In a regression analysis involving 27 observations, the following estimated regression equation was developed. ŷ = 25.2 + 5.5x₁ For this estimated regression equation SST = 1,525 and SSE = 520. (a) Ata = 0.05, test whether x₁ is significant. (b) Suppose that variables x₂ and x3 are added to the model and the following regression equation is obtained. ŷ = 16.3 +2.3x₁ + 12.1x₂ - 5.8x3 For this estimated regression equation SST = 1,525 and SSE = 100. Use an F test and a 0.05 level of significance to determine whether x₂ and x3 contribute significantly to the model.

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=-1.307 b=32.392 r²=0.675684 r=-0.822 Use this to predict the number of situps a person who watches 12.5 hours of TV can do (to one decimal place)

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+ba=-1.114b=38.796r2=0.815409r=-0.903 Use this to predict the number of sit ups a person who watches 7 hours of TV can do, and please round your answer to a whole number.

Data from 147 colleges from 1995 to 2005 (Lee,2008) were tested to predict the endowments (in billions) to a college from the average SAT score of students attending the college. The resulting regression equation was Y = -20.46 + 4.06 (X). This regression indicates that: a. for every one-point increase in SAT scores, a college can expect 4.06 billion more in endowments. b. most colleges have very high endowments. c. for every one-point increase in SAT scores, a college can expect 20.46 billion fewer in endowments. d. for every one-dollar increase in endowments, the college can expect a half-point increase in SAT scores.