Question 5 of 14>Given are the 40-yard-dash times (in seconds) and long-jumpdistances (in inches) for a small class of 12 students. AlsoUse technology to create a residual plot. Sketch thegiven is the scatterplot created by this data:residual plot and comment about whether a linear modelis appropriate.O The linear model relating the dash time to the long-jump distance is not appropriate because thecorrelation of the values in the residual plot is near 0.Dash time (sec)5.41 5.05 7.01 7.17 6.73 5.68Long-jump distance (in.)171 184 90 65 78 130Dash time (sec)5.78 6.31 6.44 6.50 6.80 7.253020Long-jump distance (In.)173 143 92 139 120 11010-10-190-180--20--30-556575170-E160-150-140-130-120-110-100-90-80-Dash time (sec)O The linear model relating the dash time to the long-jump distance is appropriate because there is noleftover pattern in the residual plot. The residualslook randomly scattered around the residual = 0 line.302070E10-60-2 -10-6.5Dash time (sec)5.57.5-20--30-5.575Dash time (sec)The linear model relating the dash time to the long-jump distance is appropriate because there is noleftover pattern in the residual plot. The residualslook randomly scattered around the residual = 0 line.ong-jump di stance (in)jenpsa

Solution: The residual plot is In the residaul plot all the residual points are randomly…

Given are the 40-yard-dash times (in seconds) and long-jump distances (in inches) for a small class of 12 students. Also Use technology to create a residual plot. Sketch the given is the scatterplot created by this data: residual plot and comment about whether a linear model is appropriate. O The linear model relating the dash time to the long- jump distance is not appropriate because the correlation of the values in the residual plot is near 0. Dash time (sec) 5.41 5.05 7.01 7.17 6.73 5.68 Long-jump distance (in.) 171 184 90 65 78 130 Dash time (sec) 5.78 6.31 6.44 6.50 6.80 7.25 30 20 Long-jump distance (In.) 173 143 92 139 120 110 10 -10- -20- 190 180- -30- 75 Residual

Given are the 40-yard-dash times (in seconds) and long-jump distances (in inches) for a small class of 12 students. Also Use technology to create a residual plot. Sketch the given is the scatterplot created by this data: residual plot and comment about whether a linear model is appropriate. O The linear model relating the dash time to the long- jump distance is not appropriate because the correlation of the values in the residual plot is near 0. Dash time (sec) 5.41 5.05 7.01 7.17 6.73 5.68 Long-jump distance (in.) 171 184 90 65 78 130 Dash time (sec) 5.78 6.31 6.44 6.50 6.80 7.25 30 20 Long-jump distance (In.) 173 143 92 139 120 110 10 -10- -20- 190 180- -30- 75 Residual

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
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Question 19: Based on the data shown below, a statistician calculates a linear model y=−2.23x+48.46y=-2.23x+48.46. x y 4 40.7 5 36.2 6 34.1 7 34.3 8 28.9 9 28.3 10 27.8 11 25.5 12 19.4 13 19.9 Use the model to estimate the y-value when x=4x=4y = Use the model to estimate the y-value when x=8x=8y =
question 32 Statistics students in Oxnard College sampled 10 textbooks in the Condor bookstore and recorded the number of pages in each textbook and its cost. The bivariate data are shown below: Number of Pages (xx) Cost(yy) 575 95.25 351 60.65 788 137.2 600 108 393 75.95 897 155.55 504 76.6 571 86.65 516 79.4 986 166.9 A student calculates a linear modely = __________x +_____________ . (Please show your answers to two decimal places)Use the model to estimate the cost when number of pages is 829.Cost = $___________- (Please show your answer to 2 decimal places.)
Question 5: A college is interested in if the year of receiving high-level education (college and above) influences students’ salaries when they join the working field. The college randomly selected 4 students who recently received a full- time work offer and collected their year of receiving high-level education and salary per week. The raw data is: Participant Year of high-level education Salary per week Participant 1 4 1150 Participant 2 5 1300 Participant 3 7 1600 Participant 4 2 750 Plot the scatterplot and label the participants. Calculate the correlation coefficient between the year of high-level education and salary per week. Calculate the regression equation using the year of receiving high-level education to predict the salary per week. If a student graduate from college and graduate school in a total of 6 years, what is this student’s predicted salary per week?
Let Yt be the sales during month t (in thousands of dollars) for a photography studio, and let Pt be the price charged for portraits during month t. The data are in the file Week 4 Assignment Chapter 12 Problem 64. Use regression to fit the following model to these data:Yt = a + b1Yt−1 + b2Pt + etThis equation indicates that last month’s sales and the current month’s price are explanatory variables. The last term, et, is an error term. If the price of a portrait during month 21 is $10, what would you predict for sales in month 21? Sales Price $400,000 $15 $1,042,000 $12 $1,129,000 $24 $1,110,000 $18 $1,336,000 $18 $1,363,000 $30 $1,177,000 $27 $603,000 $24 $582,000 $36 $697,000 $27 $586,000 $24 $673,000 $27 $546,000 $30 $334,000 $33 $27,000 $24 $76,000 $27 $298,000 $30 $746,000 $18 $962,000 $21 $907,000 $24
Question 4 - 6 refer to following variables: Dependent variable: Sentence Months Independent variable: Year of Education After calculating the regression slope of the two variables, explain what the slope refers to by selecting two correct answers (NO PATIAL CREDIT). Form: For one unit increase in [a], [b] is predicted to [c] by the slop. For one unit increase in Sentence Months, For one unit increase in Year of Education, Sentence Months is predicted to increase by the slop Sentence Months is predicted to decrease by the slop Year of Education is predicted to increase by the slop Year of Education is predicted to decrease by the slop
QUESTION 2 XXX Electric Illuminating Company is doing a survey on the relationship between electricity used in kilowatt-hours (thousand) and the number of rooms in a private single-family residence. A random sample of 10 homes was selected and the electricity consumption recorded as below. ii. Find a suitable linear regression equation ? = ? + ??. iii. Determine the number of kilowatt-hours (thousand) for an eleven-room residence.
Question 4 The following table displays the mathematics test scores for a random sample of college students, along with their final SY16C grades. Fit the regression line y = a+bx to the data and interpret the results. Use the regression equation to determine the SY16C grade for a college student who scored 60 on their achievement test. What would their SY16C grade be? Mathematics test (x) SY16C grades (y) 1 39 65 2 43 78 3 21 52 4 64 82 5 57 92 6 47 89 7 28 73 8 75 98 9 34 56
Question 5 - what is the y-intercept of the equation of the regression line y = 36.11695 + 7.20508x A. 7.20508 B. 36.11695 C. Y D. X Question 6 - what is the slop of the equation of the regression line y = 36.11695 + 7.20508x? A. 7.20508 B. 36.11695 C . Y D . X
Question 18 At a local club, the ages of the members and the times they took to run a 10 km race are recorded. For a random sample of 25 club members aged between 27 and 60, their ages (x years) and times (y minutes) produce the following regression line. y = 0.83x + 41.28Four new members join the club. Their respective ages (in years) are 26, 18, 22, and 28. When the respective race times for the 4 new members are estimated, which one of these estimated race times is the most reliable?A. 62.86 minutesB. 56.22 minutesC. 59.54 minutesD. 64.52 minutes