(b) Calculate the t-statistics of the coefficient of AADT (b = 0.002) using the following equation: /SSE/(n – 2) VE:(x; – x)² where x, = observed AADT for segment i; x = average observed AADT for all segments. Assess statistical significance of the coefficient b at a 95% confidence interval using hypothetical testing. Assume null hypothesis (Ho): b is not statistically different from zero and alternative hypothesis (H1): b is statistically different from zero. Sketch a t-distribution curve and show 1) the calculated t-statistics; 2) the critical values of t-statistics (tent0.025 = 2.01 for sample size = 48); and 3) the confidence interval in the sketch.
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- In a simple linear regression model with one predictor variable, what is the coefficient of determination (R-squared) if the Pearson's correlation coefficient between the predictor and response variable is 0.6?A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. The least-squares regression equation for these data is Yi=−1.068+1.394Xi and the standard error of the estimate is SYX=19.412. Assume that the straight-line model is appropriate and there are no serious violations the assumptions of the least-squares regression model. Significance level at 0.05 . Complete parts (a) and (b) below.The average asking rent for 10 markets, and the corresponding monthly mortgage on the median priced home (including taxes and insurance) for 10 cities are given in Rent ($) Mortgage ($) City 1 840 539 City 2 1062 1002 City 3 823 626 City 4 779 711 City 5 796 655 City 6 1071 977 City 7 953 776 City 8 851 695 City 9 762 651 City 10 723 654 What is the simple linear regression model to predict the monthly mortgage on the median priced home, using average asking rent? Fill in the coefficient for the LINEAR independent variable (keep 2 decimal points).
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- Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear1. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear1 represents their range of ankle motion (in degrees) while wearing typical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing typical shoes, y^ , based on their range of ankle motion while barefoot, x . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26° while barefoot. Predict Jan's range of ankle motion while wearing typical shoes, ?̂ . Round your answer to two decimal places.…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…Develop an estimated multiple linear regression model that could be used to predict the alumni giving rate using the graduation, % of Classes Under 20 (LT20), and Student/Faculty Ratio (SFR) as independent variables. Discuss your findings. School SFR LT20 GT50 GRAD FRR GIVE Arizona State 24 42% 16% 59% 81% 8% Arkansas State—Jonesboro 19 49% 4% 37% 69% 11% Auburn 18 24% 17% 66% 87% 31% Air Force 8 74% 0.10% 81% 88% 11% Military Academy 8 95% 0% 86% 92% 28% Akron 20 39% 6% 35% 69% 15% Arizona 20 35% 17% 60% 79% 5% Arkansas 18 28% 18% 58% 83% 23% Ball State 18 34% 12% 57% 78% 11% Baylor 14 49% 9% 71% 85% 14% Boise State 21 33% 11% 27% 67% 8% Boston College 14 47% 6% 91% 96% 27% Bowling Green State 19 33% 7% 61% 75% 8% BYU 21 46% 12% 78% 84% 17% SUNY 16 36% 21% 67% 88% 9% Alabama 19 46% 16% 67% 85% 34% Central Michigan 22 32% 11% 54% 78% 8% Clemson 16 51% 13% 76% 91% 28% Colorado State 18 34% 19% 64% 83% 7% Berkley 17 62% 15% 91% 97% 12%…