You have to examine the relationship between the age and price for used cars sold in the last year by a car dealership company to build a predictive linear regression model. The age of the car is the independent variable for your model. Here is the table of the data: Car Age in Years Price in Dollars 4 6,300 4 5,800 5 5,700 5 4,500 7 4,500 7 4,200 8 4,100 9 3,100 10 2,100 11 2,500 12 2,200 What would we expect for the price of an 8.5 year-old car?

You have to examine the relationship between the age and price for used cars sold in the last year by a car dealership company to build a predictive linear regression model. The age of the car is the independent variable for your model. Here is the table of the data: Car Age in Years Price in Dollars 4 6,300 4 5,800 5 5,700 5 4,500 7 4,500 7 4,200 8 4,100 9 3,100 10 2,100 11 2,500 12 2,200 What would we expect for the price of an 8.5 year-old car?

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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?
Problem 2 The following printout shows the results of a simple linear regression model that predicts monthly sales (in thousands of dollars) based on how much money was spent on advertising (in thousands of dollars) during a particular month for 15 stores of a retail chain. a) Is there a statistically significant relationship between money spent on advertising and sales? Test at the 5% level of significance and explain your approach (including hypotheses). b) Somebody claims that every additional $1,000 in advertising will increase sales by more than $2,000 in the population. Can you find support for this claim given the results of your analysis? Test at the 5% level of significance and explain your approach (including hypotheses). How is this test different from the one in part a)? c) Find a 95% confidence interval for the change in sales given a $1,000 increase in the amount of money spent on advertising. How does this confidence interval relate to your answer to part a)?
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.
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.
Question 3. Calculate and interpret the regression line for the data.
Question 16 Regression analysis was applied between sales (in $1000s) and advertising (in $100s), and the following regression function was obtained. = 500 + 4x Based on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is _____. $505,000 $900 $40,500 $900,000
Questions 1-30 refer to the following scenario: A company reports bi-annual (twice a year) sales data. The sales data for the last three years is shown in below Table. Why would it be a bad idea to use the linear regression line to make forecasts when looking at the data? a The linear trend line does not capture the fact that, on average, sales go up. b The linear trend line does not capture the seasonality of the data. c Both a. and b. are correct. d None of the above. Decomposition forecasting decomposes data into which two factors? a Slope and intercept b Trend and seasonality c Past and future data d Decom and position In decomposition forecasting, the calculated seasonal index for the first bi-annual period is a 0.71 b 1.29 c 0.89 d 1.41 In decomposition forecasting, the calculated seasonal index for the second bi-annual period is a 0.89 b 0.71 c 1.41 d 1.29 Using only the regression line, the…
Question 4b. (b) Finally, the researcher is interested in examining the regression model for knowledge, attitudeand practices towards the COFLU-20. The following model was developed to forecastindividual practices towards COFLU-20 using knowledge and attitude scores.P = α + β K + δ Awhere P = Practice towards COFLU-20 scoreK = Knowledge towards COFLU-20 scoreA = Attitude towards COFLU-20 scoreThe data are processed using MINITAB and the output in Exhibit 1 below was obtained:Exhibit 1The regression equation is *************Predictor Coef SE t-ratio PConstant 4.755 0.462 10.282 0Knowledge 0.8 0.039 2.055 0.041Attitude 0.024 0.6 0.393 0.695R-sq = 81.5%Analysis of VarianceSOURCE DF SS MS F PRegression 2 38.06 19.03 2.284 0.104Error 297 2474.887 8.333Total 299 2512.947(i) Identify the dependent variable(s) and the independent variable(s).(ii) Is δ significant?Show proof of the testing process used to arrive at your decision.State what this means in terms of attitude and practices. (iii)…
Question 4 The following table shows information related to the number of salesman and sales made in October 2020 for Jubilee Berhad: Salesman Sales 1 200 2 260 3 700 4 840 5 1,000 6 1,100 Required: Forecast the total sales if the company has 10 salesman. Determine the correlation coefficient between the number of salesman and total sales if the company maintain the existing number of salesman. The sales trend can be described by the linear regression equation y = 780 + 4x, where x is the month number (with January 2013 as month 0) and y is sales in RM'000. The average seasonal variation for March is 106%. Forecast the sales for March 2015 (in thousand RM). Badrol anticipates that a 90% learning curve will apply to the production of a new item. The first item will cost RM2,500.00 in materials, and will take 500 labour hours. The cost per hour for labour and variable overhead is RM15.00. Calculate the total cost…
Question 5 - what is the y-intercept of the equation of the regression line y = 36.11695 + 7.20508x A. 7.20508 B. 36.11695 C. Y D. X Question 6 - what is the slop of the equation of the regression line y = 36.11695 + 7.20508x? A. 7.20508 B. 36.11695 C . Y D . X
given the information below: Row Education Income in in Years 2015 Dollars 1 7 22587 2 10 28305 3 12 40196 4 13 49483 5 14 54483 6 16 78073 7 18 99540 8 19 155646 9 21 125310 1. Estimate the regression equation Income = a + b(Education). 2. What is the predicted increase in Income for a one-year increase in Education? 3. What do you predict Income to be for a person who has 17 years of education? 4. How much of the variation in Income is explained (or accounted for) by Education?