The accompanying data represent the number of days absent, x, and the final exam score, y, for a sample of college students in a general education course at a large state university. Complete parts (a) through (e) belowE Click the icon to view the absence count and final exam score dataClick the icon to view a table of critical values for the correlation coefficient.(a) Find the leasi-squares regression line treating number of absences as the explanatory variable and the final exam score as the response variable.(Round to three decimal places as needed.)(b) Interpret the slope and the y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice.(Round to three decimal places as needed.)O A. For every additional absence, a student's final exam score dropspoints, on average. It is not appropriate to interpret the y-intercept.O B. For every additional absence, a student's final exam score dropspoints, on average. The average final exam score of students who miss no classes isIt is not appropriate to interpret the slope.OL The average final exam score of students who miss no classes isO D. It is not appropriate to interpret the slope or the y-intercept.(c) Predict the final exam score for a student who misses five class periods.y = (Round to two decimal places as needed.)Compute the residual.ITRound to twn decimal nlaces as needed )StatcrunchNextA 11:16esc%23&49.backspaceey*00口 85 The accompanying data represent the number of days absent, x, and the final exam score, y, for a sample of college students in a general education course at a large state university. Complete parts (a) through (e) below.Click the icon to view the absence count and final exam score dataClick the icon to view a table of critical values for the correlation coefficient......he final exar(a) Find the least-squaresReferenceAbsences and final exam scores(Round to three decimalCritical Values for Correlation Coefficientanswer box(b) Interpret the slope an(Round to three decimalNo. of9.absences, x0 1 2 3 4 5 6 730.997t appropriateO A. For every addition0.950Final86.783.680.2 78.3 73.5 64.7 72.2 65.9 65.9O B. For every additionerage final eexam score, y 88.450.878O C. The average final0.811terpret the sl0.754O D. It is not appropria80.707PrintDone(c) Predict the final exam6.0.666y =(Round to two de100.632110.602Compute the residual.120.576(Round to hwn decim130.55314StatcrunchNext11:16曲

Answered: Image /qna-images/answer/b4aa80bb-330b-4680-94aa-36737946f7ef.jpg

Answered: The accompanying data represent the…

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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Compute the value of the correlation coefficient for the data obtained in the study of the number of absences and the final grade of the seven students in the statistics class. Student. Number of absences. Also, find the equation of the regression line for the given data? Student. Number of absences. Final grade% ---------------------------------------------------------------------------- A. 6 82 B 2 86 C. 15 43 D. 9 74 E. 12 58 F 5 90 G 8 78
Part A: What is the most likely value of the correlation coefficient of the data in the table? Based on the correlation coefficient, describe the relationship between time and surface area of the lake. [Choose the value of the correlation coefficient from −1, −0.98, −0.5, −0.02.] Part B: What is the value of the slope of the graph of surface area versus time between 15 and 20 days, and what does the slope represent? Part C: Does the data in the table represent correlation or causation? Explain your answer.
A car lot wants to predict the # of car accessories they need to sell monthly based on the number of cars sold. The car lot owner randomly selects 12 months of data. The simple linear regression equation is ý = 14 - 2x. The coefficient of determination is 0.6364. What is the correlation coefficient to describe the relationship between these two variables? Multiple Choice
A movie studio wishes to determine the relationship between the revenue from rental of comedies on streaming services and the revenue generated from the theatrical release of such movies. The studio has the following bivariate data from a sample of fifteen comedies released over the past five years. These data give the revenue x from theatrical release (in millions of dollars) and the revenue y from streaming service rentals (in millions of dollars) for each of the fifteen movies. Also shown are the scatter plot and the least-squares regression line for the data. The equation for this line is y = 3.58 +0.15x. Theater revenue, x (in millions of dollars) 26.4 36.7 43.5 31.6 60.4 14.8 21.3 49.3 7.7 24.8 27.8 12.9 60.6 26.4 66.4 Send data to calculator V Rental revenue, y (in millions of dollars) 8.1 12.4 6.8 5.1 15.4 2.6 5.8 16.5 2.2 9.1 3.3 9.8 10.8 11.7 9.6 Based on the studio's data and the regression line, complete the following. Rental revenue in millions of dollars) 0 X 10 X 20…
The accompanying table shows the height (in inches) of 8 high school girls and their scores on an IQ test. Here is the data table Height, x IQ score, y62 10156 9363 10167 10858 8962 10565 11054 122 Display the data in a scatter plot. The graph in the image have been chosen. The calculated sample correlation coefficient r is r = [ 0.042 ] (Round to three decimal places as needed.) Describe the type of correlation, if any, and interpret the correlation in the context of the data. There is [A Strong Positive/A Perfect Positive/A Moderate Negative/A Strong Negative/No/A Moderate Positive/or a Perfect Negative] linear correlation.
Please give answers of b&c
The trend of thinner beauty pageant winners has generated charges that the contest encourages unhealthy diet habits among young women. Listed below are body mass indexes (BMI) for beauty pageant winners from two different time periods. Find the coefficient of variation for each of the two sets of data, then compare the variation. The coefficient of variation for the BMI's of beauty pageant winners from the 1920s and 1930s is %
A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x). The results of the regression were: ý=a+bx a=-1.692 b=0.117 (a) Write the equation of the Least Squares Regression line of the form (b) Which is a possible value for the correlation coefficient, r? O -0.649 O 1.32 O 0.649 O-1.32 (c) If a country increases its life expectancy, the happiness index will O increase O decrease (d) If the life expectancy is increased by 2.5 years in a certain country, how much will the happiness index change? Round to two decimal places. (e) Use the regression line to predict the happiness index of a country with a life expectancy of 75 years. Round to two decimal places.
This table shows the average heights for American boys in 1990. Age (years) Height (cm) birth 50.8 2 83.8 3 91.4 5 106.6 7 119.3 10 137.1 14 157.5 Decide which variable should be the independent variable and which should be the dependent variable. Draw a scatter plot of the data. Does it appear from inspection that there is a relationship between the variables? Why or why not? Calculate the least-squares line. Put the equation in the form of: ŷ = a + bx Find the correlation coefficient. Is it significant? Find the estimated average height for a one-year-old. Find the estimated average height for an eleven-year-old. Does it appear that a line is the best way to fit the data? Why or why not? Are there any outliers in the data? Use the least squares line to estimate the average height for a sixty-two-year-old man. Do you think that youranswer is reasonable? Why or why not? 10. What is the slope of the least-squares (best-fit) line?…
Calculate test statistic Decide whether to reject or fail to reject
(b) Calculate the sample correlation coefficient r. (c) Describe the type of correlation, if any, and interpret the correlation in the context of the data. (d) Use the table of critical values for the Pearson correlation coefficient to make a conclusion about the correlation coefficient.

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