Define the Extended Least Squares Assumptions for Regressionwith a Single Regressor?

Identify two graphs used in a residual analysis to check the Assumptions 1–3 for regression inferences, and explain the reasoning behind their use.

A study of IT companies has found the following data on the age of each company and its annual volume of sales:               Age (years)      Sales (000)             2                22             2.5             34             3                33             4                37             4.5             40             4.5             45             5                49             3                30             6                58             6.5             58     (a)   Determine the least squares regression that relates the age of company variable to the sales variable in the form y = a + bx.                                                                 (b)   Provide a practical interpretation of the coefficients a and b.   (c)   Determine the ‘goodness of fit’ (R2) of the estimated regression line.   d) Using the estimated regression line determined in (a), calculate what volume of sales would be predicted for a company that is 3.5 years of age.   (e)   If it was found that…

A seafood-sales manager collected data on the maximum daily temperature, T, and the daily revenue from salmon sales, R, using sales receipts for 30 days selected at random. Using the data, the manager conducted a regression analysis and found the least-squares regression line to be Rˆ=126+2.37T. A hypothesis test was conducted to investigate whether there is a linear relationship between maximum daily temperature and the daily revenue from salmon sales. The standard error for the slope of the regression line is SEb1=0.65. Assuming the conditions for inference have been met, which of the following is closest to the value of the test statistic for the hypothesis test?     t=0.274 A t=0.65 B t=1.54 C t=3.65 D t=193.85 E