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- The scatter plot below shows data for the average cost of a high-end computer (y, in dollars) in the year x years since 2000. The least squares regression line is given by yˆ=−1677+314x. A coordinate plane has a horizontal x-axis labeled Year from 4 to 12 in increments of 2 and a vertical y-axis labeled Cost in dollars from 0 to 2000 in increments of 500. The following points are plotted: left-parenthesis 6 comma 250 right-parenthesis, left-parenthesis 7 comma 550 right-parenthesis, left-parenthesis 9 comma 1000 right-parenthesis, left-parenthesis 10 comma 1300 right-parenthesis, and left-parenthesis 11 comma 2000 right-parenthesis. A line rises from left to right, passing through left-parenthesis 7 comma 550 right-parenthesis and left-parenthesis 10 comma 1500 right-parenthesis. All coordinate are approximate. Interpret the y-intercept of the least squares regression line. Select the correct answer below: The predicted cost of a computer in the year 0 is…The scatter plot below shows data for the average cost of a high-end computer (y, in dollars) in the year x years since 2000. The least squares regression line is given by yˆ=−1677+314x. A coordinate plane has a horizontal x-axis labeled Year from 4 to 12 in increments of 2 and a vertical y-axis labeled Cost in dollars from 0 to 2000 in increments of 500. The following points are plotted: left-parenthesis 6 comma 250 right-parenthesis, left-parenthesis 7 comma 550 right-parenthesis, left-parenthesis 9 comma 1000 right-parenthesis, left-parenthesis 10 comma 1300 right-parenthesis, and left-parenthesis 11 comma 2000 right-parenthesis. A line rises from left to right, passing through left-parenthesis 7 comma 550 right-parenthesis and left-parenthesis 10 comma 1500 right-parenthesis. All coordinate are approximate. Interpret the slope of the least squares regression line. Select the correct answer below: On average, the average cost of a high-end computer is…estion 7 of 15 Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (°F), as the explanatory variable and the number of ice bags sold on as the response variable. The least squares regression (LSR) line for the data is y = -114.05 +2.17x. On one of the observed days, the temperature was 82 °F and 66 bags of ice were sold. Determine the number of bags of ice predicted to be sold by the LSR line, ŷ, when the temperature is 82 °F. Enter your answer as a whole number, rounding if necessary. ice bags Using the predicted value you just found, compute the residual at this temperature. residual = ice bags DOLL
- Suppose there is 1 dependent variable (dissolved oxygen, Y) and 3 independent variables (water temp X1, depth X2, and hardness of water X3). Below is the result of the multiple linear regression. Coefficients Standard Error t Stat P-value Intercept 24.84 4.36 5.69 0.00 Water Temperature (C) -1.17 0.37 -3.20 0.02 Depth (feet) -0.15 0.24 -0.61 0.56 Hardness as mg/L CaCO3 -0.04 0.04 -0.95 0.37 Which of the three independent variable(s) is (are) significant predictor(s) of dissolved oxygen? Use .05 level of significance.Multiple regression analysis was used to study how an individual's income (Y in thousands of dollars) is influenced by age (X1 in years), level of education (X2 ranging from 1 to 5), and the person's gender (X3 where 1 =female and 0=male). The following is a partial result of a computer program that was used on a sample of 30 individuals. Coefficient Standard Error X1 0.63 0.094 X2 0.94 0.190 X3 0.52 0.120 Analysis of Variance Source of Degrees Sum of Mean Variation of Freedom Squares Square F Regression 90 Error 100 Use the above results and write the…I need the answer as soon as possible
- part c pleaseWhat is the maximum and mininumber of the range regarding the explanatory variable for which the regression model can make valid predictions?For10 observations on supply (X) and price (Y) the following data are obtained ΣΧ-130, ΣΧ-2280, ΣΥ-5506 , ΣΧΥ-3467, ΣΥ-220 Obtain regression line Y on X and estimate supply when price is 16.
- Numerical analysis. UrgentFit the function y = A xB by the method of least squaresAre the signs of the coefficients on the variables "Frequency of Flights" and "Destination is a Big City" corresponding to the utility of the "Air" mode meaningful? Utility for Car Utility for Air param. t stat Param. t stat Constant 4.77 6.995 0 fixed Travel Time (mins) -0.03 -16.926 -0.03 -16.926 Travel Cost / Income ($/$) -726.35 -4.28 -726.35 -4.28 Frequency of Flights (per week) 0 fixed 0.12 13.414 Is Destination a Large City 0 fixed 1.5 5.576 Travel Party Size > =2^2 1.75 5.873 0 Fixed