Midterm_test_Mat_3375

pdf

School

University of Ottawa *

*We aren’t endorsed by this school

Course

3375

Subject

Mathematics

Date

Jan 9, 2024

Type

pdf

Pages

9

Uploaded by ghdjdhd

Report
MAT 3375 Midterm test October 19 2023 Professor M. Alvo Time: 70 minutes Student number : Given name : Family name : This is an open book exam. Calculators are permitted. Answer all questions in the spaces provided. The test is out of 20. Each question is worth 2 points. 1
We are interested in studying the relationship between the degree of brand liking Y and moisture content ( X 1 ) and sweetness ( X 2 ) of a certain product. The data is given below Liking Moisture Sweetness 1 64 4 2 2 73 4 4 3 61 4 2 4 76 4 4 5 72 6 2 6 80 6 4 7 71 6 2 8 83 6 4 9 83 8 2 10 89 8 4 11 86 8 2 12 93 8 4 13 88 10 2 14 95 10 4 15 94 10 2 16 100 10 4 Mean 81.75 7 3 Variance 131.1333 5.333 1.0667 2
a. What can you deduce from the scatter and Box plots below? 3
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help
b. Indicate the usual assumptions made in fitting the following simple linear model Y i = β 0 + β 1 X i 1 + ε i , i = 1 , ..., 16 c. Complete the analysis of variance table below whereby the degree of brand liking Y is regressed against moisture content ( X 1 ) . Response: Liking Sum of Squares df Mean Square F p-value Moisture 1566.45 Residuals 400.55 Total 15 4
d. From the output of the coefficients table below, indicate the estimated regression function for the model in part b. Compute the test statistic to test the null hypothesis H 0 : β 1 = 0 against the alternative H 1 : β 1 ̸ = 0. (Coefficients) Estimate Std. Error t-value p-value Intercept 50 . 775 4 . 395 11 . 554 10 e 08 Moisture 4 . 425 5
e. For the model in part b. compute a 95% confidence interval for the mean of the response when Moisture =5. f. Using the extra sum of squares principle, conduct a formal test to see if the variable sweetness ( X 2 ) should be included in the regression function using level α = 0 . 05. Use the information in the table below. Model1 Liking˜Moisture Model2 Liking˜Moisture+Sweetness Model Residual Sum of Squares Res. d.f. Sum of Squares F p-value 1 400.55 2 94.30 6
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help
g. Estimate β 1 , β 2 jointly by the Bonferroni procedure using 95% confi- dence intervals using the results in the table below when the full model is fitted (Coefficients) Estimate Std. Error t-value p-value Intercept 37 . 6500 2 . 9961 12 . 566 1 . 20 e 08 Moisture 4 . 425 0 . 3011 14 . 695 1 . 78 e 09 Sweetness 4 . 3750 0 . 6733 6 . 498 2 . 01 e 05 h. Express in matrix notation an interval estimate at a 95% confidence of the mean service time for a new observation when x h 1 = 5 and x h 2 = 4 7
i. What do the attached plots reveal about the assumptions of constant variance and normality when the full model is fitted? 8
j) What are the % of variation explained by each of the two regressions fitted? The anova table below is a summary when both Moisture and Sweet- ness are fitted in the regression model. Response: Liking Sum of Squares df Mean Square F p-value Moisture 1566 . 45 Sweetness 306 . 25 Residuals Total 15 9
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help