EspañolAn electric utility wishes to examine the relationship between temperature and electricity use in its service region during the summer months. The bivariate databelow give the maximum temperature (denoted by x, in degrees Fahrenheit) and the electricity use (denoted by y, in thousands of kilowatt hours) for a randomsample of fifteen summer days. The data are shown in the Figure 1 scatter plot.Temperature, x(in degrees Fahrenheit)Electricity use, y(in thousands ofkilowatt hours)94.7343.882.4302.7400+68.7282.0375-97.9296.8Aa350-98.7388.4325 +71.2233.9300+91.4319.627590.1305.425074.1294.5225-70.7303.394.7339.11007585909582.4315.7Temperature(in degrees Fahrenheit)82.1258.6Figure 194.0362.783.0357.4Send data to calculatorSend data to ExcelThe least-squares regression line for these data has a slope of approximately 2.58.Electricity use(in thousands of kilowatt hours)x XX 94.7339.175808595100Español82.4315.7Temperature(in degrees Fahrenheit)82.1258.694.0Figure 1362.783.0357.4Send data to calculatorSend data to ExcelThe least-squares regression line for these data has a slope of approximately 2.58.Answer the following. Carry your intermediate computations to at least four decimal places, and round your answers as specified below. (If necessary, consult alist of formulas.)Aa(a) What is the value of the y-intercept of the least-squares regression line for these data?Round your answer to at least two decimal places.(b) What is the value of the sample correlation coefficient for these data?Round your answer to at least three decimal places.

In multiple linear regression, there will be more than one independent variable. In simple linear…

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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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An experiment is run. The mass of an object is recorded over time. Time (min) Mass (g) 32 40 36 32 37 32 39 30 50 27 Using your calculator, run a linear regression to determine the equation of the line of best fit. Round to two decimal places, use x for the variable.y=Using your equation, estimate the mass of the object at 41 minutes.grams=
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.3 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.8 175 7.3 9.5 274 7.4 156 9.9 294 9.2 183 256 Click the icon to view the critical values of the Pearson correlation coefficient r. ..... The regression equation is y =+x. (Round to one decimal place as needed.)
Question Help v Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 78 77 O Left Arm 175 169 142 143 A Data Table 143 A Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.) Critical Values of the Pearson Correlation Coefficient r a= 0.01 0.990 a =0.05 NOTE: To test Hg: 4 0.950 b%30 against H,:p#0, reject Ho if the absolute value of r is greater than the critical value in the table 5 0.878 Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as…
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A simple regression model for 10 pair of data resulted in a standard error of 3.95 (i.e., Se = 3.95), and the. The sum of squares of error (SSE) is ______. a. 187.23 b. 171.63 c. 156.03 d. 140.42 e. 124.82
Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-intercept to at least four decimal places. 43 37 15 19 24 37 18 y 72 67 25 29 42 65 32 Send data to Excel Regression line equation: y = 1.7777x + 1.5838
Predict the average lifespan of an animal in years when the incubation/gestation time is 12 months. Animal: Nile crocodile, Aldabra giant tortoise, Lake sturgeon,Galápagos tortoise, Bowhead whale Average Lifespan (in Years):100 152 152 177 211 Incubation/ Gestation (in Months)3 8 0.25 4.7 regression equation and regression line if needed
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Run a regression analysis on the following data set, where y is the final grade in a math class and x is the average number of hours the student spent working on math each week. hours/week Grade y 8. 72.2 8. 58.2 10 60 11 71.4 14 92.6 14 76.6 14 75.6 14 83.6 15 81 20 100 State the regression equation with constants accurate to 2 decimal places. ý = What is the predicted value for the final grade when a student spends an average of 15 hours each week on math? Round to 2 decimal places. Question Help: D Video 1 Video 2 Submit Question
Question. Using the following regression result, make your answers to the questions. Call: Im(formula - wage - educ, data - data) Residuals: 10 Median -5.1579 -2.5066 0.3816 2.4539 4.4737 Min 30 Max Coefficients: Estimate Std. Error t value Pr(>1tl) (Intercept) 9.0526 educ 5.1420 1.761 0.11635 2.1842 0.5994 3.644 0.00655 ** --- Signif. codes: 0 ***** 0.001 *** 0.01 ** 0.05 . 0.1' 1 Residual standard error: 3.305 on 8 degrees of freedom Multiple R-squared: 0.6241, F-statistic: 13.28 on 1 and 8 DF, p-value: 0.006549 Adjusted R-squared: 0.5771 1) Using the estimate and standard error of educ variable, perform the following hypothesis test. Ho:Beduc = 0 H1: Beduc # 0 2) Interpret the above hypothesis test result.
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