(a) Develop an estimated regression equation relating y to  x1.  (Round your numerical values to one decimal place.) ŷ =  −18.4+2+4.74x2     Predict y if  x1 = 76.  (Round your answer to one decimal place.)   (b) Develop an estimated regression equation relating y to  x2.  (Round your numerical values to one decimal place.) ŷ =        Predict y if  x2 = 16.  (Round your answer to one decimal place.)   (c) Develop an estimated regression equation relating y to  x1 and x2.  (Round your numerical values to one decimal place.) ŷ =        Predict y if  x1 = 76  and  x2 = 16.  (Round your answer to one decimal place.)

You are given the following data, where X1 (final percentage in math class) and X2 (number of absences) are used to predict Y (standardized math test score in third grade): Y X1 X2 345 70 390 80 1 370 75 4 375 92 400 82 350 70 3 310 61 5 420 80 375 88 3 410 72 1 485 99 300 65 7 Determine the following multiple regression values. Report intercept and slopes for regression equation accurate to 3 decimal places: Intercept: a = Partial slope X1: bị = Partial slope X2: bz = Report sum of squares accurate to 3 decimal places: SSreg Test the significance of the overall regression model (report F-ratio accurate to 3 decimal places and P-value accurate to 4 decimal places): F-ratio = P-value = Report the variance of the residuals accurate to 3 decimal places: Sres = Report the results for the hypothesis test for the significance of the partial slope for final percentage in math class (report the test statistic for the regression coefficients accurate to 3 decimal places and P-value accurate to…

Consider the following data for a dependent variable y and two independent variables,  x1  and  x2. x1 x2 y 30 12 94 47 10 108 25 17 112 51 16 178 40 5 94 51 19 175 74 7 170 36 12 117 59 13 142 76 16 211 (a) Develop an estimated regression equation relating y to  x1.  (Round your numerical values to one decimal place.) ŷ =        Predict y if  x1 = 51.  (Round your answer to one decimal place.)   (b) Develop an estimated regression equation relating y to  x2.  (Round your numerical values to one decimal place.) ŷ =        Predict y if  x2 = 19.  (Round your answer to one decimal place.)   (c) Develop an estimated regression equation relating y to  x1 and x2.  (Round your numerical values to one decimal place.) ŷ =        Predict y if  x1 = 51  and  x2 = 19.  (Round your answer to one decimal place.)

Are the years of education of a child dependent on the years of education of their parent? The table shows the number of years of education of parent and the number of years of education of their child.   Years of Education of Parent 13 9 7 12 12 10 11 Years of Education of their Child 13 11 7 16 17 9 17   If there is a significant linear correlation between the variables, determine the regression equation. Are the years of education of a child dependent on the years of education of their parent? The table shows the number of years of education of parent and the number of years of education of their child.   Years of Education of Parent 13 9 7 12 12 10 11 Years of Education of their Child 13 11 7 16 17 9 17   If there is a significant linear correlation between the variables, determine the regression equation. y’ = 1.5x - 3 There is no significant correlation between the variables,…

The following table contains ACT scores and the GPA for eight college students. Estimate the relationship between GPA (yi) and ACT (x;) using OLS regression by hand. Report the intercept and slope estimates: Student 1 2 3 4 5 6 7 8 GPA 2.8 3.4 3.0 3.5 3.6 3.0 2.7 3.7 ŷ₁ =B₁ + B₁x₁ ACT 21 24 26 27 29 25 25 30 y X ŷ₁

The following table shows students’ number of absences, x, and the student’s final grade,, y.# of absences x         6     2   15     9   12    5       8        Final grade  y           82   86   43   74   58   90   78a)  Calculate r, the correlation coefficient    ________________b)   Find the equation of the regression line  __________________________________c)  If a student is absent 4 times,, what grade does your regression line predict?__________________

The following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each day in degrees Celsius. Complete parts a and b below. Temperature (°C) 22 13 22 20 5 Demand 30 35 38 16 13 16 21 O 21 41 31 47 a. Calculate the slope and y-intercept for the linear regression equation for these data. ŷ=+x (Round to two decimal places as needed.) b. Predict the demand for hot dogs on a day with a temperature of 7°C. The predicted demand for hot dogs on a day with a temperature of 7°C is (Round to the nearest integer as needed.) of

The following table shows the approximate amount of trash produced in an industrialized country from 1980 to 2000. Let x represent the year after 1980 (1980 is year 0) and y represent the amount of trash (millions of tons). Year Million Tons 1980 150 1990 202 2000 220 (a) Draw the graph for this data. (b) Find the equation of the regression line for the data. (Round your answers to one decimal place.)y =   x +  (c) Use the equation to predict the amount of trash y that will be produced in 2010 and 2015. (Round your answers to one decimal place.)   2010:    _______million tons 2015:    _______ million tons

A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 81 + 4x. Years of Annual Sales Salesperson Experience ($1,000s) 80 3 97 3 102 4 4 102 103 6. 8 101 10 119 8 10 123 9. 11 127 10 13 136 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did…

The table shows a country's average fuel consumption for "light duty" vehicles (passenger cars and small trucks) for several years. Let x be the number of years since 1985, so that x = 5 stands for 1990 and so forth. Year Fuel Economy (mpg) 1990 16.9 1995 17.7 2000 18.3 (a) Find the linear regression model for these data. What does the slope in the regression model represent? 2005 18.9 (b) Use the linear regression model to predict the country's average fuel economy for light duty vehicles in the year 2035. 2010 19.6 (a) Find the linear regression model for these data. Choose the correct answer below. (Use integers or decimals for any numbers in the equation. Round to the nearest hundredth as needed.)