Question 8 Examining the below R output, explain in detail what model was selected for forecasting?. Model: LM / ARIMA (1,1,1) (2,1,0) [7) errors
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![Question 8
Examining the below R output, explain in detail what model was selected for forecasting?.
Model: LM w/ ARIMA (1,1,1) (2,1,0) [7] errors](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fba7fd8aa-fc8c-4deb-b674-0ef5f98cd5a6%2F1d86d7dc-910e-4a19-a4d7-7dc9f7682252%2F59wcxgh_processed.jpeg&w=3840&q=75)
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- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.Repeat Example 5 when microphone A receives the sound 4 seconds before microphone B.
- Question 8 Examining the below R output, explain in detail the model that has been selected for forecasting. Model: LM w/ ARINA (0,1,1) (2,0,0) [7] errorsWhat type of model is presented below – Model 3. What impact do d_y and d_rs have on the dependent variable d_m_p? Model 3: OLS, using observations 1980:2-1999:3 (T = 78) Dependent variable: d_m_p Coefficient Std. Error t-ratio p-value const 0.0113971 0.00130078 8.7617 <0.0001 *** d_y −0.0106911 0.100221 −0.1067 0.9153 d_rs 0.00151462 0.00110613 1.3693 0.1751 d_rl 0.000127744 0.00141954 0.0900 0.9285 d_p −0.354204 0.093535 −3.7869 0.0003 *** Mean dependent var 0.007578 S.D. dependent var 0.004753 Sum squared resid 0.001399 S.E. of regression 0.004378 R-squared 0.195607 Adjusted R-squared 0.151531 F(4, 73) 4.437922 P-value(F) 0.002885 Log-likelihood 315.5363 Akaike criterion −621.0725 Schwarz criterion −609.2890 Hannan-Quinn −616.3554 rho 0.485424 Durbin-Watson 1.00121610) A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.767 b=31.009 r2=0.609961 r=-0.781 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)
- A paper gives data on x = change in Body Mass Index (BMI, in kilograms/meter2) and y = change in a measure of depression for patients suffering from depression who participated in a pulmonary rehabilitation program. The table below contains a subset of the data given in the paper and are approximate values read from a scatterplot in the paper. BMI Change (kg/m²) Depression Score Change S = The accompanying computer output is from Minitab. Depression score change 15- 10- -0.5 S Fitted Line Plot Depression score change = 6.577 +5.440 BMI change 20- 5.30586 Coefficients T 0.0 0.5 -0.5 R-sq 25.96% - 1 Term Coef Constant 6.577 BMI change 5.440 % 0.5 BMI change 1.0 SE Coef 2.28 2.90 9 0 0.1 0.7 0.8 1 1.5 4 T-Value 2.88 1.87 Interpret this estimate. s is the typical amount by which the ---Select--- line. 4 5 Regression Equation Depression score change = 6.577 +5.440 BMI change P-Value 0.0164 0.0906 S 5.30586 25.96% R-Sq R-Sq (adj) 18.56% 8 (b) Give a point estimate of o. (Round your answer to…A WebAssign - SP21 A Section 7.2: Estimating u When a X A 07.06 WebAssign: Section 7.2: Es X + A webassign.net/web/Student/Assignment-Responses/last?dep=23798469 ABP E student.fz.k12.mo.us bookmarks Future Plans Tyler Student M É AP Students - Coll. A Launch Dashboard Canvas Dashboard Calendar 00 Duolingo * MackinVIA ap stats book WA ap stats assignmen. >> (b) Using the given data as representative of the population of prices of all summer sleeping bags, find a 90% confidence interval for the mean price µ of all summer sleeping bags. (Round your answers to two decimal places.) lower limit $4 upper limit $4 Submit Answer 3. [-14 Points] DETAILS BBUNDERSTAT12HS 7.2.016.NVA MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER Do you want to own your own candy store? Wow! With some interest in running your own business and a decent credit rating, you can probably get a bank loan on startup costs for franchises such as Candy Express, The Fudge Company, Karmel Corn, and Rocky Mountain Chocolate…
