2. Expression levels of GeneA and GeneB were measured in 10 cell lines. The researcher would like to know if expression levels of GeneA and GeneB are related. c. Researcher hypotheses that level of GeneB is a linear function of Gene A, test if such a linear relationship is significant or not. Carry out all above tests for a = 0.05. GeneA GeneB CellLine1 7 15.8 CellLine2 7.7 18.6 CellLine3 9.3 20.9 CellLine4 5.5 13.2 CellLine5 8.5 21.1 CellLine6 9.1 21.5 CellLine7 9.4 22.7 CellLine8 4.9 12 CellLine9 8.2 20.2 CellLine10 5.6 14.3

The CDC released the following table of life expectancies for Americans from 1900-2010. Year 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Life Expectancy 47.3 50.0 54.1 59.7 62.9 68.2 69.7 70.8 73.7 75.4 76.8 78.7 It is believed that there is a logarithmic relation between life expectancy and the year. Transform the data correctly to perform a linear least squares regression, then report your model as y=mlnx+by=mln⁡x+b. Estimate the year when life expectancy should be 82.

Ay -5

Need help If my outer regression equation is Weight= -191.891 + 5.06 (Height) How do I find the estimated weight LBS for an individual with height 76 inches?

The equation used to predict annual cauliflower yield​ (in pounds per​ acre) is y=24,596+4.423x1−4.772x2​, where x1 is the number of acres planted an x2 is the number of acres harvested. Use the multiple regression equation to predict the​ y-values for the values of the independent variables.

Suppose that the regression equation that estimates the value of a car (in dollars) based on its age (in years) is given by: Predicted Value = 21375 - 1215 Age. You have an 8-year old car. How much is it worth?

A business statistics professor would like to develop a regression model to predict the exam scores for students based on their current GPAs, the number of hours they studied for the exam, and the number of times they were absent during the semester.  The data for these variables can be found in this file. a) Run the multiple regression in Excel.  Hint: set x1 = GPA, x2 = Hours, X3 = Absences.      b) Find the R2 and explain its meaning.    c) Explain the outcome of the F test.  What does it mean?    d) Explain the outcomes of the t tests for the slope coefficients.   e) Write out the regression equation.   f)  Explain the meanings of the slope coefficients.

You run a linear regression to predict BMI (body mass index) from average minutes of physical activity per day. You calculate the slope of the regression to be -.054. Interpret the slope in terms of the independent and dependent variables.

What are some ideas for practice problems that use the regression equation?

The number of hours 13 students spent studying for an exam and their scores on that test are described by the regression equation ŷ = 7.350x + 34.617.  What is the predicted score if the student spent 4.5 hours studying?