A random sample of 22 students was selected from a class. The goal is to predict GPA (y) from the ACT score (x) using simple linear regression. The following data set was obtained from the sample: EY = 67.871, EX,Y, = 1825.538, Ex? = 16062, EY?= 221.0524 ΣΧ-590, %3D Using the data set: a) Construct a 95% prediction interval on the average GPA of four students whose ACT score is 28. b) Construct a 95% prediction interval for the GPA of an individual student whose ACT score is 28. c) Construct a 95% confidence interval for the mean GPA of students whose ACT score is 28.
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- A random sample of twelve students were chosen, and their midterm test score (y), as- signment score (x1), and missed classes (x2) were recorded as follows: Midterm Score, y Assignment Score, x1 Classes Missed, x2 85 74 76 90 85 87 94 98 81 91 76 74 65 50 55 65 55 70 65 70 55 70 50 55 5 7 5 2 6 3 2 5 4 3 1 4 (i) What is the fitted multiple linear regression equation of the form yˆ = b0 + b1x1 + b2x2? (ii) From part (i) above, estimate the midterm test score grade for a student who has an assignment score of 60 and missed 4 classes.A researcher notes that, in a certain region, a disproportionate number of software millionaires were born around the year 1955. Is this a coincidence, or does birth year matter when gauging whether a software founder will besuccessful? The researcher investigated this question by analyzing the data shown in the accompanying table. Complete parts a through c below. a. Find the coefficient of determination for the simple linear regression model relating number (y) of software millionaire birthdays in a decade to total number (x) of births in the region. Interpret the result. The coefficient of determination is 1.___? (Round to three decimal places as needed.) This value indicates that 2.____ of the sample variation in the number of software millionaire birthdays is explained by the linear relationship with the total number of births in the region. (Round to one decimal place as needed.) b. Find the coefficient of determination for the simple linear regression model…Years of Work Experience and number of Job Offers of 10 job-seekers were as follows: Work Exp. 4 2 5 3 7 12 2 5 4 9 No. of Offers 7 1 8 4 13 19 3 11 9 15 a. Fit the regression equation of No. of Job Offers on Years of Work Experience. b. What will be the predicted number of offers for an applicant with 6 years of experience? c. Verify the relationship between the number of job offers and years of work experience using at least two relevant methods
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- The marketing manager of a supermarket chain would like to determine the effect of shelf spaceon the sales of pet food. A random sample of 10 stores was selected, and the results are presentedbelow. Store shelf space in cm weekly sales in thousand pesos 1 45 18 2 45 21 3 75 15 4 80 18 5 95 23 6 100 26 7 135 22 8 140 27 9 185 25 10 190 28 d. Using the estimated simple linear regression equation Y=15.6414+0.0611X, estimate the weekly sales when theshelf space is 230cm? 250cm? e. Compute the coefficient of determination and interpret its value.A random sample of 10 individuals is selected from a population, and measurements on two variables (X and Y) are obtained, as seen in the table below. Individual X Y 1 9 6 2 4 4 3 6 5 4 8 5 5 9 5 6 8 6 7 4 4 8 8 6 9 7 5 10 9 6 Assuming all the model assumptions are met, and the inference procedures are valid, then: a. to calculate the value of the y-intercept of the fitted regression line. b. calculate the value of the slope of the fitted regression line.Consider the multiple regression model shown next between the dependent variable Y and four independent variables X1, X2, X3, and X4, which results in the following function:Ŷ = 33 + 8X1 − 6X2 + 16X3 + 18X4For this model, there were 35 observations; SSR = 1,544 and SSE = 600. Assume a 0.01 significance level.Based on the given information, which of the following conclusions is correct about the statistical significance of the overall model? Multiple Choice Reject the null hypothesis that β3 = 0. Do not reject the null hypothesis that β1 = β2 = β3 = β4 = 0. Reject the null hypothesis that β1 = 0. Reject the null hypothesis that β1 = β2 = β3 = β4 = 0.
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