Match the regression equation with the appropriate graph. (Note that the x- and y- axes are broken.) -1.72x+84.99
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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.Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
- The following table provides values of the function f(x,y). However, because of potential; errors in measurement, the functional values may be slightly inaccurately. Using the statistical package included with a graphical calculator or spreadsheet and critical thinking skills, find the function f(x,y)=a+bx+cy that best estimate the table where a, b and c are integers. Hint: Do a linear regression on each column with the value of y fixed and then use these four regression equations to determine the coefficient c. x y 0 1 2 3 0 4.02 7.04 9.98 13.00 1 6.01 9.06 11.98 14.96 2 7.99 10.95 14.02 17.09 3 9.99 13.01 16.01 19.02You estimated a regression with the following output. Source | SS df MS Number of obs = 327 -------------+---------------------------------- F(1, 325) = 88196.79 Model | 1.7062e+09 1 1.7062e+09 Prob > F = 0.0000 Residual | 6287233.75 325 19345.3346 R-squared = 0.9963 -------------+---------------------------------- Adj R-squared = 0.9963 Total | 1.7125e+09 326 5253017.44 Root MSE = 139.09 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 24.69304 .0831473 296.98 0.000 24.52947 24.85662 _cons | 79.95371 9.884466 8.09 0.000 60.5081 99.39933…A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-0.883 b=30.476 r2=0.660969 r=-0.813 Use this to predict the number of situps a person who watches 8 hour(s) of TV can do, and please round your answer to a whole number.
- You estimated a regression with the following output. Source | SS df MS Number of obs = 157 -------------+---------------------------------- F(1, 155) = 64808.73 Model | 1.0654e+09 1 1.0654e+09 Prob > F = 0.0000 Residual | 2548025.21 155 16438.8724 R-squared = 0.9976 -------------+---------------------------------- Adj R-squared = 0.9976 Total | 1.0679e+09 156 6845708.24 Root MSE = 128.21 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 41.93209 .1647137 254.58 0.000 41.60672 42.25746 _cons | 94.53504 18.26855 5.17 0.000 58.44757 130.6225…You estimated a regression with the following output. Source | SS df MS Number of obs = 273 -------------+---------------------------------- F(1, 271) = 50383.44 Model | 1.8349e+09 1 1.8349e+09 Prob > F = 0.0000 Residual | 9869639.33 271 36419.3333 R-squared = 0.9947 -------------+---------------------------------- Adj R-squared = 0.9946 Total | 1.8448e+09 272 6782356.44 Root MSE = 190.84 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 33.52845 .1493721 224.46 0.000 33.23437 33.82253 _cons | -5.820242 13.28989 -0.44 0.662 -31.9848 20.34432…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=-1.067 b=20.719 r²-0.682276 r=-0.826 Use this to predict the number of situps a person who watches 5 hours of TV can do (to one decimal place)
- Match the regression equation with the appropriate graph. (Note that the x- and y-axes are broken.) ^ y = -0.677x + 52.3 A) û (ວວ 13d snot) ounງ ວ່nsາວ 18+ 40 41 42 43 44 45 46 47 48 49 50, X ork 220 fy 200- Systolic BP (in mm of mercury) 180+ 160+ 140+ 120+ 100+ 80+ 20 Age getin years50 70 X Ô 11 Energy-efficiency_rating 8 18+ 17+ 16+ 5615+ 9 14+ 13+ 12+ 11 5,800 600,0 6200 Cooling capacity (in B70s) X + 32 3Protein in gams) 38 *The estimated regression equation for a model involving two independent variables and 10 observations follows. ŷ = 33.2566 + 0.76251 + 0.2507r2 a. Interpret b1 and b2 in this estimated regression equation (to 4 decimals). %3D y changes by 0.7625 when x1 increases by 1 unit and x2 stays the same v y changes by 0.2507 when x2 Increases by 1 unit and x1 stays the same v b. Estimate y when 1 = 180 and 2 = 310 (to 3 decimals).The estimated regression equation for a model involving two independent variables and 10 observations follows. ŷ = 26.0336 + 0.5179z1 + 0.7599x2 a. Interpret by and by in this estimated regression equation. %3D b1 y changes by 0.5179 when x1 increases by 1 unit and x2 stays the same y changes by 0.7599 when x2 increases by 1 unit and x1 stays the same b. Estimate y when a1 = 180 and æz = 310 (to 3 decimals).
