Some non-linear regressions can also be estimated using a linear regression model (using "linearization'). Assume that the data below show the selling prices y (in dollars) of a certain equipment against its age x (in years). We'd like to fit a non-linear regression in the form y= cd to estimate parameters c and d from the data by linearizing the model through In y= In c+ (In d)x = b0 + b,x. y y 6312 3. 5387 5697 4973 5734 4892 (Click the button to copy or download the data.) !! Using Excel ot other software, the non-linear regression model y = cdX can be estimated as: (Round c and d to four decimal places, inlcuding any zeros.)

A negative correlation between variables X and Y will always result in a positive slope in the linear regression model. Cannot tell from the given information. False True

Finally, the researcher considers using regression analysis to establish a linear relationship between the two variables – hours worked per week and yearly income. (a) Estimate a simple linear regression model and present the estimated linear equation. Display the regression summary table and interpret the intercept and slope coefficient estimates of the linear model. (b) Display and interpret the value of the coefficient of determination, R-squared (R2).   Data Hours Per Week Yearly Income ('000's) Class  18 43.8   13 44.5   18 44.8   25.5 46.0   11.5 41.2   18 43.3   16 43.6   27 46.2   27.5 46.8   30.5 48.2   24.5 49.3   32.5 53.8   25 53.9   23.5 54.2   30.5 50.5   27.5 51.2   28 51.5   26 52.6   25.5 52.8   26.5 52.9   33 49.5   15 49.8   27.5 50.3   36 54.3   27 55.1   34.5 55.3   39 61.7   37 62.3   31.5 63.4   37 63.7   24.5 55.5   28 55.6   19 55.7   38.5 58.2   37.5 58.3   18.5 58.4   32 59.2   35…

Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. HRT Use Breast Cancer Incidence 46.30 103.30 40.60 105.00 39.50 100.00 36.60 93.80 30.00 83.50 n USE SALT (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) ý = 45.5727 + (1.3354 )x (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) 1.3354 cases per 100,000 women (c) What breast cancer incidence…