For Exercise, use the scatter plot to determine if a linear regression model appears to be appropriate.

A doctor wanted to determine whether there is a relation between a male’s age and his HDL (so-called good cholesterol). He randomly selected 9 of his patients and determined their HDL cholesterol. The data is reported in the table below: a) Compute the regression equation for HDL as a function of age. b)Interpret the meaning of the regression parameters.

Use the regression line to make the appropriate prediction. A random sample of records of electricity usage of homes in the month of July gives the amount of electricity used and size (in square feet) of 135 homes. A regression was done to predict the amount of eleçtricity used (in kilowatt-hours) (y) from size (x). The residuals plot indicated that a inear model is appropriate. The model is y = 0.2x+ 1271. How much electricity would you predict would be used in a house that is 2471 square feet? 494.2 kilowatt-hours O 6000.00 kilowatt-hours O 1765.2 kilowatt-hours O 776.8 kilowatt-hours 3742.2 kilowatt-hours

Q: The US Box Office Gross profit was recorded for the first eleven weekends after the release of a major motion picture. For the dataset posted below this question, construct a scatterplot with the weekend as the horizontal axis and US Gross (in millions of dollars) as the vertical axis. Conduct an analysis of the Residuals using the Data Analysis ToolPak Regression tool. Comment on whether the assumptions of linear regression have been met. (Please solve this in Excel) Weekend US Gross (millions $) 1 47.7 2 25 3 11.35 4 7 5 4.68 6 3.02 7 1.34 8 0.72 9 0.49 10 0.31 11 0.2

An online shopping website collected data regarding its operations and obtained the following linear regression model for the estimated revenue in millions, the hat notation Y  , based on the click-through rate in thousands, x.   What is the best interpretation of the value of the estimated slope of 0.2? Select one.       When there are no clicks on the website, the estimated revenue is $0.2 million.   The estimated change in revenue for each additional thousand clicks is $0.2 million.   Every click on the website causes $0.2 million more revenue.   The estimated change in the click-through rate is 0.2 thousand for each for each one million in revenue.

The age and height (in cm) of 400 adult women from Bolivia were measured. A researcher wants to know if age has any effect on height. A linear regression is carried out in Minitab and the following output obtained. Coefficients Term Constant Age (a) Write down the regression model. (b) Interpret the regression coefficient for the fitted model. (c) Use the output from Minitab to explain if the age of a participant affects their height. Percent (d) The normal probability plot of the residuals from this regression model is given below. Do the assumptions of the regression model seem reasonable? Justify your answer. 99.9 8 28 22299229 88 Coef SE Coef 152.94 7.69 0.022 0.231 01 -100 T-Value P-Value VIF 19.90 0.000 0.10 0.924 1.00 -50 Normal Probability Plot (response is Height) 0 Residual 50 ***** 100 150

For a linear regression b1 is the estimated y-intercept, and b0is the estimated slope of the regression line.   True or False

Draw the regression line for the following equation: Y = -3 + (1) X Show your calculation for a low and high value on the predictor value.

This question has do with linear regression.If I consider attendance in a class versus final grade in the class can you explain where there is a positive and when there is a negative relationship between them.

I need help with finding test statistic, thanks