The table shows the total square footage (in billions) of retailing space at shopping centers and their sales (in billions of 10 years. The equation of the regression line is y=504.535x-1630.078. Complete parts a and b. 4.9 5.1 5.2 5.3 5.6 5.6 5.8 5.9 6.1 6.2 Total Square Footage, x Sales, y 878.9 939.5 982.2 1055.1 1108.5 1212.8 1297.4 1346.1 1434.5 1546.8 (a) Find the coefficient of determination and interpret the result. (Round to three decimal places as needed.)

A shoe store developed the following estimated regression equation relating sales to inventory investment and advertising expenditures. ý = 23 + 12x, + 6x2 where x, = inventory investment ($1,000s) X2 = advertising expenditures ($1,000s) y = sales ($1,000s). (a) Predict the sales (in dollars) resulting from a $14,000 investment in inventory and an advertising budget of $11,000. (b) Interpret b, and b, in this estimated regression equation. Sales can be expected to increase by $ for every dollar increase in inventory investment when advertising expenditure is held constant. Sales can be expected to increase by $ for every dollar increase in advertising expenditure when inventory investment is held constant.

Interpret an R^2 value of 0.62 for a linear regression model where X is the independent variable and Y is the dependent variable.

The annual profits for a company are given in the following table, where x represents the number of years since 2004, and y represents the profit in thousands of dollars. Write the linear regression equation that represents this set of data, rounding all coefficients to the nearest tenth. Using this equation, find the projected profit (in thousands of dollars) for 2012, rounded to the nearest thousand dollars.   Years since 2004 (x) Profits (y)(in thousands of dollars) 0 110 1 137 2 135 3 141 4 160 5 172

Suppose Isabella has developed the following estimated regression equation for the price of an Edmonton condo, based off square footage size (in square feet). Price = 151000 + 48* (Square Footage) a) If someone wants to purchase an Edmonton condo with a size of 1010 square feet, how much should they expect to pay? Answer: dollars b) If they want to buy a condo that is 43 square feet bigger, then how much more money should they expect to pay? Answer: dollars c) Would the intercept be meaningful if this regression represents the average Edmonton condo? Yes No

For many people, breakfast cereal is an important source of fiber in their diets. Cereals also contain potassium, a mineral shown to be associated with maintaining a healthy blood pressure. An analysis of the amount of fiber (in grams) and the potassium content (in milligrams in serving of 77 breakfast cereals produced the regression model Potassium 38+27Fiber. From this model you can estimate a cereals potassium content from the amount of fiber it contains. In this context, what does it mean to say that a cereal has a negative residual? Choose the porrect answer below. OA. The potassium content is actually higher than the model predicts for a cereal with that much fiber OB. The potassium content is actually lower than the model predicts for a cereal with that much fiber OC. The cereal does not have enough potassium OD. The cereal is an outlier in the model.

The data below is the amount of tar and nicotine in a sample of different brands of 100 mm, non-menthol, filtered cigarettes. a) Using the calculator, find a linear regression model that gives the amount of nicotine, N, as a function of the amount of tar, t. Write the function below. N(t)= b) use this model of nicotine to estimate the amount of nicotine in a cigarette if the amount of tar is listed as 21 mg. c) What is the correlation coefficient, r, for this model? r= Tar 5 16 17 13 13 14 9 15 2 15 13 14 15 16 7 17 Nicotine  0.4 1 1.2 0.8 0.8 1 0.8 1 0.2 1.1 0.8 1 0.9 1.1 0.6 1.3

Analysis of the relationship between the fuel economy (combinedMPG) and displacement (in liters) for 1262 models of cars produces the regression model combinedMPH = 33.472 - 3.317Displacement. Explain what the slope means.

A real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y = sales price (in thousands of dollars) x₁ = total floor area (in square feet) x₂ = number of bedrooms x3 distance to nearest high school (in miles) = The estimated model is as follows. =76+0.098x₁ +16x₂ - 8x3 Answer the questions below for the interpretation of the coefficient of X₂ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each additional bedroom in a house? dollars (b) Is this change an increase or a decrease? O increase O decrease X

The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last 7 years. Construct and interpret a 98% prediction interval for the amount of oil imported by the country and the amount of oil produced by the country is 5,483 thousand barrels per day. The equation of the regression line is y = -1.194x + 16,234.906