The chart below shows the correlation between altitude and pressure. Part a: Make a scatter plot and determine which type of model best fits the data. Part b: Find the regression equation. Part c: Use the equation from Part b to determine y when x = 50.

The chart below shows the correlation between altitude and pressure. Part a: Make a scatter plot and determine which type of model best fits the data. Part b: Find the regression equation. Part c: Use the equation from Part b to determine y when x = 50.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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microorganisms that break down these compounds. BOD is hard to measure accurately. Total organic carbon (TOC) is easy to measure, so it is common to measure TOC and use regression to predict BOD. A typical regression equation for water entering a municipal treatment plant is BOD= -55.42 + 1.507 TOC Both BOD and TOC are measured in milligrams per liter of water. (a) What does the slope of this line say about the relationship between BOD and TOC? O TOC rises (falls) by 1.507 mg/l for every 1 mg/l increase (decrease) in BOD O BOD rises (falls) by 1.507 mg/l for every 1 mg/l increase (decrease) in TOC O TOC rises (falls) by 1.507 mg/l for every 55.42 mg/l increase (decrease) in BOD O BOD rises (falls) by 55.42 mg/l for every 1 mg/l increase (decrease) in TOC (b) What is the predicted BOD when TOC = 0? Values of BOD less than 0 are impossible. Why do you think the prediction gives an impossible value? This arises from extrapolation; the data used to find this regression formula must not…
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. VIF 1 VIF₂ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? = A. No. The VIF for each independent variable is greater than 5. B. Yes. The VIF for each independent variable is less than 5. C. No. The VIF for each independent variable is less than 5. D. Yes. The VIF for each independent variable is greater than 5. Table of Data Standby Total Staff Remote Hours Present Hours 244 338 413 272 357 658 198 339 523 ITI 197 333 382 114 326 347 156 303 351 115 282 381 276 320 159 203 353 276 238 327 473 X
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Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 22 minutes. How does the result compare to the actual tip amount of $5.05? Distance 1.80 8.51 1.40 1.65 12.71 1.02 1.32 Time Fare Tip 0.68 8.00 6.00 25.00 31.00 18.00 11.00 27.00 8.00 16.30 31.75 12.30 9.80 36.80 7.80 7.80 6.30 1.50 2.98 2.46 1.96 0.00 2.34 0.00 1.89 The regression equation is ŷ = + (x. (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.)
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Use the computer output to estimate the interceptPo and the slope B,. The regression equation is Y 797 - 4.73X. %3D Predictor Coef SE Coef T Constant 797.101 86.64 9.20 0.000 -4.731 1.551 -3.05 0.006 Intercept Bo: Slope B: eTextbook and Media Hint Save for Later Type here to search
A realty company would like to develop a regression model to help it set weekly rentai rates for beach properties (y). The independent variables for this model are the number of bedrooms a property has (x). its age (x2), and the number of biocks away from the ocean it is (x). Use the accompanying data to complete parts a through e below. E Click the icon to view the rental property data. a) Using technology, construct a regression model using all three independent variables. -(8992 6) (295.6)x + (41.7) x2 + (-3146.3) x (Round to one decimal place as needed.) b) Test the significance of each independent variable using a= 0.05. Test the significance of x. Identify the null and alternative hypotheses. Ho: , = 0 H: B, 0 (Type integers or decimals.) Calculate the appropriate test statistic. The test statistic is 1.24 (Round to two decimal places as needed.) Determine the appropriate critical value(s) for a0.05. The critical value(s) is(are) (Round to two decimal places as needed. Use a…
The accompanying data are the shoe sizes and heights (in inches) of 14 men. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x=11.5 (b) x=8.0 (c) x=15.5 (d) x= Shoe Size, x Height, y8.5 66.09.0 69.09.0 67.59.5 70.510.0 70.510.0 72.010.5 71.510.5 70.011.0 71.011.0 71.511.0 73.012.0 73.012.0 74.012.5 73.5 The equation of the regression line is y= ____x +____
A study of the amount of rainfall and the quantity of air pollution removed produced the accompanying data. Complete parts (a) and (b) below. Click the icon to view the daily rainfall and air pollution data. (a) Find the equation of the regression line to predict the particulate removed from the amount of daily rainfall. The equation is y=+x. (Round to two decimal places as needed.) t more help. Daily Rainfall and Air Pollution Data Daily Rainfall, x (0.01 cm) 4.3 4.5 5.9 5.6 6.1 5.3 3.8 2.1 7.5 Particulate Removed, y (µg/m³) 127 121 117 Print 119 114 119 133 142 107 Done X
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A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-1.223 b=22.907 r2=0.600625 r=-0.775 Use this to predict the number of sit ups a person who watches 5 hours of TV can do, and please round your answer to a whole number.
Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 7.