(a) Fit the multiple regression model to these data.(b) Interpret the meaning of the slopes bl and b2 in this problem.(c) Predict the electric power consumption with x1 = 62, x2 = 22, x3 = 88 and x4 = 106(d) At the 0.05 level of significance, determine whether X1 and X2 make a contribution to theregression model respectively.(e) Interpret the meaning of the coefficient of multiple determination.(f) Compute the adjusted r². QI)The electric power consumed each month by a chemical plant is thought to be related to the averageambient temperature (x1), the number of days in the month (x2), the average product purity (x3),and the tons of product produced (x4). The past year's historical data are available and are presentedin the following table:yх1x2x3x42402524911002363121909527045248110274602587883016525919431672269499300802587972968425869626775248811027660259110528850259010026138238998Here is the Excel output from performing the regression analysis:Regression StatisticsMultiple RR SquareAdjusted R Square0.9230654760.852049873Standard Error11.78657744Observations12Coefficients Standard Errort StatP-valueUpper 95% Lower 95.0% Upper 95.0%Lower 95%Intercept-123.1312463157.2560575 -0.78299843x10.7572890890.279089778 2.71342467x27.5187839554.0101214191.8749517x32.4830785551.809385602 1.37233244x4-0.4811352320.55517418 -0.86663834

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A First Course in Probability (10th Edition)

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A statistical program is recommended. The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly Television Gross Newspaper Advertising Advertising ($1,000s) ($1,000s) Revenue ($1,000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 1 (a) Develop an estimated regression equation with the amount of television advertising as the independent variable. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s and y represent the weekly gross revenue in $1,000s.) y = 88.64 + 1.60x1 X (b) Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s, x₂ represent the amount of…
An individual wants to determine how much money a boy scout earns from selling popcorn (Y-variable) based on the length of time (in days) that they went out selling (X-variable). Using the following linear regression equation Ŷ = 5 + 20X: a. State the value of the intercept AND the value of the slope (2 points) b. Based on the given regression equation, what can you determine about the direction of the relationship between X and Y (i.e. is it positive or negative)? (2 points) c. If a boy scout sells popcorn for 4 days, how much money is he predicted to earn? (2 points)
The table shows the number of goals allowed and the total points earned (2 points for a win, and 1 point for an overtime or shootout loss) by 14 ice hockey teams over the course of a season. The equation of the regression line is y= - 0.558x + 216.186. Use the data to answer the following questions. (a) Find the coefficient of determination, r, and interpret the result. (b) Find the standard error of the estimate, s,, and interpret the result. Goals Allowed, x Points, y 218 212 216 220 257 266 274 200 211 206 216 204 264 244 O 111 106 99 90 86 83 45 105 100 101 94 83 67 68 (a) ? =O (Round to three decimal places as needed.)
An oceanographer measured the length, in meters, of a deepwater wave and its speed, in meters per second. The results are shown in the following table. (a) Find the equation of a linear regression line for the data where wave length is the independent variable, x, and speed is the dependent variable. (Round your numerical values to two decimal places.) y= ? (b) Using the equation from part (a), estimate the speed (in meters per second) of a wave that is 200 m long. (Round your answer to one decimal place.) ? m/s
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(a) Fit the multiple regression model to these data. (b) Interpret the meaning of the slopes b1 and b2 in this problem. (c) Predict the electric power consumption with x1 = 62, x2 = 22, x3 = 88 and x4 = 106