A university building produces the following data during a monitoring period. Calculate the theoretical base load gas consumption using a linear regression analysis. Degree Days Experienced Per Month Gas 24 28 35 51 98 136 223 243 262 267 281 284 Consumption (GJ) 46 33 25 63 50 92 147 160 198 276 266 200

A university building produces the following data during a monitoring period. Calculate the theoretical base load gas consumption using a linear regression analysis. Degree Days Experienced Per Month Gas 24 28 35 51 98 136 223 243 262 267 281 284 Consumption (GJ) 46 33 25 63 50 92 147 160 198 276 266 200

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Consider the following simple linear regression model between the dependent variable, SALES (reflecting monthly sales, in thousands of dollars) and independent variable, ADVERTISING (representing the monthly advertising expenditure, in thousands of dollars): Expected (SALES) = 4.2 - 2.6 ADVERTISING. The value 2.6 in the above regression model represents: A. the value of SALES, on average, when ADVERTISING is zero. B. the change in SALES following a unit decrease in ADVERTISING. C. a ratio of SALES over ADVERTISING as a percentage. D. the proportion of the variation in SALES explained by using ADVERTISING in the regression equation. E. a ratio of ADVERTISING over SALES as a percentage.
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: River Falls Hudson Ellsworth Prescott Rock Elm Stillwater Select one: a. 78.39% b. 88.54% O c. 161.39% O d. 100% City What percentage of the total variation in the candy bar sales is explained by prices? e. 48.19% Price ($) 1.30 1.60 1.80 2.00 2.40 2.90 Sales 100 90 90 40 38 32
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $110 million. Use a significance level of alpha equals 0.05 . Budget left parenthesis $ right parenthesis in Millions 42 24 119 69 77 48 121 62 5 63 129 21 12 154 4 Gross left parenthesis $ right parenthesis in Millions 117 16 112 66 111 111 104 90 62 104 219 27 20 288 42 The regression equation is ŷ = __ + __x. (Round to one decimal place as needed.) The linear coefficient of correlation r = ___ (Round to three decimal places as needed.) The critical value of r = ____ The best predicted gross for a move with a $110 million budget is _____.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.4 8 6.8 3.4 2.4 2.2 2.4 0.4 y 13.5 10.7 10.1 6.9 5.9 5.6 6.3 4.2 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.85x + 3.97. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 10.3 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 4.3 thousand automatic weapons? Answer= Answer = Round to 3 decimal places.
The following table shows the annual expenditures, in dollars, per customer unit for residential landline phone services and cellular phone services in the United States in the given year.† Year Landline Cell 2004 592 378 2006 542 524 2008 467 643 2010 401 760 Calculate the regression line for each type of service. (Let t be the time in years since 2004, L be the operating revenue of landline phone services and C be the expenditure of cellular services. Round your regression parameters to two decimal places.) L(t) = C(t) = Determine the expenditure level at which the two lines cross. Round your answer for the expenditure level to one decimal place. million dollars
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.8 8.2 6.9 3.9 2.3 2.4 2.6 0.6 y 14.2 11.2 10.2 7.5 5.9 6.4 6 4.3 x = thousands of automatic weaponsy = murders per 100,000 residents This data can be modeled by the equation y=0.88x+3.95. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7.2 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 1.6 thousand automatic weapons? Answer = Round to 3 decimal places.
The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGE; = Bo + B1 EDUC; + e: where WAGE; is the hourly wage of person i (i.e., any specific person) and EDUC; is the number of years of education for that same person. The residual e; encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAGE; = –11.1+6.2 EDUC; If the standard error of the estimate of B, is 1.34, then the true value of B1 lies between and As the number of observations in a data set grows, you would expect this range to in size.
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…
The table contains data on vehicle speed (h) and fuel consumption (lt / 100km) of 5 randomly selected vehicles. Estimate the average fuel consumption of a vehicle traveling at 45 km / h using the simple linear regression equation between vehicle speed and fuel consumption. Speed 55 60 65 70 75 Consumption 13 12 11 10 9 a. 15 b. 8 c. 7 d. 20
The table below show data that has been collected from different fields from various farms in a certain valley. The table contains the grams of Raspberries tested and the amount of their Vitamin C content in mg. Find a linear model that express Vitamin C content as a function of the weight of the Raspberries. grams 50 60 70 80 90 100 110 Vitamin C content in mg 9.3 14.3 18.9 23.5 27.5 31.7 36.9 A) Find the regression equation: y = places x + Round your answers to 3 decimal B) Answer the following questions using your un-rounded regression equation. If we test 140 grams of raspberries what is the expected Vitamin C content? nearest tenth) mg (round to the
Consider the following data on x = rainfall volume (m³) and y = runoff volume (m³) for a particular location. 7 12 14 17 23 30 40 49 55 67 72 83 96 112 127 4 10 13 15 15 25 27 46 38 46 53 70 82 99 103 Use the accompanying Minitab output to decide whether there is a useful linear relationship between rainfall and runoff. The regression equation is runoff = -2.00 + 0.841 rainfall Predictor Coef Stdev t-ratio Constant -2.000 2.194 -0.91 0.379 rainfall 0.84079 0.03369 24.96 0.000 s = 4.823 R-sg = 98.0% R-sq (adj) = 97.8% State the appropriate null and alternative hypotheses. O Ho: B1 = 0 H: B, 0 O Hoi Bq # 0 H: B1 = 0 Compute the test statistic value and find the P-value. (Round your test statistic to two decimal places and your P-value to three decimal places.) t = P-value = State the conclusion in the problem context. (Use a = 0.05.) O Reject H.. There is a useful linear relationship between runoff and rainfall at the 0.05 level. O Reject H.. There is not a useful linear relationship…
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $135 million. Use a significance level of alpha equals 0.05 . Budget left parenthesis $ right parenthesis in Millions 45 24 115 74 72 48 117 67 4 60 124 24 6 152 8 Gross left parenthesis $ right parenthesis in Millions 115 10 95 69 127 112 93 101 50 102 223 26 18 282 56 The regression equation is ŷ = __ + __x. (Round to one decimal place as needed.)