yIndependentDependentVariableVariable15127109.711What is he least squares regression estimate of the intercept?a) -1.3b) 16.41176c) 21.4d) -7.647 You are given the following information about x and y. Input this into Excel and complete the following question:yIndependentDependentVariableVariable15512109.711What is he least squares regression estimate of the intercept?a) -1.3b)16.41176c)21.4

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Answered: Independent Dependent Variable Variable…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Find the least squares regression line for temperature (x) and number of ice cream cones sold per hour (y). 65 70 75 80 85 90 95 100 105 y 8 10 11 13 12 16 19 22 23 Oŷ = 2.469x+48.240 ý = 0.383x-17.694 Oŷ = -125.376x+31.656 Oŷ = 0.4x-18
3 of 14 For many people, the women's figure skating competition is the highlight of the Olympic Winter Games. Scores in the short program x and scores in the free skate y were recorded for each of the 24 skaters who competed in both rounds during the 2010 Winter Olympics in Vancouver, Canada. Here is a scatterplot with least-squares regression line y =-16.2 +2.07x. For this model, s = 10.2 and = 0.736. 160 Interpret the value of s. 140 O 10.2% of the variation in free skate score is 120 accounted for by the least-squares regression line with x = short program score. The actual free skate score is typically about 10.2 points lower than the score predicted by the least- squares regression line with x = short program score. O For every 1 point increase in the short program score, the predicted free skate score increases by 10.2 points. O The actual free skate score is typically about 10.2 points higher than the score predicted by the least- squares regression line with x = short program…
A seafood-sales manager collected data on the maximum daily temperature, T, and the daily revenue from salmon sales, R, using sales receipts for 30 days selected at random. Using the data, the manager conducted a regression analysis and found the least-squares regression line to be Rˆ=126+2.37T. A hypothesis test was conducted to investigate whether there is a linear relationship between maximum daily temperature and the daily revenue from salmon sales. The standard error for the slope of the regression line is SEb1=0.65. Assuming the conditions for inference have been met, which of the following is closest to the value of the test statistic for the hypothesis test? t=0.274 A t=0.65 B t=1.54 C t=3.65 D t=193.85 E
The following table number of absences (x) and the grade earned on the first exam (y) for a Statistics class. 3. 9. 5 8. y 86 76 91 54 84 54 The least-squares regression line is in the form y= bo + b,x. Compute the value of bo. That is, compute the intercept. Round your answer to two decimal places. Write only a number as your answer. Your Answer: Answer
When a stone is dropped in a pond, ripples are formed and travel in concentric circles away from where the stone was dropped. The equation of the least-squares regression line is (picture attached) What is the predicted area of the circle, in cm2, 4 seconds after the stone is dropped? 49.72 cm2 199.43 cm2 311.89 cm2 1854.10 cm2
11. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.
The data below represent commute times (in minutes) and scores on a well-being survey. Commute Time (minutes), x 5 15 25 35 60 84 105 Well-Being Index Score, y 69.2 68.4 67.7 67.3 66.3 66.0 64.6 (a) Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the response variable. (b) Interpret the slope and y-intercept, if appropriate. (c) Predict the well-being index of a person whose commute time is 30 minutes. (d) Suppose Barbara has a 20-minute commute and scores 67.4 on the survey. Is Barbara more "well-off" than the typical individual who has a 20-minute commute?
An article gave a scatter plot, along with the least squares line, of x = rainfall volume (m³) and y data on rainfall and runoff volume (n = runoff volume (m³) for a particular location. The simple linear regression model provides a very good fit to 15) given below. The equation of the least squares line is y = -2.364 + 0.84267x, ² 0.976, and s = 5.21. = x 5 12 14 17 23 30 40 47 55 67 72 81 96 112 127 y 3 9 12 14 14 24 27 45 38 46 52 71 81 100 101 (a) Use the fact that s = 1.43 when rainfall volume is 40 m³ to predict runoff in a way that conveys information about reliability and precision. (Calculate a 95% PI. Round your answers to two decimal places.) Ŷ 28.25 1x ) m³ Does the resulting interval suggest that precise information about the value of runoff for this future observation is available? Explain your reasoning. OYes, precise information is available because the resulting interval is very wide. 34.46 Yes, precise information is available because the resulting interval is very…
The number of speeding tickets issued (Y) and the number of radar traps set up by police officials on an expressway (X) during nine weekends are shown below: No. of speed traps X: 10 12 15 8 8 12 11 7 13 14 No. of speeding tickets issued Y: 76 84 93 61 79 70 55 84 85 (a) Draw a scatter plot of the data. (b) Find the equation of the least squares regression line using the information below: x, -102, Σx-1212, Σν- 687, Σν,-53649, ΣΧ.y, 8036. (Show the details of your work.) T

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Independent Dependent Variable Variable 15 5 12 7 10 7 11 What is he least squares regression estimate of the intercept? a) -1.3 O b) 16.41176 c) 21.4 -7.647