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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4bThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.
- Based on the data shown below, a statistician calculates a linear model y=2.41x+2.76y=2.41x+2.76. x y 4 12.5 5 14.35 6 16.9 7 20.65 8 22.1 9 23.95 Use the model to estimate the yy-value when x=9x=9y=y=Based on the data shown below, calculate the regression line.Regression Equation: Enter the equation in slope-intercept form (y=mx+b)(y=mx+b) with parameters accurate to three decimal places. x y 2 10.3 3 11.41 4 9.92 5 12.93 6 12.34 7 9.85 8 12.36 9 11.87 10 11.68 11 12.49 12 11.4The prelim grades (x) and midterm grades (y) of a sample of 10 MMW students is modeled by the regression line y = 12.0623 + 0.7771x. Estimate the prelim grade if the midterm grade is 83.
- According to the summary result of linear regression model between A and B obtained from R given below, we can fit a regression line. Assume thatA has any value. If we decrease the value of A by 3, how would Y be affected?a) 58.8945 decreaseb) 58.8945 increasec) 29.8827 increased) 49.5142 decreasee) 29.8827 decreaseRun a regression analysis on the following data set, where yy is the final grade in a math class and xx is the average number of hours the student spent working on math each week. hours/weekx Gradey 5 57 5 49 6 57.4 8 60.2 9 58.6 12 70.8 13 74.2 13 75.2 14 75.6 18 100 State the regression equation y=m⋅x+by=m⋅x+b, with constants accurate to two decimal places. What is the predicted value for the final grade when a student spends an average of 11 hours each week on math?Grade = Round to 1 decimal place.A large national bank charges local companies for using its services. A bank official reported the results of a regression analysis designed to predict the bank's charges (y), measured in dollars per month, for services rendered to local companies. One independent variable used to predict the service charge to a company is the company's sales revenue (x), measured in Data for 21 companies who use the bank's services were used to fit the model E(y) = β0 + β1x. The results of the simple linear regression are provided below. = 2,700 + 20xInterpret the estimate of β0, the y-intercept of the line. For every $1 million increase in sales revenue, we expect a service charge to increase $2,700. All companies will be charged at least $2,700 by the bank. There is no practical interpretation since a sales revenue of $0 is a nonsensical value. About 95% of the observed service charges fall within $2,700 of the least squares line.