M_DecTermTTermCoefficient-1.03Constant36.82M_Jun11.45M_Jul-3.21-6.08M_Aug-4.970.29M_Sep-6.63-0.72M_Oct-5.59-0.35M_Nov-4.16-0.56M_Dec-0.87tXM_FebM_MarM_AprM_May0, otherwise'Coefficient427.11.706-11.980.71-1.81-0.87-1.40P0.0000.0000.0000.7770.4760.7300.582TP-0.410.686-1.26 0.220-1.930.067-2.560.018-2.150.043-1.60 0.124-0.33 0.746Answer the following questions by using a = 0.10 and p-values. Circle the correct answer whenrequired.a. Use the fitted regression equation given above to predict the of end-of-month balance ofmonth 44 (which is an August) when x = 30.b. Which month has the lowest mean end-of-month balance for a fixed month and compositebond rates? (Circle the closest answer.)i. Marchii. Juneiii. September and/or Octoberiv. January and/or Februaryv. Cannot determine without the data set.

Regression analysis is one of the techniques that is used for predicting a value for one variable…

Problem 3 The end-of-month balance values of a mutual savings bank were studied by formulating a time-series regression model. By defining y: the end-of-month / balance (in thousands of dollars); x,: the composite bond rates in month 1, where t = 1,..., 36, and a qualitative variable to describe seasonal components, the following model was proposed: [1, if month j [0, otherwise' y₁ = Bo + B₁x₁ + B₂t + B3 M₂ + B₁ M3 +...+ B₁3 M12 +&, where M₁ = for j = 2, 3,..., 12 correspond to February, March, ..., December, respectively. January is the base month. The solution to the model is shown below: Y = 427.1 +1.706 t - 11.98 X+0.71 M_Feb - 1.81 M_Mar-0.87 M_Apr-1.40 M_May-1.03 M_Jun -3.21 M_Jul - 4.97 M_Aug-6.63 M_Sep-5.59 M_Oct - 4.16 M_Nov -0.87 M_Dec Term Constant t X M_Feb M_Mar M_Apr M_May Coefficient 427.1 1.706 -11.98 0.71 -1.81 -0.87 -1.40 P 0.000 11.45 0.000 -6.08 0.000 0.29 0.777 -0.72 0.476 -0.35 0.730 -0.56 0.582 T 36.82 Term M_Jun M_Jul M_Aug M_Sep M_Oct M_Nov M_Dec Coefficient -1.03 -3.21 -4.97 -6.63 -5.59 -4.16 -0.87 ii. June iii. September and/or October T -0.41 -1.26 -1.93 -2.56 -2.15 -1.60 -0.33 Answer the following questions by using a = 0.10 and p-values. Circle the correct answer when required. 0.686 0.220 0.067 0.018 0.043 0.124 0.746 a. Use the fitted regression equation given above to predict the of end-of-month balance of month 44 (which is an August) when x = 30. b. Which month has the lowest mean end-of-month balance for a fixed month and composite bond rates? (Circle the closest answer.) i. March iv. January and/or February v. Cannot determine without the data set.

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter1: Functions

Section1.EA: Extended Application Using Extrapolation To Predict Life Expectancy

Problem 5EA