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- A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, v. 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. A Click the icon to view the children's data. (a) Find the least-squares regression line treating height as the explanatory variable and head circumference as the response variable. y=x+ (D (Round the slope to three decimal places and round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. First interpret the slope. Select the correct choice below and, if necessary. in the answer box to complete your choice. O A. For every inch increase in height, the head circumference increases by (Round to three decimal places as needed.) in., on average. O B. For a height of 0 inches, the head circumference is predicted to be (Round to three decimal places as…A nonprofit analyst considered two independent variables as a predictor for the dependent variable Commitment, the percent of total expenses that are allocated to charitable services. The independent variables are Revenue, total revenue in billions of dollars, andEfficiency, the percent of private donations remaining after fundraising expenses. The regression analysis resulted in this ANOVA table. Determine whether there is a significant relationship between commitment and the two independent variables at the 0.01 level of significance. Source Degrees of Freedom Sum of Squares Mean Square F p-value Regression 2 3640.0416 1820.02 51.0429 <.0001 Error 87 3102.1301 35.66 Total 89 6742.1717 Determine the p-value. The p-value is ________ (Round to three decimal places as needed.)An 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 data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) to (c) below. Click the icon to view the data table. C... (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for female Find the least-squares regression line for males. ŷ=0x+0 (Round the slope to three decimal places and round the constant to the nearest integer as needed.) Data for licensed drivers by age and gender. 21-24 25-34 35-44 45-54 55-64 65-74 > 74 Number of Male Fatal Licensed Age Drivers (000s) < 16 12 16-20 6,424 6,914 18,068 20,406 Number of Number of Female Fatal Crashes Licensed (Males) Drivers (000s) 227 12 6,139 Crashes (Females) 77 2,113 1,534 5,180 5,016 6,816 8,567 17,664 2,780 7,990 20,047 2,742 19,984 14,441 8,386 5,375 19,898 14,328 8,194…Compute the least-squares regression line for predicting the 2012 budget from the 2006 budget. Round the slope and y- intercept to at least four decimal places.Please help it’s not graded
- True or False? The r2 value and a least squares regression line can be an excellent way to demonstrate the degree of correlation between two variables, and the type of association between the two variables. This quation was asked twice in bartley but the answers were different. Clarification is needed. Thank youReport the equation of the regression line and interpret it in the context of the problemTo properly examine the effect of a categorical independent variable in a multiple linear regression model we use an interaction term. True O False
- We have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: . regress sales salary Source Model Residual Total sales salary cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 Number of obs F (1, 207) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE 1.74 0.084 5.72 0.000 = = -.1252934 3757.543 = 209 3.02 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Interpret the…You are studying how a penguin's bill length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. Given the information below, choose the formula for the least squares regression line. b₁ = 87.42 bo = 362.31 x = 43.92 y = 4202.0 O Bill Length = 87.42 Body mass + 362.31 O Bill Length = 87.42*4202.0 + 362.31 O 4202.0 = 362.31*43.92 +87.42 O Body mass = 87.42 * Bill Length + 362.31 O Body mass = 362.31 *Bill Length + 87.42 O Body mass = 362.31 43.92 + 87.42Draw a scatterplot of the right foot temperature (y) versus the left foot temperature (x). Then draw the least-squares regression line on the graph.