In the equation of a linear regression, Y = b¡X + bo , if b1 = -4.7 which means %3D %3D
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- In the regression equation Y = 85.65 + 0.70X1 - 3.43X2, the intercept is a. 0.70 b. -3.43 c. 1 d. 85.65in the equation of a linear regression, Y = b,X + bo, if b, = -6.8 which means a. For each unit increase in x, y will decrease by 6.8. b. For each unit increase in x, average of y will increase by 6.8 c. For each unit increase in x, average of y will decrease by 6.8 d. For each unit increase in x, y will increase by 6.8The 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.What's true about the first differences and slope for a linear equation?The table shows the total square footage (in billions) of retailing space at shopping centers and their sales (in billions of dollars) for 10 years. The equation of the regression line is y = 516.388x - 1699.161. Complete parts a and b. Total Square Footage, X Sales, y 4.9 5.1 5.2 5.4 5.6 5.7 ********** (a) Find the coefficient of determination and interpret the result. 5.9 5.8 (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? 5.9 883.3 937.6 986.3 1059.5 1107.2 1200.9 1281.1 1340.8 1438.2 1536. 6.2 OA. The coefficient of determination is the fraction of the variation in sales that can be explained by the variation in total square footage. The remaining fraction of the variation is unexplained and is due to other factors or to sampling error. OB. The coefficient of determination is the fraction of the variation in sales that is unexplained and is due to other factors or sampling error. The remaining fraction of the variation is…
- A particular article proposed a quadratic regression model to describe the relationship between x = degree of delignification during the processing of wood pulp for paper and y = total chlorine content. Suppose that the actual model is the following. y=245+ 75x-4x² + e (a) Mean chlorine content for x = 6 is ---Select--- mean chlorine content for x = 8. (b) What is the change in mean chlorine content when the degree of delignification increases from 8 to 9? What is the change in mean chlorine content when the degree of delignification increases from 12 to 13?A study investigating the relationship between a country's annual gross domestic product x (in trillions of dollars) and carbon dioxide emissions y(in millions of metric tons) yielded r = 0.87, se = 141.9 , and the regression equation y-hat = 199.5x + 56.0. For each additional trillion dollars in %3D gross domestic product, carbon dioxide emissions increases by about 0.87 million metric tons, on average increases by about 199.5 million metric tons, on average changes by an amount that cannot be determined from the information given O increases by about 141.9 million metric tons, on average increases by about 56.0 million metric tons, on averagePlease solve only part d) and part e)
- A teacher wants to form a linear regression equation to predict a student's Score on the final based on the number of hours they spent studying for it. Final scores are in percents. y=2x + 60 R-Squared = 0.65 %3D What does the slope mean in terms of the situation? O For each additional hour a student studies, their grade on the final increases by 2%. O For each additional 2 hours a student student studies, their score on the final increases by 1%. O Every hour a student studies, increases their score on the final. O Always choose C. O The more a student studies, the better they will do on the final.In 2012, the CDC published data on the average weight of American female children by year. Using ages 1−7 of this data, researchers made a linear regression with age in years as the x variable and weight in pounds as the y variable. The equation of the line of best fit is yˆ=5.78x+17.56. According to the line of best fit, what would you predict the weight would be for a 14-year-old female? Is it reasonable to use this line of best fit to make this prediction?