**Table 4.24**| \( x \) | 3 | 5 | 6 | 8 | 11 ||--------|---|---|---|---|----|| \( y \) | 21 | 25 | 24 | 30 | 41 |### Problem Statement**a.** Use tables and formulas to determine the linear regression equation for the data set.**b.** Verify that the regression line has a smaller sum of squared error than lines A and B from Exercise (33). ### Data from Exercise #33#### Table Data| Line A: y = 6 + 4x | Line B: y = 7 + 3.5x ||---------------------|---------------------|| x | 3 | 5 | 6 | 8 | 11 || y | 21 | 25 | 24 | 30 | 41 | 50 |#### Scatterplot of y vs xThe scatterplot depicts the relationship between variable x and variable y. Individual data points are marked with red dots. Two linear regression lines are shown:- **Line A**: Represented by the equation \( y = 6 + 4x \), this line is labeled as "A" on the graph.- **Line B**: Represented by the equation \( y = 7 + 3.5x \), this line is labeled as "B" on the graph.**Axes:**- The x-axis ranges from 3 to 11, consistent with the values provided in the table.- The y-axis ranges from 20 to 50, encompassing the calculated y-values from both Line A and Line B.The scatterplot reveals how the given linear models (Line A and Line B) fit the data points:- For Line A, the y-values for the given x-values are: 18, 26, 30, 38, and 50.- For Line B, the y-values for the given x-values are: 17.5, 24.5, 28, 36, and 47.5.Each dot's position reflects the corresponding y-value for each x as calculated by the specified equations, highlighting the relationship and differences between the two lines.

Answered: x 3 5 6 8 8 11 y 21 25 24 30 41 50 8 a.…

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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answer using minitab or excel
Solve b
An instructor asked a random sample of eight students to record their study times at the beginning of a course. She then made a table for total hours studied (x) over 2 weeks and test score (y) at the end of the 2 weeks. The table is given below. Complete parts (a) through (f). x 10 13 10 18 6 15 16 21 y 93 79 81 74 85 81 85 80 a. Find the regression equation for the data points. b. Graph the regresson equation c. Describe the apparent relationship between the two variables. d. Identify the predictor and response variables. e. Identify outliers and potential influential observations. f.Predict the score for a student that studies for 17 hours.
Use the data in the table below to complete parts (a) through (d). x 37 34 40 46 42 50 62 56 51 y 22 20 25 32 27 30 30 25 28 Find the equation of the regression line. y=
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An educator wants to see how the number of absences for a student in her class affects the student's final grade by formulating the equation of the regression line. The data obtained from a sample are shown. Required: Slope of the regression line Only the final answer should be rounded-off to TWO decimal places. 12 Number of absences (x) 10 2 0 7 4 Final Grade (y) 65 68 70 94 77 96
Using the given equation for the regression line shown, determine the amount of time required to completely burn the candle. [2C] A Burning Candle 35.0 30.0 25.0 y = -0.2x + 30 20.0 15.0 10.0 5.0 0.0 20 40 60 80 100 Time (min) Height of Candle (cm)
Use the data in the table below to complete parts (a) through (d). 39 33 40 47 42 50 59 56 51 24 22 27 32 30 31 31 27 29 Click the icon to view details on how to construct and interpret residual plots. (a) Find the equation of the regression line. (Round to three decimal places as needed.) (b) Construct a scatter plot of the data and draw the regression line. Plot the x-values on the horizontal axis and the y-values on the vertical axis. Choose the correct graph below. OA. В. Oc. OD. 34 70, (c) Construct a residual plot. Plot the x-values on the horizontal axis and the residuals on the vertical axis. Choose the correct graph below. O A. Ов. Oc. OD. (d) Determine if there are any patterns in the residual plot and explain what they suggest about the relationship between the variables. The residual plot a pattern because the residuals about 0. This implies the regression line a good representation of the relationship between the variables.
1. In a manufacturing process the assembly line speed (feet per minute) was thought to affect the number of defective parts found during the impaction process. To test this theory, managers devised a situation in which the same batch of parts was inspected visually at a variety of line speeds. They collected the following data. Line Speed (x) Number of Defective Parts Found (y) 20 21 20 19 40 15 30 16 60 14 40 17 Use Microsoft Excel to generate regression output for this problem and use the information in the output to answer the following questions.
Grades on Midterm Grades on Final79 8464 7676 8385 9170 7586 7960 7975 8781 8263 63 The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, yˆ=b0+b1x . Round the slope and y-intercept to the nearest thousandth.

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x 3 5 6 8 8 11 y 21 25 24 30 41 50 8 a. Use tables and formulas to determine the linear regression equation for the data set. b. Verify that the regression line has a smaller sum of squared error than lines A and B from Exercise (33)