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- On the second sheet is data which shows the rate of growth of a particular patch of bamboo vs daily high temperature.(a) Construct a scatterplot, including the equation of the line of best fit and value of R2.(b) What would the predicted growth rate be for a day with a temperature of 84◦?(c) Is there evidence, at α = 0.01, to support a claim that there is a linear relationship between temperature and growth rate? Please state clearly the null hypothesis, the alternative hypothesis, and what decision you make.A college bookstore must order books two months before each semester starts. They believe that the number of books that will ultimately be sold for any particular course is related to the number of students registered for the course when the books are ordered. They would like to develop a linear regression equation to help plan how many books to order. From past records, the bookstore obtains the number of students registered, X, and the number of books actually sold for a course, Y, for 12 different semesters. These data are below. Show the complete table for your solutions. At a .01 level of significance is there sufficient evidence to conclude that the number of books sold is related to the number of registered students in a straight-line manner? Calculate df:Indicate whether it is a one-tail or two-tail test:Use the table for PEARSON'S CORRELATION COEFFICIENT r (Critical Values):Make a Decision by comparing critical r with the computer Pearson Correlation Coefficient:The equation of the regression line is Y^=50000+(−500)X. Where is the observed value in relation to the regression line? A financial education time of 29 hours is found to result in a amount of debt of 35500 dollars. What is the predicted amount of debt? Where is the observed value in relation to the regression line? A financial education time of 31 hours is found to result in a amount of debt of 36551 dollars. What is the predicted amount of debt? Where is the observed value in relation to the regression line? A financial education time of 22 hours is found to result in a amount of debt of 38591 dollars. What is the predicted amount of debt? Where is the observed value in relation to the regression line? dollars dollars
- A value of r close to _______ suggests at most a weak linear relationship between the variables.Obtain the general solution VARIATION OF PARAMETERSIn a study of the lung function of children, the volume of air exhaled under force in one second is called FEV1. (FEV1 stands for forced expiratory volume in one second.) Measurements were made on a group of children each year for two years. A linear model was fit to predict this year’s FEV1 as a function of last year’s FEV1 (in liters), the child’s gender (0 = Male, 1 = Female), the child’s height (in m), and the ambient atmospheric pressure (in mm). To try to improve the prediction of FEV1, additional independent variables are included in the model. These new variables are Weight (in kg), the product (interaction) of Height and Weight, and the ambient temperature (in °C). The following MINITAB output presents results of fitting the model FEV1 = β0 + β1 Last FEV1 + β2 Gender + β3 Height + β4 Weight + β5 Height · Weight + β6 Temperature + β7 Pressure + ε a) The F statistic is? b) How many degrees of freedom does the F statistic have? c) Find the P-value for the F statistic. Is the…
- A surgery intern has conducted a study of the sleeping habits of her colleagues and has developed a following regression equation: Y = 6 + 0.1X, where X is the number of hours working on one shift, and Y is the number of hours sleeping at night after that shift. Each work shift analyzed was between 8 and 24 hours long. All the following statements about the regression equation are true EXCEPT The minimum amount of sleep an intern would get is 6 hours For each hour working on a shift, interns slept an additional 0.1 hours The maximum amount of sleep an intern would get is 8.4 hours The minimum amount of sleep an intern would get is 6.8 hoursA researcher found a linear correlation between course grades and the average number of hours spent on a mobile phone each day. The line of best fit has equation y ^ = 3.7 − 0.786 x with correlation coefficient r = − 0.84. a. Does this prove that spending too much time on a phone causes students to get lower grades? b. What does the regression line predict your final grade in a course will be if you spend an average of 150 minutes per day on your phone?The least-squares regression equation is y=728.0x+14,705 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.8165. For every dollar increase in median income, the percent of adults having at least a bachelor's degree is ___%, on average. For a median income of $0, the percent of adults with a bachelor's degree is ____%.
- Four pairs of data yield r= 0.942 and regression equation y=3x.Also, y= 12.75. What is the best predicted value of y for x= 2.9?I need A & also the equation regression line for set of 9 points & the description Thankyou smA study was conducted to determine if a linear relationship exists between Calculus II (Y) grades and Calculus I (X). The researcher found out that the values of the slope and the y-intercept of the regression line is equal to 1.0134 and – 5.239, respectively. What is the predicted Calculus II grade when the Calculus I grade is equal to 85?