A simple regression model developed for ten pairs of data resulted in a sum of squares of error, SSE = 125. The standard error of the estimate is O 12.5 O 3.5 O 25 O 15.6 O 3.95
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- If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-0.883 b=30.476 r2=0.660969 r=-0.813 Use this to predict the number of situps a person who watches 8 hour(s) of TV can do, and please round your answer to a whole number.A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy). The results of the regression were: y=ax+ba=-1.392b=32.952r2=0.527076r=-0.726Use this to predict the number of situps a person who watches 2 hour(s) of TV can do, and please round your answer to a whole number.
- Perform a regression on two different variables within the attached data, and exaplin what the findings determine. What decision can be made?A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: уах+ b a = -1.098 b = 37.154 r2 = 0.444889 r = -0.667 Use this to predict the number of situps a person who watches 11 hours of TV can do. situps = [one decimal accuracy]A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+ba=-0.719b=37.806r2=0.484416r=-0.696 Use this to predict the number of situps a person who watches 11.5 hours of TV can do (to one decimal place)
- For each gender, estimate an earnings regression where the dependent variable is Annual Earnings and the independent variable is Age. Report the results for each regression, and provide an interpretation of the coefficient associated with age. Female- annual earning: 49347.58 age: 42.81 education: 14.4 Male- Annual earning: 69984.54 age: 42.86 education: 14.11.A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (x) and number of sit ups a person can do (y). The results of the regression were: y=ax+b a=-0.784 b=34.422 r²=0.793881 r=-0.891 Use this to predict the number of sit ups a person who watches 0.5 hours of TV can do, and please round your answer to a whole number.The prelim grades (x) and midterm grades (y) of a sample of 10 MMW students is modeled by the regression line y = 12.0623 + 0.7771x. Estimate the prelim grade if the midterm grade is 83.
- A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.926 b=28.863 r²=0.470596 r=-0.686 Use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)A photoconductor film is manufactured at a nominal thickness of 25 mils. The product engineer wishes to increase the mean speed of the film and believes that this can be achieved by reducing the thickness of the film to 20 mils. Eight samples of each film thickness are manufactured in a pilot production process, and the film speed (in microjoules per square inch) is measured. For the 25-mil film, the sample data result is.x₁ = 1.15 and S₁ = 0.11, while for the 20-mil film, the data yield 2 = 1.06 and s2 = 0.09. Note that an increase in film speed would lower the value of the observation in microjoules per square inch. Do the data support the claim that reducing the film thickness increases the mean speed of the film? Use a = 0.10 and assume that the two population variances are equal and the underlying population of film speed is normally distributed. The appropriate decision for the test is to reject the null hypothesis True FalseFind the regression line of the points: (12.7, 18.7), (9.1, 17.2), (15.3, 14.5), (19.5, 12.9)
