(Solved) I have most of this question done. I would like my work checked for problem 1 parts a - d i. I will upload my work once a tutor responds.

I have most of this question done. I would like my work checked for problem 1 parts a - d i. I am stuck on d ii and e and need some help. I will upload my work once a tutor responds.


1. Use the âLakeErieâ dataset. One column of the dataset gives water levels of Lake Erie for n = 40 consecutive Novembers and another gives the lag 1 values of the series. The lag 1 value is the value from the previous year.


(a)   Do a time series plot of the variable NovLevel. That is, plot the NovLevel in time order. [In Minitab, use Graph > Time Series Plot.] Is there any obvious upward or downward trend to the Lake Erie levels?

(b)   Plot NovLevel against the lag 1 values. Describe the noteworthy features of the plot.

(c)   Determine the partial autocorrelations for the NovLevel series. [In Minitab, use Stat > Time Series > Partial Autocorrelation, enter NovLevel as the variable (or series) and click OK.] What do the results indicate about the autoregression order for a model that describes November water levels of Lake Erie?

(d)   Do a simple regression with NovLevel as the response and lag 1 values of NovLevel as the predictor variable. Use the Storage button to store the residuals. (Note: This is a first-order autoregression model for the series.)

                               i.           Write the estimated regression equation.

                             ii.           Use the regression equation to find the fitted value of the November water level of Lake Erie in the next year after the final year of the data series. [For this question you donât need to use the method at the bottom of Section 14.3.]

(e)   Determine partial autocorrelations for the residuals from the regression that you did in part (d). [In Minitab, use Stat > Time Series > Partial Autocorrelation, enter RESI1 as the variable (or series) and click OK.] What do the results indicate? (Note: The residuals ideally are a random sample, which means that they should have no time series structure to them. That is, ideally, any partial autocorrelations should have values near 0.)

 


Solution details:
STATUS
Answered
QUALITY
Approved
ANSWER RATING

This question was answered on: Mar 27, 2022

PRICE: $18

Solution~000200242968.zip (25.37 KB)

Buy this answer for only: $18

This attachment is locked

We have a ready expert answer for this paper which you can use for in-depth understanding, research editing or paraphrasing. You can buy it or order for a fresh, original and plagiarism-free solution (Deadline assured. Flexible pricing. TurnItIn Report provided)

Pay using PayPal (No PayPal account Required) or your credit card . All your purchases are securely protected by .
SiteLock

About this Question

STATUS

Answered

QUALITY

Approved

DATE ANSWERED

Mar 27, 2022

EXPERT

Tutor

ANSWER RATING

GET INSTANT HELP

We have top-notch tutors who can do your essay/homework for you at a reasonable cost and then you can simply use that essay as a template to build your own arguments.

You can also use these solutions:

  • As a reference for in-depth understanding of the subject.
  • As a source of ideas / reasoning for your own research (if properly referenced)
  • For editing and paraphrasing (check your institution's definition of plagiarism and recommended paraphrase).
This we believe is a better way of understanding a problem and makes use of the efficiency of time of the student.

Order-Now