This output tells us the regression line equation is: margin=-5.2 +8.4home. What was the Nets' average margin of victory for games in 2014 when they were the away tea (i.e. when home=0)? (round to 2 decimal places)
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The regression equation for predicting Nets' margin of victory on the basis of whether they were home tean or away team is given by
margin = -5.219512 + 8.439024 home
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- The applet displays a scatterplot with the following points: (7,10),(27,35),(24,25),(8,12),(10,19),(15,22). Identify the slope of the regression line, intercept of the regression line, and correlation coefficient. Report your answers accurate to within two decimal places.Refer to the data set:(-1, 2), (1, 3), (1, 5), (2, 7), (3, 8), (4, 11)Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 10The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…
- I’m taking a statistics and probability class. Please get this correct because I want to learn. I have gotten wrong answers on here beforeNISH Boom Cardsl - Play boom.combg.com/play/1Dwb@fgH9jdMjFmxn/?Lenchcrld AYANWBBrodXKJA its X + 000000000000000000000 13 boomcardsk Use the following regression equation to predict the height of an 8-year-old American girl. Round all answers to the nearest hundredth (2 places after the decimal) and type it in the space below. y = 2.59x + 29.31 An average 8-year-old American girl is approximately 5 centimeters tall. W y tip Type here 90 C Submit X Give up Skip Q Overview ★ Save Card O P Dec 3 1The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. X 11.3 y 13.3 10.9 9.9 8.4 6.8 3.9 2.8 2.4 2.5 7 6.7 6.4 6.3 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation ŷ = 0.79x + 4.32. Use this equation to answer the following. Answer = 12.829 0.9 5 A) How many murders per 100,000 residents can be expected in a state with 10.1 thousand automatic weapons? Answer = 6.123 x Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 2.7 thousand automatic weapons? x Round to 3 decimal places.
- 16.) Use the following regression equation regarding airline tickets to answer the question. price=49+0.22 x (Distance) Note that Distance is the amount of miles between the departure and arrival cities, and Price is the cost of an airline ticket. Interpret the slope of the regression equation in the context of the data.A researcher that there is a linear association between the level of potassium content (y) in milligrams and the amount of fiber (x) in grams in cereal. The regression line for the data is computed to y=37+28x rate. It was also computed that r=.59 A. What does the value . 59 tell you? B. What does the value 37 tell you? C. What does the value 28 tell you ? 2. For the line in question 1 what percentage of variability in potassium can be explained by variability in fiber?Use the data below for the PHONES questions that follow. Data compiled from the CIA World Factbook, https://www.cia.gov/. The number of cell phones in use in a country can be approximated from the number of main lines in use in that country by using the regression equation yhat=25+3x which had an R² of about 90%. (The numbers here are millions. For example, "1.2" would represent 1.2 million or 1,200,000.) PHONES 1 At the time the data was collected, the USA had about 314 million cell phones in use and about 150 million main lines in use. What was the residual for the USA? Enter a number of millions. For example, if your answer is 2.85 million enter "2.85". Do not enter anything else such as symbols or words.
- The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.We run the following regression using a sample of 148 women living in the western states. The variable wage is the hourly wage ( in USD) and educ is the number of years of education completed. wage = -3.07 + 1.52 educ Which statement best described the estimated slope coefficient? Group of answer choices A 1.52 extra years of education is predicted to increase wage by 1 USD An extra year of education is predicted to increase hourly wage by 1.52 USD A 1 percent increase in education is predicted to increase wage by 1.52 percent A 1.52 percent increase in education is predicted to increase wage by 1The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs = 247733.3 +(43.2000) x Billable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) b. Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? Interpret the fixed term, bo. if appropriate. Choose the correct answer below. OA. The value of by is the predicted overhead costs for 0 billable hours. OB. For each increase of 1 unit in overhead costs, the predicted billable hours are estimated to increase by bo OC. It is not appropriate to interpret by. because its value is the predicted…