A regression analysis between weight (y in pounds)and height (x in inches) resulted in the following leastsquares line: = 120 + 5x. This implies that if theheight is increased by 1 inch, the weight is expectedto:A. increase by 24 poundsB. increase by 1 poundC. decrease by 1 poundD. increase by 5 poundsIf all the points in a scatter diagram lie on the leastsquares regression line, then the coefficient ofcorrelation must be:A. -1B. 1C. either 1 or -1D. 0

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A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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A researcher wishes to examine the relationship between years of schooling completed and the number of pregnancies in young women. Her research discovers a linear relationship, and the least squares line is: ŷ = 1 – 2xwhere x is the number of years of schooling completed and y is the number of pregnancies. The slope of the regression line can be interpreted in the following way: O When amount of schooling increases by one year, the number of pregnancies increases by 2. O When amount of schooling increases by one year, the number of pregnancies decreases by 1. O When amount of schooling increases by one year, the number of pregnancies increases by 1. O When amount of schooling increases by one year, the number of pregnancies decreases by 2. Check Answer
We use the form ŷ = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in a state. A Minitab printout provides the following information. Predictor Coef SE Coef T P Constant 315.27 28.31 11.24 0.002 Elevation -31.812 3.511 -8.79 0.003 S = 11.8603 R-Sq = 96.8% Notice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation ŷ = a + bx. (a) Use the printout to write the least-squares equation. ŷ = + x (b) For each 1000-foot increase in elevation,…
We use the form ŷ = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in a state. A Minitab printout provides the following information. Predictor Coef SE Coef T P Constant 316.62 28.31 11.24 0.002 Elevation -30.516 3.511 -8.79 0.003 S = 11.8603 R-Sq = 96.2% The printout gives the value of the coefficient of determination r2. What is the value of r? Be sure to give the correct sign for r based on the sign of b. (Round your answer to four decimal places.) What percentage of the variation in y can be explained by the corresponding variation in x and the least-squares…
Consider the following pairs of measurements: a. Use Stata to chart a scattergram for the data. Show the graph below. X 5 8 3 4 9 Y 6.2 3.4 7.5 8.1 3.2 b. Use Stata to estimate the least squares model between X and Y. Copy the output below and specify the least squares equation. c. What are SSE, s2, and s?
A researcher want to know if there is a relationship between sleep and anxiety. The researcher randomly selected n = 5 participants and asked each to rate their sleep quality and anxiety level on a scale of 1 to 5. The data are below: Sleep X Anxiety Y 2 2 3 3 2 2 4 2 5 2 Calculate the Sum of Squares for X (SSX)
Given the following summary information, calculate the value of the y-intercept for the least squares line. Enter your answer to two decimal places. Hint: You will need to find the slope estimate first. = 12; Σ x = 27; %3D Σ y = 36.8; %3D Σ x2 = 74; Σ ху 3D 96
If the coefficient of determination is .25 and the sum of squares residual is 180, then what is the value of SSY?
Show necessary calculation/justification for each answer. a) In regression analysis, the residuals represent the:a. difference between the actual y values and their predicted values.b. difference between the actual x values and their predicted values.c. square root of the slope of the regression line.d. change in y per unit change in x. b) The least squares method for determining the best fit minimizes:a. total variation in the dependent variableb. sum of squares for errorc. sum of squares for regressiond. All of these choices are true. c) The regression line has been fitted to the data points (4, 8), (2, 5), and (1, 2).The sum of the squared residuals will be:a. 7b. 15c. 8d. 22

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