How would you write a Log-Linear regression with interaction terms?   Log-Linear- Log(Y) = B0 + B1(x) + u   interaction terms- Y = B0+ B1X1 + B2X2 + B3X1X2 + u   Can you please provide an example if possible and how you would interpret the model.

How should I draw this linear regression graph for this model? Thanks.

Please help me this question.

A new footwear manufacturer is trying to compete with the major brands by investing heavily in Research and Development (R&D). Their goal is to produce a premium lifestyle sneaker that is more comfortable, longer lasting and more stylish than any of their competitors. Below are some data on dollars spent on R&D and new customers acquired for the last 5 quarters. Regression Equation: Y= 0.5 + 1.35X New Customers R&D Dollars Acquired (in millions) (in thousands) 8 12 12 15 13 18 14 20 18 25 Based on looking at the numbers and/or sketching a scatter-plot, what conclusion can we draw about the data? The variables are negatively correlated You cannot tell until we perform some calculations There does not appear to be a strong correlation between the variables The variables are positively correlated

Explain how using multiple linear regression controls for confounding.

Jackson finds that the heavier a person is, the higher his pulse rate tends to be. A linear regression model he built suggests that 20-kilogram differences in weight correspond to differences in pulse rate of 5 beats per minute. Which of the following must be true and explain your answer.  (I) The correlation coefficient between body weight and pulse rate is 1/4. (II) Your pulse rate slows down 3 beats per minute if your weight decreases by 12 kilograms.

In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67  the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?

Give an equation for a linear regression model for these data to predict the weight from the fuel capacity, Identify the vanables in your equation. • Justify how well your linear equation fits the model by discussing a scatter plot of the date and the correlation coetlicient in assessing the fit of your linear model. • Use your model to predict the total weight of a small motorcycle that has a tuel capacity of 6 gallons. and discuss the usefulness of this prediction.