A study was conducted too see if a correlation exist between a person's annual salary and the car that they drive. ( please show me how to solve it because I have to show work on paper. Make it 8th level please) Question 11 of 16What your Car says about your Salary$140,000$120,000$100,000(193,000,95,000)$80,000$60,000$40,000$20,000SOSO$50,000$100,000$150,000S200,000$250,000Annual SalaryA scatterplot was created from the collected data, with a linear regression model of = 0.52r+3000. Use this equationto find the residual value of the circled point shown on the graph.The residual value of this point is Select Choice vDone and RNext QuestionPitvacy and Cookies Terms of Use Minimum Requirements Platform StatusC2022 McGraw-Hill Education All Rights ReservedPurchase Price of Car Annual SalaryA scatterplot was created from the collected data, with a linear regression moto find the residual value of the circled point shown on the graph.The residual value of this point is Select ChoiceSelect Choice8,360100,360-8,360Next Question-100,360Privacy and Cookles Terms ot2022 McGraw-Hill Education All Rights Reserved.

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Answered: A scatterplot was created from the…

A scatterplot was created from the collected data, with a linear regression model of y = 0.52r+ 3000. Use this equation to find the residual value of the circled point shown on the graph.

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter2: Matrices

Section2.CR: Review Exercises

Problem 90CR

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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.
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?
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
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.
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 9 Assume a regression analysis yields a regression line with the value Y=$120,000 + $0.58X, where Y equals plant labor costs and X equals dollars of production output. If the company plans to produce $2,400,000 of product during the upcoming month, it would project plant labor costs to be: a. $324,000 b. $120,000 c. $204,000 d. $2,400,000
Question 4 The following table displays the mathematics test scores for a random sample of college students, along with their final SY16C grades. Fit the regression line y = a+bx to the data and interpret the results. Use the regression equation to determine the SY16C grade for a college student who scored 60 on their achievement test. What would their SY16C grade be? Mathematics test (x) SY16C grades (y) 1 39 65 2 43 78 3 21 52 4 64 82 5 57 92 6 47 89 7 28 73 8 75 98 9 34 56
question 26 What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman's weight? The time on the phone and weight for 8 women are shown in the table below. Time 54 88 82 61 39 40 84 83 Pounds 149 198 184 166 142 140 170 163 The equation of the linear regression line is: ˆyy^ = ?+ x (Please show your answers to 3 decimal places) Use the model to predict the weight of a woman who spends 50 minutes on the phone.Weight = ? (Please round your answer to the nearest whole number.) Interpret the slope of the regression line in the context of the question: For every additional minute women spend on the phone, they tend to weigh on averge 0.87 additional pounds. As x goes up, y goes up. The slope has no practical meaning since you cannot predict a women's weight. Interpret the y-intercept in the context of the question: The y-intercept has no practical meaning for this study. The average woman's weight is…
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 2 Suppose that you are assigned a task to investigate the relationship between selling price and valuation of plots sold by a local municipality. Data was obtained for a random sample of ten plots. Plot Selling price ($'000) Valuation 1 120 72 2 100 68 3 140 72 4 150 70 5 155 75 6 100 50 7 150 58 8 200 90 9 80 56 10 145 70 Required: a) Use the method of least squares and estimate the regression equation between selling price and valuation & Provide an interpretation for the slope coefficient? b) Use the estimated regression equation and make a prediction for a value of the dependent variable when the independent variable is N$ 85 000. c) Calculate and interpret: (i) the coefficient of determination? (ii) Calculate and interpret the correlation coefficient?
Do students with higher college grade point averages (GPAs) earn more than those graduates with lower GPAs?† Consider the following hypothetical college GPA and salary data (10 years after graduation). GPA Salary ($) 2.22 72,000 2.29 48,000 2.57 72,000 2.59 64,000 2.77 88,000 2.85 98,000 3.12 133,000 3.35 130,000 3.66 157,000 3.68 162,000 Use these data to develop an estimated regression equation that can be used to predict annual salary 10 years after graduation given college GPA. (Let x = GPA, and let y = salary (in $). Round your numerical values to the nearest integer.) ŷ = Find the value of the test statistic. (Round your answer to two decimal places.) = Find the p-value. (Round your answer to three decimal places.) p-value =
Question 2 The following table shows the mean weight in kilograms of members of a group of young children of various ages. Age (x years) 1.6 2.5 3.3 4.4 5.6 Weight ( y kg) 12 15 16 17 20 The relationship between the variables is modeled by the regression line with equation y=ax+by=ax+b