Given the linear regression model: Yi = Bo+B1x1 + ẞ2xi2 + Єi, i = 1,..., n Consider the following sums from n = 10 observations on Y, X1, and X2: 10 10 10 Σ3 = 20, Σ i=1 10 Συ? = 98.2, i=1 i=1 10 10 10 71 =0.0, Σ i=1 10 x12 = 0.0 Σαμ = 92, Σα? i=1 = 163 i=1 10 Σ Yixil = 59, Yixi2 = 88, Xi1i2=119 i=1 i=1 i=1 Explore the following steps: 1. Construct the XTX matrix and the vector XTy. 2. Calculate the least-squares estimates of Bo, B₁, and ẞ2, along with the residual sum of squares, SSE. 3. Investigate the hypothesis that the coefficient of X 2, represented by 32, is zero.

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=-0.853 b=34.041 r2=0.389376 r=-0.624 Use this to predict the number of situps a person who watches 7 hour(s) of TV can do, and please round your answer to a whole number.

Q22. Find out the regression coefficients of Y on X and of X on Y on the basis of following data: ΣΧ- 50 X = 5, Er = 60, F=6, ΣΧΥ-350 Variance of X= 4, Variance of Y= 9

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=-1.38 b=39.555 r2=0.693889 r=-0.833 Assume the correlation is significant, and use this to predict the number of situps a person who watches 7 hours of TV can do (to one decimal place)

A statistical program is recommended.A highway department is studying the relationship between traffic flow and speed. The following model has been hypothesized:y = 0 + 1x + wherey traffic flow in vehicles per hourx = vehicle speed in miles per hour.The following data were collected during rush hour for six highways leading out of the city.Traffic Flow(y)Vehicle Speed(x )1,247351,320401,218301, 327451,341501, 11325(a) Develop an estimated regression equation for the data. (Round bo to one decimal place and b1 to two decimal places.) =

The following estimated regression equation based on 10 observations was presented. ŷ = 28.1270 + 0.6906x, + 0.3980x2 (a) Develop a point estimate of the mean value of y when x, = 170 and x2 = 350. (b) Develop a point estimate for an individual value of y when X1 = 170 and X2 = 350.

A regression was run to determine if there is a relationship between hours of study per week (xx) and the final exam scores (yy).The results of the regression were: y=ax+b a=6.22 b=21.6 r2=0.477481 r=0.691 Use this to predict the final exam score of a student who studies 8 hours per week, 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 (x) and number of situps a person can do (y). The results of the regression were: ý = bo + bịx bo = 36.465 - 1.082 b1 - 0.646 Use this to predict the number of situps a person who watches 2.5 hours of TV can do (to one decimal place)

Suppose the following regression equation was generated from the sample data of 50 cities relating number of cigarette packs sold per 1000 residents in one week to tax in dollars on one pack of cigarettes and if smoking is allowed in bars: PACKS, 58803.462982-1005.438507TAX, +284.030008BARS, + BARS, 1 if city / allows smoking in bars and BARS,= 0 if city i does not allow smoking in bars. This equation has an R² value of 0.305162, and the coefficient of BARS, has a value of 0,088136. Which of the following conclusions is valid? Answer Keypad Keyboard Shortcuts m Tables O If there is no cigarette tax in a city that allows smoking in bars, the approximate number of cigarette packs sold per 1000 people is 58803. O According to the regression equation, cities that allow smoking in bars have lower cigarette sales than cities that do not allow smoking in bars. O More than half of the variation in cigarette sales is explained by cigarette taxes and whether or not a city allows smoking in bars.…

For 40 eruptions of the Old Faithful geyser in Yellowstone National Park, duration times (sec) were recorded along with the heights (ft) of the eruptions. The linear correlation coefficient is r = 0.0915 and the regression equation is  = 119 + 0.0331x, where x represents duration time. The mean of the 40 duration times is 245.0 sec and the mean of the 40 heights is 127.2 ft. What is the best predicted height of an eruption with a duration time of 120 min?