MarketWeekly Gross Revenue ($100s)Television AdvertisingNewspaper AdvertisingMobile101.35.01.5Shreveport51.93.03.0Jackson74.84.01.5Birmingham126.24.34.3Little Rock137.83.64.0Biloxi101.43.52.3New Orleans237.85.08.4Baton Rouge219.66.95.8 Question 1: Use the data file A3Q1.xlsx to answer this question. Dixie Showtime Movie Theaters,Inc., owns and operates a chain of cinemas in several markets in the southern UnitedStates. The owners would like to estimate weekly gross revenue as a function of ad-vertising expenditures.a. Develop an estimated regression equation with the amount of television advertis-ing as the independent variable. Test for a significant relationship between televi-sion advertising and weekly gross revenue at the 0.05 level of significant. What isthe interpretation of this relationship?b. How much of the variation in the sample values of weekly gross revenue does themodel in part (a) explain?c. Develop an estimated regression equation with both television advertising andnewspaper advertising as the independent variables. Test whether each of theregression parameters Bo, B1, and 3, is equal to zero at a 0.05 level of significance.What are the correct interpretations of the estimated regression parameters? Arethese interpretations reasonable?d. How much of the variation in the sample values of weekly gross revenue does themodel in part (c) explain?

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Description: MarketWeekly Gross Revenue ($100s)Television AdvertisingNewspaper AdvertisingMobile101.35.01.5Shreveport51.93.03.0Jackson74.84.01.5Birmingham126.24.34.3Little Rock137.83.64.0Biloxi101.43.52.3New Orleans237.85.08.4Baton Rouge219.66.95.8 Question 1: Us

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Market Weekly Gross Revenue ($100s) Television Advertising Newspaper Advertising Mobile 101.3 5.0 1.5 Shreveport 51.9 3.0 3.0 Jackson 74.8 4.0 1.5 Birmingham 126.2 4.3 4.3 Little Rock 137.8 3.6 4.0 Biloxi 101.4 3.5 2.3 New Orleans 237.8 5.0 8.4 Baton Rouge 219.6 6.9 5.8

Market Weekly Gross Revenue ($100s) Television Advertising Newspaper Advertising Mobile 101.3 5.0 1.5 Shreveport 51.9 3.0 3.0 Jackson 74.8 4.0 1.5 Birmingham 126.2 4.3 4.3 Little Rock 137.8 3.6 4.0 Biloxi 101.4 3.5 2.3 New Orleans 237.8 5.0 8.4 Baton Rouge 219.6 6.9 5.8

Algebra and Trigonometry (MindTap Course List)

4th Edition

ISBN:9781305071742

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section1.FOM: Focus On Modeling: Fitting Lines To Data

Problem 10P

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
QUESTION 2 XXX Electric Illuminating Company is doing a survey on the relationship between electricity used in kilowatt-hours (thousand) and the number of rooms in a private single-family residence. A random sample of 10 homes was selected and the electricity consumption recorded as below. ii. Find a suitable linear regression equation ? = ? + ??. iii. Determine the number of kilowatt-hours (thousand) for an eleven-room residence.
Ch 13. 7: Refer to the Lincolnville School District bus data. First, add a variable to change the type of engine (diesel or gasoline) to a qualitative variable. If the engine type is diesel, then set the qualitative variable to 0. If the engine type is gasoline, then set the qualitative variable to 1. Develop a regression equation using statistical software with maintenance cost as the dependent variable and age, odometer miles, miles since last maintenance, and engine type as the independent variables.
Question 4: A company studied the productivity of their employees on a new information system. They were interested in if the age (X) of data entry operators influenced the number of completed entries made per hour (Y). If the regression equation is = 14.374 + 0.145x. The SD of age is = 14.04, and the SD of the number of completed entries made per hour is = 2.61. What is the correlation coefficient between age and productivity? How to interpret the correlation? How to interpret the slope? If a data entry operator is 40 years old, what is the predict productivity using the regression equation?
Question 4 - 6 refer to following variables: Dependent variable: Sentence Months Independent variable: Year of Education After calculating the regression slope of the two variables, explain what the slope refers to by selecting two correct answers (NO PATIAL CREDIT). Form: For one unit increase in [a], [b] is predicted to [c] by the slop. For one unit increase in Sentence Months, For one unit increase in Year of Education, Sentence Months is predicted to increase by the slop Sentence Months is predicted to decrease by the slop Year of Education is predicted to increase by the slop Year of Education is predicted to decrease by the slop
Question #6 Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 4 8 14 24 27 31 32 Temperature 55 37 20 −5 −27 −41 −57 a. Find the explained variation. ______________ (Round to two decimal places as needed.) b. Find the unexplained variation. _______________ (Round to five decimal places as needed.) c. Find the indicated prediction interval. _____________°F < y < ____________ °F (Round to four decimal places as needed.)
Question 3. Calculate and interpret the regression line for the data.
The operations manager of a musical instrument distributor feels that the demand for Bass Drums may be related to the number of television appearances by the popular rick group Green Shades during the previous month. The manager has collected the data shown in the following table. Demand for Bass Drums 3 6 7 5 10 8 Green Shades TV appearances 3 4 7 6 8 5 Develop the linear regression equation to forecast. Forecast demand for Bass Drums when Green Shades’ TV appearances are 10. Compute MSE and standard deviation for Problem 8.
Consider the following scenario for Questions 6 through 9: The City of Bellmore’s police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs? If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
Question 5 Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue. The following is the descriptive statistics and regression output from Excel. Revenue People Income Competitors Price Mean 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0.142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample…

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Market	Weekly Gross Revenue ($100s)	Television Advertising	Newspaper Advertising
Mobile	101.3	5.0	1.5
Shreveport	51.9	3.0	3.0
Jackson	74.8	4.0	1.5
Birmingham	126.2	4.3	4.3
Little Rock	137.8	3.6	4.0
Biloxi	101.4	3.5	2.3
New Orleans	237.8	5.0	8.4
Baton Rouge	219.6	6.9	5.8