Exercise Consider the model with vaccination dS dt dI dt = = I μN-3s - (μ+p)S BS/- (μ+y)I dV 7I+pS-µV dt where V(t) denotes the number of vaccinated or recovered members at time t and p the vacci- nation rate. = 1. Determine the DFE point. 2. Compute and compare the values of Ro(p) and Ro(0). 3. Do stability analysis for the DFE.
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- Bb UTF Bb UTF Bb Sigi 2 Sta Bb E Rep 9 Nev Bb UT Rep Bb UTF Bb UTF Bb Mic * Win ent.blackboardcdn.com/5df8324563085/7628679?X-Blackboard-Expiration=1645045200000&X-Blackboard-Signature%3D BIU A A- | 三= Fill out the table below and calculate the x statistic and degrees of freedom. Blackjack Preserve Winthrop Woods Glencarin Garden Observed 54 48 24 d.f. = If you conduct this analysis in R, the output tells you the p-value = 0.002. What conclusion can you make? Explain how you reached this conclusion.Find the mean and standard deviation for each uniform continuous model. (Round mean answers to 1 decimal place and standard deviation answers to 4 decimal places)a. U(2,15) Mean= Standard deviation=b. U(110,190) Mean=Standard Deviation=c. U(3,97)Mean= Standard Deviation=The flow rate y (m³/min) in a device used for air-quality measurement depends on the pressure drop x (in. of water) across the device's filter. Suppose that for x values between 5 and 20, the two variables are related according to the simple linear regression model with true regression line y = -0.14 + 0.095x. n USE SALT (a) What is the expected change in flow rate associated with a 1 in. increase in pressure drop? |(m³/min) Explain. We expect the change in flow rate (x) to be the y-intercept of the regression line. We expect the change in flow rate (x) to be the slope of the regression line. We expect the change in flow rate (y) to be the slope of the regression line. We expect the change in flow rate (y) to be the y-intercept of the regression line. (b) What change in flow rate can be expected when pressure drop decreases by 5 in.? | (m³/min) (c) What is the expected flow rate for a pressure drop of 10 in.? A drop of 17 in.? 10 in. drop (m³/min) 17 in. drop | (m3/min) (d) Suppose o =…
- The flow rate y (m/min) in a device used for air-quality measurement depends on the pressure drop x (in. of water) across the device's filter. Suppose that for x values between 5 and 20, the two variables are related according to the simple linear regression model with true regression line y = -0.13 + 0.095x. n USE SALT (a) What is the expected change in flow rate associated with a 1 in. increase in pressure drop? (m3/min) Explain. O we expect the change in flow rate (x) to be the slope of the regression line. O we expect the change in flow rate (x) to be the y-intercept of the regression line. O We expect the change in flow rate (y) to be the slope of the regression line. O We expect the change in flow rate (y) to be the y-intercept of the regression line. (b) What change in flow rate can be expected when pressure drop decreases by 5 in.? (m³/min) (c) What is the expected flow rate for a pressure drop of 10 in.? A drop of 16 in.? 10 in. drop (m³/min) 16 in. drop | (m³/min) (d) Suppose…Heights (om) and weights (kg) are measured for 100 randomly selected adult males, and range from heights of 138 to 188 cm and weights of 41 to 150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield x 167.74 cm, y 81.46 ko, r0.239, Pvalue 0.017, and y - 106 + 1,15x. Find the best predicted value of y (weight) given an adult male who is 153 cm tall. Use a 0.01 significance level. The best predicted value of y for an adult male who is 153 cm tall is kg. (Round to two decimal places as needed.)Heights (cm) and weights (kg) are measured for 100 randomly selected adult males, and range from heights of 132 to 194 cm and weights of 38 to 150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield x 167.75 cm, y=81.58 kg, r0.318, P.value = 0.001, and y-109 1.14x. Find the best predicted value of y (weight) given an adult male who is 184 cm tal, Use a 0.05 significance level. The best predicted value of y for an adult male who is 184 cm tall is kg. (Round to two decimal places as needed.)
- X X zy Section 5.4- QNT/275T: Statistics x + 598163/chapter/5/section/4 for Decision Making home > nce between two population means 4.1: Hypothesis test for the difference between two population means. Jump to level 1 A clinical researcher performs a clinical trial on 14 patients to determine whether a drug treatment has an effect on serum glucose. The sample mean glucose of the patients before and after the treatment are summarized in the following table. The sample standard deviation of the differences was 8. Before treatment What is the test statistic? Ex: 0.123 Check What type of hypothesis test should be performed? Select Select Left-tailed z-test Paired t-test Two-tailed z-test Unpaired t-test Next After treatment 75 Sample mean glucose (mg/dL) What is the number of degrees of freedom? Ex: 25 Does sufficient evidence exist to support the claim that the drug treatment has an effect on serum glucose at the a = 0.05 significance level? Select 81 MESA 81101 2 hp 3 IArm circumferences (cm) and heights (cm) are measured from randomly selected adult females. The 139 pairs of measurements yield x = 31.99 cm, y = 163.33 cm, r= 0.032, P-value = 0.708, and y = 158 + 0.1703x. Find the best predicted value of y (height) given an adult female with an arm circumference of 35.0 cm. Let the predictor variable x be arm circumference and the response variable y be height. Use a 0.05 significance level. %3D ..... The best predicted value is cm. (Round to two decimal places as needed.)M/M/2 Mean Arrival Rate: 20 customers/hour Mean Service Rate: 12 customers/hour Number of Servers: 2 On average, how long does a customer wait in line? 11 minutes 11 minutes, 21 seconds 12 minutes, 15 seconds 12 minutes, 45 seconds
- 1. You will obtain regression estimates predicting Attendance from the number of Flyers posted around town and whether the flyers were posted in Color. Assume standard regression assumptions hold. 1a. Give the regression estimate assuming the model: Attendance = β0 + β1*Flyers + β2*Color. (Round your answers to 4 decimal places.) Attendance = +. * (Flyers) + *(Color) 1b. How many people are expected to attend if the band posted 80 flyers printed in color around town? (Round final answers to 2 decimal places.) Predicted Attendance: 1c. At the 5% level, would you recommend that the band post flyers printed in color when advertising for their show (provided that the increase in cost of printing in color is negligible)? multiple choice 1 Yes, since we reject the relevant H0. Yes, since we do not reject the relevant H0. No, since we reject the relevant H01. (30 pts) We wish to determine a regression equation that relates the length of an infant (in cm) to age (in days), gender and weight at birth (in kg). Below is portion of the regression analysis derived using a software. *Note: under the gender variable: male and female categories are assigned a value of 1 and 0, respectively. Std. Err. 0.0980 Source Coef. Model Residual 316.8866 age weight gender intercept 0.4798 0.4020 1.3113 71.4734 8 1.0454 1.9591 19.53 7.7829 a. What is the sample size for this problem? b. Write the estimated regression equation, interpret each slope coefficients, use the proper unit of measurement. c. Test for significance of the Bage, Bweight, and Bgender at the 99% confidence level. d. From (b) which parameter/s is/are statistically significant. e. Test whether or not there is a significant relationship between the infant's length and the independent variables. Use a .01 level of significance. Use only the critical value approach. f. Provide the Coefficient…Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy".t 半 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651 334 342 355 363 365 372 381 392 400 412 420 Here is regression output from Minitab: Predictor Coef SE Coef P Constant 321.878 2.483 129.64 0.000 absorb 156.711 6.464 24.24 0.000 S = 3.60498 R-Sq = 98.5% R-Są (adj) - 98.3% SOURCE DF MS F P Regression 1 7639.0 7639.0 587.81 0.000 Residual Error 9 117.0 13.0 Total 10 7756.0 (a) Does the simple linear regression model appear to be…