Question 8 Examining the below R output, explain in detail what model was selected for forecasting? Nodel: LM w/ ARIMA (1,1,1) (2,1,0) (7) errors
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- 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.What 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 1.001216A 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…
- 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] errorsA) A multiple regression model was used in production the speed of a car based on several factors known to affect the speed. A graph of the residuals for the predicted values is presented below. i) Discuss the relevance of the graph shown below in relation to the normality of predicted values. 6.00000- 4.00000- 225 2.00000 00000- -2.00000- 424 164 226 227 O163 -4.00000- Standardized Residual (b) The diameter of iron rods issued in a high rising building pillars are under investigation. The diameter for Eleven rods were measured and the following results are obtained: Days 14.5 16.0 15.4 16.3 15.4 15.9 15.5 14.9 15.7 16.0 15.9 i. Determine The Interquartile range of the data ii. Determine a measure to describe the asymmetry of the data set.Which of the following is false concerning the interpretation of a regression equation expressing the relationship between variables X and Y? A) The beta coefficient predicts a per unit increase in Y based on X B) The regression equation is typically adapted to populations beyond the observed range for X C) Regression equations are strongly influenced by the presence of outliersD) Regression equations only describe linear relationships between two variables
- 10) 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)From the instructor: "I sampled 35 of my students from my last three classes, and their grades for the class (A = 4, B = 3, etc.)" A's (4) = 9 students B's (3) = 13 students C's (2) = 7 students D's (1) = 3 students F's (0) = 3 students Question: What kind of grade might a student expect in one of my stats classes? (Answer should include boxplot, potential outliers). Thank you!'1) Interpret the following regression line y = 10.50 – 0.18x 2) Interpret the following coefficient of determination r? = 0.69 3) Interpret the following coefficient of correlation r =-0.83
- An estimator for parameter v is consistent if E(v) is close to v when sample size is increased. Instruction TRUE or FALSE ?Which of the non-parametric test for ordinal data is the best to use in the given scenario? An experiment was conducted to compare the strengths of two types of elastic bandages: one a standard bandage of a specified weight and the other the same standard but treated with a chemical substance. Ten pieces of each were randomly selected from production. Does the treated bandage tend to be stronger than the standard? Table 1. Strength measurements (and their ranks) for 2 types of bandages. STANDARD TREATED 1.21(2) 1.49(15) 1.43(12) 1.37(7.5) 1.35(6) 1.67(20) 1.51(17) 1.50(16) 1.39(9) 1.31(5) 1.17(1) 1.29(3.5) 1.48(14) 1.52(18) 1.42(11) 1.37(7.5) 1.29(3.5) 1.44(13) 1.4(10) 1.53(19) a. Mood median test b. sign test c. Wilcoxon rank-sum test d. Wilcoxon matched-pairs signed-ranks test e. Spearman and Kendall correlation coefficients f. Kruskal-Wallis testIn (WAGE) = B, + B,EDUC + B,EDUC² + B,EXPER+ B,EXPER² + Bo (EDUC × EXPER) + e The above regression is fitted with a range of restrictions. The regression results are given below: Coefficient Estimates and (Standard Errors) Eqn (A) Eqn (C) Eqn (D) Eqn (E) Variable Eqn (B) 0.403 1.483 1.812 2.674 1.256 (0.771) (0.495) (0.494) (0.109) (0.191) EDUC 0.175 0.0657 0.0669 0.0997 (0.091) (0.0692) (0.0696) (0.0117) EDUC -0.0012 0.0012 0.0010 (0.0027) (0.0024) (0.0024) EXPER 0.0496 0.0228 0.0314 0.0222 (0.0172) (0.0091) -0.00038 -0.00032 (0.0104) (0.0090) EXPER -0.00060 -0.00031 (0.00019) (0.00019) (0.00022) (0.00019) EXPER X EDUC -0.001703 (0.000935) SSE 37.326 37.964 40.700 52.171 38.012 What is the value of F-test statistic for the restriction on the coefficients of Equation E that give Equation D? Note: Sample size N is 200 Select one: O a. 3.89 O b. 5.85 O. 1.78 O d. 73.01
