regression equation: Y= 11.5355x-58.77 If a new home is to have 3000 sq. ft. (i.e., 30 hundred sq. ft.), predict the price of the home using your regression equation.

The regression model for the number of diseases y = 0.4x2 – 2.5x +7.2 represents the number of flu cases (y) in a town in the thousands in x years after 2010. The model shows a(n) first followed by a(n) in the number of flu cases. The year had the greatest number of cases of the flu.

Use the regression equation to predict the suicide rate for a state with a divorce rate of 6.5. Y = -0.20 + 2.87x

The linear regression equation for the data from the number 2 is y=.27x+5.82.  Describe the slope of the linear best-fit equations.

biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? a) Interpret the slope of the regression line.

The volume (in cubic feet) of a black cherry tree can be modeled by the equation y = - 50.7 + 0.3x, + 5.2x2, where x, 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. X1 = 70, x, = 8.9 The predicted volume is cubic feet. (Round to one decimal place as needed.)

An economist wants to predict the GDP per person measured in 2011 US dollars in Afghanistan based on Austria's GDP per person. She used data from 2000 to 2018. The regression equation was yhat= -5123.932 +0.152x Interpret the slope. For each additional dollar growth in GDP per person in Austria's, Afghanistan GDP per capita decrease on average by 0.152. O For each additional dollar growth in GDP per person in Austria, Afghanistan's GDP per capita increased on average by 0.152 O For each additional dollar growth in GDP per person in Afghanistan, Austria's GDP per capita increased on average by 0.152. For each additional dollar growth in GDP per person in Afghanistan, Austria's GDP per capita decreased on average by 0.152

Height Weight GPA Study TV Computer FamilySz Floors stairs friends female Acctg Mgmt Mktg Fin IntBus Other 67 138 4 6 2 1. 2 26 6. 1 73 175 3.47 5 4 26 1 62 112 4 6 2 1. 4 34 3 1 1. 64.5 122 3.5 3.4 1. 3 47 4 1 1. 69 135 4 4 2 4 13 1. 61 129 3.8 4 2 3 5 17 1. 1. 64 135 3.4 3 2 5 1. 1 1. 68 150 4 10 1 4 2 31 4 1 1 70 130 3.8 4 1 2 4 2 26 8. 1 65 172 3.6 2.5 1.5 0.5 4 2 26 8. 1 74 235 2.6 1. 1. 1. 4 3 52 1. 69 150 3 2 3 3 4 3 26 1. 72 152 2.8 2.5 0.5 3 5 3 30 10 1. 73 155 3 1. 2 4 2 26 1 74 170 3.75 1.5 1.5 2 15 5 1. 68 150 3 2.5 1. 4 3 26 10 1 63 115 2.5 3 2.5 3 2 13 8 1. 1 71 180 2.1 1 3 3 4 13 10 1 72 167 2.7 2. 4 4 2 18 3 72 180 3.8 2 1.5 0.5 1. 300 68.575 152.6 3.341 3.395 1.55 1.75 4.1 2.2 38.25 5.8 s= 4.146257 28.65842 0.584798 2.137257 0.759155 1.409554 0.640723 0.695852 62.7215 2.587419 ------ ---- ----- ----- ----- ----- ---- ----- ----- ----- ----- ------ ----- ---- ---- --

Use the linear regression model yˆ=−24.8x+564.38 to predict the y-value for x=63.

The equation used to predict annual cauliflower yield (in pounds per acre) is y=24,786+4.563x, -4.736x2, where x, is the number of acres planted and x₂ 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, 37,000, x₂=37,400 (b)x₁=38,700, x₂ = 39,000 (c) x₁=39,700, x₂-39,900 (d) x, 43,000, x₂=43,100 (a) The predicted yield is pounds per acre. (Round to one decimal place as needed.) CIT