No. of absences, xFinal grade, y0123456 78 988.5 85.7 82.8 80.4 77.5 73.1 63.5 68.1 65.3 62.5(a) Find the least-squares regression line treating the number of absences, x, as the explanatory variable and the final grade, y, as the response variable.ŷ=[ x+(Round to three decimal places as needed.)(b) Interpret the slope and y-intercept, if appropriate.Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.(Round to three decimal places as needed.)OA. For a final score of zero, the number of days absent is predicted to bedays. No. of absences, xFinal grade, y02345 6 7 89188.5 85.7 82.8 80.4 77.5 73.1 63.5 68.1 65.3 62.5HIEDD(c) Predict the final grade for a student who misses five class periods and compute the residual. Is the observed final grade above or below average for this numberof absences?The predicted final grade is. This observation has a residual of [ which indicates that the final grade is(Round to one decimal place as needed.)(d) Draw the least-squares regression line on the scatter diagram of the data. Choose the correct graph below.average.

x y 0 88.5 1 85.7 2 82.8 3 80.4 4 77.5 5 73.1 6 63.5 7 68.1 8 65.3 9 62.5…

Answered: No. of absences, x Final grade, y 0 1 2…

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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show how to do it on a TI-84 please and thank you From the data, find the least-squares regression line. What type of correlation is there? Study Hours Test Score 0 20 1 35 2 50 4 47.5 4 65 5 72.5 5 75 6 67.5 6 92.5 7 85 7 90 8 95 5 70 y=9.051+24.765x.y=9.051+24.765x. There is positive linear correlation. y=24.765−9.051x.y=24.765-9.051x. There is negative linear correlation. y=24.765+9.051x.y=24.765+9.051x. There is positive linear correlation. y=−24.765+9.051x.y=-24.765+9.051x. There is negative linear correlation y=9.051−24.765x.y=9.051-24.765x. There is negative linear correlation.
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. LOADING... Click here to view the weight and gas mileage data. Question content area bottom Part 1 (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. ModifyingAbove y with caretyequals=enter your response herexplus+(enter your response here) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.)
What is the least-squares regression line with the point (9,13) included in the data set? (Type integers or decimals rounded to four decimal places as needed.) Data Set o X y 3 4 8 81 10 11 9 11 7 12 13 13 14 10 12 10 11 (This is a reading assessment question. Be certain of your answer because you only get one attempt on this question.)
Compute the least-squares regression line for predicting the right foot temperature from the left foot temperature. Round the slope and y-Intercept values to four decimal places.
Data on 12 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. ski 85 2.5. treadmill = Coefficients Estimate (Intercept) Treadmill 85 - 2.5 Std. Error What is the test statistic? -2.5 0.45 1 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? Round your answers to three decimal places. Using your answer from the previous question, find the p-value. Part 2 o
The data below represents commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. Commute time (in minutes), x 5, 20, 25, 40, 60, 84, 105 Well-being index score, y 69.1, 67.8, 67.1, 66.3, 66.0, 64.3, 62.3 Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the response variable. y=___x + ____ (round to three decimal places as needed).
Statistical software is generally used to test the significance of a regression model. The table below shows software output for a least squares regression line. Fill in the blanks with the correct answers, using the table above. Assume a significance level of 0.05. The variable "unemp" is the_______variable. The value of the slope of the regression line is_______. The slope of the regression line is not significantly different from______because the p-value is______ which is larger than 0.05.
21. Least-squares OK? Following is a residual plot produced by MINITAB. Was it appropriate to compute the least-squares regression line? Explain. Residuals Versus x -2 -3 5.0 5.5 6.0 6.5 7.0 7.5 8.0 Residual
The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7 Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample? (b) Write the estimated regression equation. ŷ = (c) What is the value of sb1? Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value =

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