2.51.50.56.101214161820Percentage of drivers under age 21Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed withperforming linear regression analysis. The results are as follows:TABLE. LINEAR REGRESSION OUTPUTFOR U.S. DEPARTMENT OF TRANSPORTATION PROBLEMCoefficientsStandard Errort Statisticp-valueIntercept-1.59740.3717-4.29790.0001Percent Under 210.28710.02949.76710.0000What is the predicted value for accidents per 1000 licenses for a city that has 13% of its licensed drivers under age 21, according to the estimated regressionequation?O 2.962O 2.135O 2.634O 1.142......00Fatal accidents per 100 U.S. Department of TransportationAs an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companiesis the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which itanalyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sampleof 42 cities.Your first step in the analysis is to construct a scatterplot of the data.FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEMU.S. Department of TransportationThe Relationship Between Fatal Accident Frequency and Driver Age4.54.3.53.2.51.50.50.6.8101214161820Percentage of drivers under age 21pon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed witherforming linear regression analysis. The results are as follows:2.1.Fatal accidents per 1000 licenses

Answered: Image /qna-images/answer/1581ead1-a7a2-4ded-b911-92656b91907a.jpgOption 2 is correct

Answered: What is the predicted value for…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Assuming the lowest decile of men receive an average of $800/month in Social Security, while the highest decile receive $2200/month in Social Security how much does each receive (on average) during their years in retirement. Use the chart called: Predictive Years of Receiving Social Security Benefits. Also use the born in 1940 data.
A student is interested in knowing if there is a relationship between the age of a child and the amount of time spent on the computer. He asks 7 people their ages and the number of hours they spend on the computer. Is there a relationship between age and the amount of time spent on computers? Test at a = .05 level of significance. Age of child (yrs) Time on computer (hrs) 11 17 4. 10 4. 1 13 8.
Which of the following will help to mitigate confounding? A. Multiple regression modelB. RandomizationC. StratificationD. All of the above
1) Sara believes that as she increases her walking speed, her pulse rate will increase. Sara records her pulse rate, in beats per minute (bpm), while walking at each of seven different speeds, in miles per hour (mph). Regression Analysis: Pulse Versus Speed Coef 63.457 SE Coef 2.387 0.8192 Predictor 26.58 19.88 Constant 0.000 Speed 16.2809 0.000 S= 3.087 R-Sq = 98.7% R-Sq (adi) = 98.5% a) Is there evidence at the 1% level to show a positive linear relationship between walking speed and pulse rate? Assume conditions for inference are satisfied. b) Interpret the p-value in context. c) Construct an interval to support the conclusion to part a. Make sure to briefly explain how it supports the conclusion. d) Interpret the interval.
Use the summary statistics to calculate the regression line for this data. Explain each coefficient. What is the total amount of weight would you expect a weightlifter with a body weight of 84kg to lift? At the 5% significance level, is there evidence that the body weight of the athlete is a useful predictor for the total amount an athlete can lift? Assume the assumptions can be verified.
Suppose the following data were collected from a sample of 15 CEOS relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARY; bo + B¡EXPERIENCE; + 62SERVICE; + B3INDUSTRIAL; + e;. Is there enough evidence to support the claim that on average, CEOS in the service sector have lower salaries than CEOS in the financial sector at the 0.01 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Copy Data CEO Salaries Service (1 if service sector, 0 Industrial (1 if industrial sector, 0 Financial (1 if financial sector, 0 Salary Experience otherwise) otherwise) otherwise) 195700 25 1 165600 32 1 197943 22 1 141650 201450 24 1 133250 1 141063 5 1 130012 3 1 184775 21 1 170637 29 1
A regression model to predict Y, the state burglary rate per 100,000 people, used the following four state predictors: X1 = median age, X2 = number of bankruptcies per 1,000 population, X3 = federal expenditures per capita (a leading predictor), and X4 = high school graduation percentage. Click here for the Excel Data File (a) Using the sample size of 45 people, calculate the tcalc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your t-values to 3 decimal places and p- values to 4 decimal places.) Predictor Intercept AgeMed Coefficient SE tcalc p-value 4,641.0430 798.0634 -28.8630 12.4684 Bankrupt 20.1604 12.1079 FedSpend HSGrad% -0.0181 0.0181 -30.3196 7.1136 (b-1) What is the critical value of Student's tin Appendix D for a two-tailed test at a = .01? (Round your answer to 3 decimal places.) -value =
In simple linear regression, a correlation coefficient value of 86.5342 indicates which of the following? a. There is a very strong positive linear relationship between X and Y. b. There is a moderate positive linear relationship between X and Y. c. There is a weak positive linear relationship between X and Y. d. There is a weak negative linear relationship between X and Y. e. There is a moderate negative linear relationship between X and Y. f. There is a very strong negative linear relationship between X and Y. g. There is a mathematical error in the computations.
A manager uses the SLR model to study the relationship between training hours (X) and a job efficiency index (Y). From a sample of 40 employees, she finds: bo = 13, b₁ = 2, SSR = 94.3, and SSE = 13.9 What percentage of variation in job efficiency index values is accounted for by the assumed relationship with training hours? (Round to the nearest percent and do not include the % sign in your answer.)
In order to examine the relationship between the selling price of a used car and its age, an analyst uses data from 20 recent transactions and estimates Price = Be + B1Age+ e. A portion of the regression results is shown in the accompanying table. Standard Error 734.41 p-Value 1.51E-16 2.02E-08 -1,203.251 126.91 a. Specify the competing hypotheses in order to determine whether the selling price of a used car and its age are linearly related. Intercept Age Coefficients 21,258.96 Test statistic 0; versus HA -9.481 t Stat 28.947 b. Calculate the value of the test statistic. Note: Negative value should be indicated by a minus sign. Round your answer to 3 decimal places. 10
A researcher has developed a regression model from fourteen pairs of data points. He wants to test to determine if the slope is significantly different from zero. He uses a two-tailed test and a = 0.01. The critical tablet value is 2.718 3.012 2.650 O 3.055 O 2.168
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What is the predicted value for accidents per 1000 licenses for a city that has 13% of its licensed drivers under age 21, according to the estimated regression equation? O2.962 O 2.135 O 2.634 O 1.142