Obtain regression equation of Y on X and estimate Y when X=55 from the ff.
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- Data was collected for a regression analysis where sleep quality (as a percentage) depends on the amount of caffeine consumed in a day (measured in mg). bo was found to be 92.7, bị was found to be -0.76, and R' was found to be 0.86. Interpret the slope of the line. On average, each one mg increase in caffeine consumed increases a person's sleep quality by 92.7%. On average, when x = 0, a person has a sleep quality of -0.76%. On average, when x = 0, a person has a sleep quality of 92.7%. On average, each one mg increase in caffeine consumed decreases a person's sleep quality by 0.76%. We should not interpret the slope in this problem. We should interpret the slope in this problem, but none of the above are correct.The y-intercept in a linear regression model is always relevant and of interest to investigators for every model constructed. True FalseThe equation used to predict annual cauliflower yield (in pounds per acre) is y =24,231 + 4.417x, - 4.677x,, where x, is the number of acres planted and x, is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x, = 36,100, X2 = 36,500 (b) x, = 37,800, X2 = 38,100 (c) X4 = 38,800, X2 = 39,000 (d) x, = 42,100, X2 = 42,200 %3D %3D (a) The predicted yield is pounds per acre. (Round to one decimal place as needed.)
- Can the regression equation reasonably be used to make predictionsUse the linear regression model yˆ=−24.8x+564.38 to predict the y-value for x=63.The volume (in cubic feet) of a black cherry tree can be modeled by the equation y=−50.8+0.3x1+5.1x2, where x1 is the tree's height (in feet) and x2 is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables.
- The volume (in cubic feet) of a black cherry tree can be modeled by the equation y=−51.9+0.3x1+4.9x2, where x1 is the tree's height (in feet) and x2 is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables.Given that the systolic blood pressure in the right arm is 90 mm Hg, the best systolic blood pressure in the left arm is how many mm Hg?A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds), Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session These variables are described below: Y-BO+B1'X+82'D 83'X'D+E Y-Weight loss (in pounds) X- Length of time in weight-loss program (in months) D-1 if morning session. O if not in terms of the Bs in the model, what is the difference between the weight loss of an individual who has spent 3 months in the program when attending the morning session, and an individual who has spent 2 months in the program when attending the evening session? OB1+83 OB1+82-83 OB1+82+283 O81+82+383
- 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.Select the appropriate interpretation for the slope of the linear regression equation below. Y (Dependent Variable) = Grade Point Average X (Independent Variable) = Average number of hours spent using electronic devices for entertainment purposes yhat = 4 - 0.125*X A. For every 1 hour more spent using electronic devices for entertainment per week then a person's GPA will increase on average by 0.125 points B. For every 1 GPA gained obtained by a student then on average that person will have watched 0.125 hours fewer of entertainment on electronic devices per week C. For every 1 GPA point lost by a student then on average that person will have watched 0.125 hours more of entertainment on electronic devices per week D. For every 1 hour more spent using electronic devices for entertainment per week then a person's GPA will decrease on average by 0.125 pointsA particular article used a multiple regression model to relate y = yield of hops to x₁ = mean temperature (°C) between date of coming into hop and date of picking and x₂ = mean percentage of sunshine during the same period. The model equation proposed is the following. y = 415.116.6x₁4.50x2+e (a) Suppose that this equation does indeed describe the true relationship. What mean yield corresponds to a temperature of 20 and a sunshine percentage of 39? (b) What is the mean yield when the mean temperature and percentage of sunshine are 19.1 and 42, respectively? You may need to use the appropriate table in Appendix A to answer this question.
