(a) For the estimated regression equation given, compute R2.(Round your answer to two decimal places.) R2 - (b) Compute the adjusted r-square, R2. (Round your answer to two decimal places.) (c) Does the model appear to explain a large amount of variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the nearest integer.) The adjusted coefficient of determination shows that |% of the variability has been explained by the two independent variables; thus, we conclude that the model -Select--- v explain a large amount of variability.

Using the given equation for the regression line shown, determine the amount of time required to completely burn the candle. [2C] A Burning Candle 35.0 30.0 25.0 y = -0.2x + 30 20.0 15.0 10.0 5.0 0.0 20 40 60 80 100 Time (min) Height of Candle (cm)

A regression was run to determine if there is a relationship between hours of TV watched per day, x, and number of sit-ups, y, a person can do in a minute. The results of the regression were: y=ax+b a=-1.255 b=21.239 Use this to predict the number of sit-ups a person who watches 11.5 hours of TV can do in a minute. Round to the nearest whole number.

2. Participants were kept awake for a certain number of hours before given a visuospatial task. Researchers measured how many correct responses each participant had. Results are shown below. Use alpha = .01. Number of Correct Hours Kept Awake (X) Responses (Y) 21 X = 10 SS, = 422 Y = 10 SS, = 690 2 4 19 %3D 6. 13 SPxy =-525 ху 5 20 S, =5.70 S, = 7.29 9. 11 10 9. 14 5 15 5 17 2 18 1 17 1 13 4 18 8 11 A. Graph the data. B. State the hypotheses. C. Make a decision about the null. a. Calculate Pearson's r i. Decision about null hypothesis? b. Calculate effect size i. Interpret effect size. D. State your conclusion. E. Relate your conclusion to the research. F. Calculate the regression formula. G. If someone was kept awake for 9 hours, what is the predicted number of correct responses?

Use B = -15.812961, and ₁ = 0.096300 to determine the equation of the estimated regression line. (Round your numerical answers to four decimal places.) ŷ =

The fitted regression is Sales = 842 − 37.5 Price. (a-1) If Price = 1, what is the prediction for Sales? (Round your answer to 1 decimal place.)  (b) If Price = 20, what is the prediction for Sales?

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=a+bx b=-0.993 a=29.135 r2=0.463761 r=-0.681 Use this to predict the number of situps a person who watches 6 hours of TV can do. Round 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+b a=-1.33 b=25.138 r2=0.712336 r=-0.844 Use this to predict the number of sit ups a person who watches 10 hours of TV can do, and please round your answer to a whole number.