Consider the following population regression function: In(price)=Bo+B₂ln(dist)+u, where price represents housing price and dist represents distance from a recently built garbage incinerator. Data from 1988 for houses sold in Andover, Massachusetts are used to estimate the model. The intercept, Bo, is estimated to equal 9.40, and the slope parameter, B₁, is estimated to equal 0.312. Does simple regression provide an unbiased estimator of the ceteris paribus elasticity of price with respect to dist? Explain.

A. Write the equation of the regression line. B. Interpret each one of the slopes in this context. C. Calculate the estimated number of days absent and the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school.

A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled, “The relation of dive duration (DD) to depth (D).” Duration DD is measured in minutes, and depth D is in meters. The report then says, “The regression equation for this bird is DD=2.69+0.0138D. (a) What is the slope of the regression line? (Enter your answer rounded to four decimal places.) slope =     (b) On average, if the depth of the dive increases by one meter, what is the increase in the diving duration? (Enter your answer rounded to four decimal places.) increase in duration =   minutes (c) According to the regression line, how long does a typical dive to a depth of 168 meters last? (Enter your answer rounded to three decimal places.) length…

A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x2 suggests this: __________. If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes

Researchers at a large nutrition and weight management company are trying to build a model to predict a person’s body fat percentage from an array of variables. A variables selection method is used to build a regression model. SAS output for the final model is given in photo.  Question: What percentage of the variation in percent body fat remains unexplained, even after introducing weight and abdomen circumference into the model, and then also determine the interpretation of the slope for weight?

State the regression equation and use it to predict taxes for a house with lot size 10K.

Fit a linear regression model for the following data.

Stoaches are fictional creatures that nest in truffula forests. A researcher wants to know whether there is a relationship between a stoach’s wingspan (?W, the predictor) and its nest height (?H, the response). A sample of 88 stoaches is observed, and for each, the wing-span (in cm) and the nest height (in m) are recorded. The observed data meet the assumptions for a linear regression, so the researcher fits the regression model and obtains a regression equation ℎ̂=−0.813+0.177?,h^=−0.813+0.177w, with standard error for the coefficient of ?w equal to 0.448.   Determine the ?p-value from a test for a statistically significant linear dependence of nest height on wing-span. (Give your answer to 4 decimal places.

The flow rate in a device used for air quality measurement depends on the pressure drop x (inches of water) across the device's filter. Suppose that for x values between 5 and 20, these two variables are related according to the simple linear regression model with true regression line y = -0.11 + 0.097x. (a.1) What is the true average flow rate for a pressure drop of 10 in.?(a.2) A drop of 15 in.?(b) What is the true average change in flow rate associated with a 1 inch increase in pressure drop?(c) What is the average change in flow rate when pressure drop decreases by 5 in.?

The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGE; = Bo + B1 EDUC; + e: where WAGE; is the hourly wage of person i (i.e., any specific person) and EDUC; is the number of years of education for that same person. The residual e; encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAGE; = –11.1+6.2 EDUC; If the standard error of the estimate of B, is 1.34, then the true value of B1 lies between and As the number of observations in a data set grows, you would expect this range to in size.