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- Answer the following questions regarding the two variables under consideration in a regression analysis. a. What is the dependent variable called? b. What is the independent variable called? What other name(s) refer(s) to the dependent variable? Select all that apply. outlier explanatory extrapolation least-squares What other name(s) refer(s) to the independent variable? Select all that apply. least-squares predictor extrapolation influential error response predictor influential error response explanatory outlierQUADRATIC REGRESSION 2) Road & Track provides the following sample tire wear and maximum load capacity for automobile tires. (IMG 1) A. Find the coefficient of determination. comment on it B. Find the equation of the regression parabola and calculate the estimated data for each value of the independent variable. C. Determine the residual variance, the standard error of estimate, and the explained variance. comment themIs It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
- In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.In order to be able to statistically determine which levels of the significant main effect(s) differ dec the analysis of variance, which of the following is performed? A)t-test B)Simple linear regression analysis C)Post-hoc analysis D)Analysis of residuesAn engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. Click here to view the weight and gas mileage data. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. Car Weight and MPG (Round the x coefficient to five decimal places as needed. Round the constant to two decimal places as needed.) Weight (pounds), x Miles per Gallon, y 3806 16 3796 15 2669 24 3520 18 3361 21 2911 22 3808 18 2612 24 3375 19 3737 16 3320 19
- The difference between a regression weight and a beta weight is: A regression weight assumes linearity. A beta weight is for the population while a regression weight is for the sample. A regression weight is less biased. A beta weight is a standardized regression weight.How did you get the variance for this problem ?A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data. Complete parts (a) through (g) below. Click the icon to view the children's data. Data table (a) Find the least-squares regression line treating height y =x+ (O (Round the slope to three decimal places and round the d Height (inches), x Head Circumference (inches), y O. 28 17.6 24.5 17.1 25.75 17.1 25.75 17.5 24.25 17.0 27.75 17.7 26.5 17.3 27.25 17.6 26.5 17.3 26.5 17.5 27.75 17.6 Print Done Help me solve this View an example Get more help - Media - Clear all Check answer
- A researcher records data on 7 adult pairs' heights (in inches) to compare the physical characteristics of brothers and sisters. Brother Sister 71 69 68 64 6 65 67 63 70 65 71 62 66 62 Mean 68.4285 64.2857 SD 2.2253 2.4299 r=0.4050 What would the least-squares regression equation be for predicting the brother's height from the sister's? A. brother's height = 0.037+44.58 * sister's height B. brother's height = 44.58 + 0.371* sister's height C. brother's height = 20.71 + 0.029 * sister's height D. brother's height = 3.28- 40.68 * sister's height If the sister's height is the same as the mean (64.2857 inches), what would the brother's predicted height be A. 68.4285 (the same as the mean as well) B. 62.1234 C. 70.8990 D. None of the above. Which of the following would be correct? A. The pair of means, (68.4285, 64.2857), lies on the linear regression line. B. The effectiveness of the linear regression model is about 16%, C. The effectiveness of the linear regression model is 10096. D. Both…Use the following information to answer questions 6 and 7. In a study, nine tires of a particular brand were driven on a track under identical conditions. Each tire was driven a particular controlled distance (measured in thousands of miles) and the tread depth was measured after the drive. Tread depth is measured in "mils." Here, 1 mil is 0.001 inch. The least-squares regression line was computed and added to a scatterplot of these data. On the plot, one data point is marked with an "X." The equation of the least-squares regression line is: Tread depth = 360.64 - 11.39 Miles The data value marked with "X" in the provided scatterplot has Tread Depth (Mils) 60 80 150 0 5 O a negative value for the residual. O a positive value for the residual. O a zero value for the residual. O a zero value for the correlation. 10 15 Miles (x 1000) 20 25 30The durability of the hacksaw is measured in a quality control laboratory. A standard saw needs 2500 cuts. The cutting average of 28 saws selected randomly from a company was found to be 2600 and the variance as 17500. I wonder if the company's production is above the standard? (α = 0.01)