The least-squares line predicting sales from the number of advertisement is:
sales = 105 + 9.3 * advertisement
sales increase by 105 for each additional advertisement
sales increase by 9.3 for each additional advertisement
sales decrease by 105 for each additional advertisement
sales decrease by 9.3 for each additional advertisement
We have given regression equation is,
sales = 105 + 9.3 * advertisement
slope= 9.3
Therefore,
sales increase by 9.3 for each additional advertisement
The least-squares line predicting sales from the number of advertisement is: sales = 105 + 9.3...
4. The equation of the least squares regression line for predicting number of car accidents from age is: Age Number of Accidents (per 100 drivers) | 16 23.1 17 20.3 18 19.1 19 15.7 20 15.2 21 14.1 22 13.9 23 14.0 24 11.9 Scatterplot of Accidents (per 100 drivers) vs Age Accidents per 100 drivers Accidents - 31.594 -0.708(Age) Accidents - 31.594 +0.708(Age) Accidents - 41.600 - 1.262(Age) Accidents 41.600 + 1.262(Age)
Compute the least-squares regression line for predicting y from x given the following summary statistics, Round the slope and y- intercept to at least four decimal places. I=8.8 5,- 2.2 y = 102 y-101 =-0.82 Send data ol Regression line equation: -
Compute the least-squares regression line for predicting y from x given the following summary statistics. Round final answers to four decimal places, as needed. x = 12.9 x 2.241000 S y 15000 0.60 Download data Regression line equation:
6) Compute the least-squares regression line for predicting y from x given the following summary statistics. Round the slope and y -intercept to at least four decimal places. = x 8.8 = s x 1.2 = y 30.4 = s y 16 = r 0.60 Send data to Excel Regression line equation: = y 7)Compute the least-squares regression equation for the given data set. Use a TI- 84 calculator. Round the slope and y -intercept to at least four decimal...
112 con E. Compute the least squares regression line for predicting y from x given the following summary statistics. Round the slope and y - intercept to at least four decimal places * - 43,000 $-13 y - 103 * = 13,000 0.70 Regression line equation: Submit Assigent 2000 Merwe Education. All Rights Reserved Terms of Use | Privacy ** 80 888 esc # 3 $ 4 % 5 & 7 9 8 6 7 2 O P R т....
3. Using data on the appraised value of homes, a real estate agent computes the least- squares regression line for predicting a home's value in 2002 from its value in 1992. The equation of the least-squares regression line is: y $44,000 + 1.8x where y represents a home's value in 2002 and x is the value in 1992 Question: Explain y, $22,000, 1.6 and x separately? Suppose Julia owns a home that was worth S100,000 in 1992. What would be...
Interpreting technology: The following display from the TI-84 Plus calculator presents the least squares regression line for predicting the price of a certain stock ) from the prime interest rate in percent 6). LinRed a=2.48512318 1 = 0.39651976 20369149179 p=0.60757648 Part 1 of 3 Write the equation of the least squares regression line. Use the full accuracy shown in the calculator output (do not round your answers). Regression line equation: 9 = 2.48512318 + 0.39651976x Part: 1/3 Part 2 of...
Objective 2: Interpret the Slope and the y-Intercept of the Least-Squares Regression Line 4.2 Least-Squares Regression 4.2.13 0 of 1 Point Question Help A student at a junior college conducted a survey of 20 randomly selected full-time students to determine the relation between the number of hours of video game playing each week, x, and grade-point average, y. She found that a linear relation exists between the two variables. The least-squares regression line that describes this relation is y0.0575x +...
The least squares regression line for the cost of a diamond necklace from a department store (y, in dollars) in the year x years since 2000 is given by yˆ=1823+314x. Interpret the y-intercept of the least squares regression line.
Using data on the appraised values of homes, a real estate agent computes the least squares regression line for predicting a home's value in the year 2010 (y) from its value in 2000 (x). The Equation of the regression line is y=$22,000 + 1.6x 1. By how much does a home's 2010 value change for each $1 increase in the home's 2000 value? 2. If we were to use the regression line to predict the home's value in the year...