Explain why selecting a regression equation using stepwise regression is considered superior to selecting one using forward selection.

What other methods could one try if a linear regression does not perform well?

Solve the given differential equation by using an appropriate substitution. The DE is of the form  dy dx = f(Ax + By + C). dy dx  = sin(x + y)

Enter the equation of the least‑squares regression line, with the numerical values rounded to three decimal places and ?� as the explanatory variable. (If you are using CrunchIt, adjust the default precision under Preferences as necessary.

What are the different ways to reduce the error in least square regression?

The adjusted R-squared accounts for the amount of variance explained while also adjusting for the number of independent variables in the linear regression equation.   True or False

Explain about the least square regression line?

How could we provide evidence for the positive association between two variables through linear regression model and hypothesis testing? What are the steps?

An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model,   where y = cost of drilling the new well (in $thousands) and x = number of feet drilled to create the well. Using data collected for a sample of  n=83 wells, the following results were obtained:     = 10.5 + 16.20x     Give a practical interpretation of the estimate of the  slope of the least squares line. An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model,   where y =…

In comparing two multiple regression models, one with variables that are a subset of the other, bigger model’s variables, to infer which model is superior, I get confused in looking at R-squared, adjusted R-squared, and F-statistic values. What’s the difference among them and is one of these preferable to the others?  Thanks.