Concept explainers
Predicting Test Scores A professor tells his class that he knows their second exam score without their having to take the test. He tells them that the second exam score can be predicted from the first with this equation:
Predicted second exam score
This tells us that the deterministic part of the regression model that predicts second exam score on the basis of first exam score is a straight line. What factors might contribute to the random component? In other words, why might a student’s score not fall exactly on this line?
Explain the factors that contribute to the student’s score, to not fall exactly on the regression line.
Explanation of Solution
A professor tells his students that he can predict the scores of the students from their first exam scores using the calculated regression line.
There may be chances that the predicted second exam scores do not fall on the regression line due to some random factors.
The amount of time the student spent on the study.
The level of difficulty of the question paper and familiarity with the type of questions
Psychological and physical condition of the candidate (depression, confidence level, sleeplessness, illness etc.) during exam.
Disturbance in the exam hall.
There may be many such factors, which affect the student’s score in the second exam.
Want to see more full solutions like this?
Chapter 14 Solutions
Introductory Statistics
Additional Math Textbook Solutions
Applied Statistics in Business and Economics
STATS:DATA+MODELS-W/DVD
Basic Business Statistics, Student Value Edition
Introductory Statistics
Fundamentals of Statistics (5th Edition)
Intro Stats, Books a la Carte Edition (5th Edition)
- What is extrapolation when using a linear model?arrow_forwardWhat is interpolation when using a linear model?arrow_forwardCellular Phone Subscribers The table shows the numbers of cellular phone subscribers y in millions in the United States from 2008 through 2013. Source: CTIA- The Wireless Association Year200820092010201120122013Number,y270286296316326336 (a) Find the least squares regression line for the data. Let x represent the year, with x=8 corresponding to 2008. (b) Use the linear regression capabilities of a graphing utility to find a linear model for the data. How does this model compare with the model obtained in part a? (c) Use the linear model to create a table of estimated values for y. Compare the estimated values with the actual data.arrow_forward
- Elementary Linear Algebra (MindTap Course List)AlgebraISBN:9781305658004Author:Ron LarsonPublisher:Cengage LearningCalculus For The Life SciencesCalculusISBN:9780321964038Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.Publisher:Pearson Addison Wesley,Algebra & Trigonometry with Analytic GeometryAlgebraISBN:9781133382119Author:SwokowskiPublisher:Cengage
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillFunctions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning