Due in 3 hours, 41 minutes. Due Sat 04/18/2020 11:59 pmShow Intro/InstructionsA regression was run to determine if there is a relationship between hours of study per week (x) and the final examscores (y).The results of the regression were:у-ах+ba=6.704b=30.59p2=0.972196r=0.986Use this to predict the final exam score of a student who studies 8.5 hours per week, and please round your answer to awhole number.Hint: HelpLicensePoints possible: 1This is attempt 1 of 3.Message instructor about this questionSubmit

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Answered: Due in 3 hours, 41 minutes. Due Sat…

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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A group of scientists are interested in finding out whether the days of rainfall during the dry season could predict the magnitude of butterfly migration in the local area during wet season. Every year for twenty years, they count the total days of rainfall during the dry season preceding the migration and measure the number of butterflies migrating in the following wet season. Which type of statistical test should they use to analyze their data? (pearson's r correlation OR, simple regression equation)
A researcher is hired to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance in miles between home and work for the employees. A sample of 10 employees was chosen and the results are listed in the chart below. Use the proper Data Analysis function in Excel to computethe regression equation needed to predict the number of missed days that could be expected foremployees who live certain distances away from the workplace. Distance to Work (miles): 1, 3, 4, 6, 9, 10, 12, 14, 14, 18 Number of Days Absent: 9, 6, 7, 7, 6, 4, 5, 3, 4, 2
Suppose the athletic director at a university would like to develop a regression model to predict the point differential for games played by the men's basketball team. A point differential is the difference between the final points scored by two competing teams. A positive differential is a win, and a negative differential is a loss. For a random sample of games, the point differential was calculated, along with the number of assists, rebounds, turnovers, and personal fouls. Use the data in the accompanying table attached below to complete parts a through e below. Assume a = 0.05. a) Using technology, construct a regression model using all three independent variables. y = __ + (_)x1 + (_)x2 + (_)x3 + (_)x4 b) Test the significance of each independent variable using a= 0.10. c) interpret the p-value for each independent variable. d) Construxt a 90% confidence interval for the regression coefficients for each independent variable and interpret the meaning. e) Using the results from…
After performing a statistical regression on a set of data the value of the correlation coefficient r was -0.2041. What does that imply about the data?
A researcher examined the relationship between the annual tax amount paid by the construction company owners and the total expense items spent for the construction with regression analysis and calculated the regression line estimation as Y=6+2X. According to this; Write the interpretations of the cutoff and regression parameters, respectively, in your OWN SENTENCES in a statistically appropriate way over the tax amount and expenses.
You conducted a regression analysis between the number of absences and number of tasks missed by your 5 classmates in Statistics and Probability. It resulted that the regression line is y = 0.65x + 1.18. What is the predicted number of tasks missed of a learner who is always present? a. The learner has 1 task missed. b. The learner has less than 2 tasks missed. c. The learner has more than 2 tasks missed. d. The learner has no task missed.
A man had a height that is at the 99.725th percentile of the distribution for height. This means that his height is approximately _ SD(s) above the average. Using regression, we can predict that his weight will be approximately _ SD(s) above average. Therefore, he is predicted to have a weight at the _ percentile of the distribution for weight.
find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. In each case, there is sujficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Altitude and Temperature Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded by the author during Delta Flight 1053 from New Orleans to Atlanta. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet).
4. In multiple regression, why do we prefer our IVs to be uncorrelated with each other (i.e., we want to be 0)?
Having the variation data regression line r=0.931 and the coefficient of determination is 0.867.. And the variation of 91.6% is explained by the regression line.. About the unexplained variation? [ ]% of the variation is unexplained and is due to other factors or to sampling error. (Round to one decimal place as needed.)
At a .01 level of significance is there sufficient evidence to conclude that the number of books sold is related to the number of registered students in a straight-line manner, and give the regression equation.
Assume that you have collected cross-sectional data for average hourly earnings (ahe), the number of years of education (educ) and gender of the individuals (you have coded individuals as "1" if they are female and "0" if they are male; the name of the resulting variable is DFemme). Having faced recent tuition hikes at your university, you are interested in the return to education, that is, how much more will you earn extra for an additional year of being at your institution. To investigate this question, you run the following regression: ahe= -4.58 + 1.71×educ N = 14,925, R2 = 0.18, SER = 9.30 a. Interpret the regression output. b. Being a female, you wonder how these results are affected if you entered a binary variable (DFemme), which takes on the value of "1" if the individual is a female, and is "0" for males. The result is as follows ahe= = -3.44 - 4.09×DFemme + 1.76×educ N = 14,925, R2 = 0.22, SER = 9.08 Does it make sense that the standard error of the regression decreased…

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