A life insurance company wishes to examine the relationship between the amount of life insurance held by a familyand family income. From a random sample of households, the company collected the accompanying data. The dataare in units of thousands of dollars. (See worksheet INSUR)Let12345678abсd3bсdabсdabсdbсdbсdabссdbсdсd10abсd11bсd12abCdThe numerator of the slope coefficient formula for the estimated regression equation is:265.202.84270.615.14276,137.90281,773.37The denominator of the slope coefficient formula for the estimated regression equation is:66,646.9868,007.1269,395.0270,811.259 The sum of squared explained deviations (SSR) is,a1,098,812.8b 1,121,237.61,144,120.01,167,469,4The vertical intercept of the estimated regression equation is-8.8241-7.6731-6.6723-5.80203.4514.0604.7775.620The estimated regression equation predicts that for each additional $1,000 income life insurance policy will riseby($1,000)239.274249.243259.629The income of one policy holder is 58 ($1000). The predicted life insurance policy is229.703y=x=7.5078.3419.26810.297The observed policy amount for the policy holder in previous question is 240 ($1,000).The prediction error isThe sum of squared errors (SSE) is,28,567.029,757.330,997.232,288.7INSURINCOMEThe sum of squared total (SST) is,1,150,994.91,174,484.61,198,453.61,222,911.9The sample data provides that0.97410.93670.90070.8660The observed insurance policy deviates from the predicted policy, on average, by25.99026.79427.62328.4770.07720.08580.09530.1059($1,000)($1,000)fraction variations in the life insurance policy is explained by income.For the purpose of statistical inference for the slope parameter, the standard error of the slope coefficient, se(b,),is INSUR INCOME911182532474012911692067091382464434391913876361751401452404124352577354224602003916891092522353862931951791451462354354472929636811668354016440571071015197169464239581111065817111210554110170286462109694546414059105108

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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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Listed below are the heights (cm) of winning presidential candidates and their main opponents from several recent presidential elections. Find the regression equation, letting president be the predictor (x) variable. Find the best predicted height of an opponent given that the president had a height of 188 cm. How close is the result to the actual opponent height of 175 cm? President Opponent 178 177 188 188 191 175 185 183 188 180 183 188 173 169 173 177 185 175 The regression equation is y= 223 + (-0.245) x. (Round the y-intercept to the nearest integer as needed. Round the slope to three decimal places as needed.) The best predicted height of an opponent given that the president had a height of 188 cm is (Round to one decimal place as needed.) cm.
Listed below are the heights (cm) of winning presidential candidates and their main opponents from several recent presidential elections. Find the regression equation, letting president be the predictor (x) variable. Find the best predicted height of an opponent given that the president had a height of 188 cm. How close is the result to the actual opponent height of 175 cm? President 178 177 188 188 191 175 185 183 188 Opponent 180 183 188 173 169 173 177 185 175 The regression equation is y + Ox. (Round the y-intercept to the nearest integer as needed. Round the slope to three decimal places as needed.)
Listed below are the heights (cm) of winning presidential candidates and their main opponents from several recent presidential elections. Find the regression equation, letting president be the predictor (x) variable. Find the best predicted height of an opponent given that the president had a height of 177 cm. How close is the result to the actual opponent height of 183 cm? President Opponent 191 188 175 192 185 188 177 183 188 169 188 173 180 177 175 183 185 173 A The regression equation is y= 203 + ( -.137) x. (Round the y-intercept to the nearest integer as needed. Round the slope to three decimal places as needed.) The best predicted height of an opponent given that the president had a height of 177 cm is cm. (Round to one decimal place as needed.)
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Use the scatterplot of Vehicle Registrations below to answer the questions Vehicle Registrations in the United States, 1925- 2011 Vehicles millions 300 y = 3.0161x - 5819.5 R? = 0.9695 250 200 150 100 50 1920 -50 1940 1960 1980 2000 2020 Year Write a sentence explaining the value of the slope for this regression line. For every increase in year, the number of vehicle registrations in the US increases by 3.0161 million. For every increase in year, the number of vehicle registrations in the US increases by 5819.5. For every increase in vehicle registrations in the US, the number of years increases by 5819.5. For every increase in vehicle registrations in the US, the number of years increases by 3.0161 million. Registrations (in millions)
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A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage industry and so collects monthly data for 25 firms. He estimates the model: Sales = Bo + B1 Advertising + ɛ. The following table shows a portion of the regression results. Coefficients Standard Error t-stat p-value Intercept 41.1 13.88 2.961 0.0046 Advertising 2.72 1.7 -1.6 0.0586 When testing whether the slope coefficient differs from 3, the value of the test statistic is Multiple Choice t23 = 1.809 t23 = 0.165 t23 = -0.165 t23 =-1.809

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