Use the cubic regression model above to answer this question.Predict the percentage of female high school graduates in 2023. Round to thenearest tenth (1 decimal place).%Question 6Use the cubic regression model above to answer this question.Predict instantaneous rate of change of the percentage of female high schoolgraduates in 2024. Round to the nearest hundredth (2 decimal places).% per yearNOTE: Your answer may be negative. College EnrollmentThe percentage of female high school graduates who enrolled in college isgiven in the table below.Year19701980199020002010College Enrollment Percentages forFemales25.530.338.3Find the cubic regression model45.650.5Let a represent time (in years) since 1970, and let y represent thecorresponding percentage of female high school graduates who enrolled incollege.y = F(x) = ax³ + bx² + cx+d.where a is rounded to 6 decimal places, b is rounded to 4 decimal places, c isrounded to 3 decimal places, and d is rounded to 1 decimal place.Use this cubic regression model to answer questions 5-6 below.

Given that: Year, X College Enrollment Percentages for Females, Y 1970 25.5 1980 30.3 1990…

Answered: College Enrollment The percentage of…

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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[Simple Linear Regression] How do you solve this?
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Annual high temperatures in a certain location have been tracked for several years. Let X represent the number of years after 2000 and Y the high temperature. Based on the data shown below, calculate the linear regression equation using technology (each constant to three decimal places). y 34.38 2. 35.56 3 37.64 4 36.22 38.8 6. 39.68 7. 40.66 8 38.84 9 39.32 10 40.3 11 43.78 12 43.16 13 43.24 14 43.62 Question Help: D Video Submit Question
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Annual high temperatures in a certain location have been tracked for several years. Let X represent the number of years after 2000 and Y the high temperature. Based on the data shown below, calculate the linear regression equation using technology (each constant to 2 decimal places). X y 3 35.01 4 37.78 5 37.35 6 36.72 7 40.09 8 40.36 9 40.83 10 41.3 11 40.57 12 42.64 13 44.21 14 42.58 15 46.05 16 45.12 The equation is ĝ ŷ = X + Interpret the slope O For each additional 33.75 years, the annual high temperature will increase by 1 degree on average. O For each additional year, the annual high temperature will increase by 33.75 degrees on average. For each additional year, the annual high temperature will increase by 0.74 degrees on average. O For each additional 0.74 years, the annual high temperature will increase by 1 degree on average. Interpret the y-intercept O In 2000, the temperature was about 33.75. O In 2003, the temperature was about 33.75. O In 2003, the temperature was about…
Annual high temperatures in a certain location have been tracked for several years. Let x represent the number of years after 2000 and y the high temperature. Based on the data shown below, calculate the linear regression equation using technology (each constant to three decimal places). X y 3 37.64 4 37.02 5 39.4 6 40.78 7 40.76 8 44.54 9 44.42 10 46.2 11 45.58 12 46.66 13 49.54 The equation is ŷ
The table below shows the number, in thousands, of vehicles parked in the central business district of a certain city on a typical Friday as a function of the hour of the day. Hour of the day Vehicles parked(thousands) 9 A.M. 6.2 11 A.M. 7.6 1 P.M. 7.6 3 P.M. 6.7 5 P.M. 4 (a) Use regression to find a quadratic model for the data. (Let V be the number of vehicles and t be the time in hours since midnight. Round the regression parameters to three decimal places.) V= (b) Express using functional notation the number of vehicles parked on a typical Friday at 2 P.M., and then estimate that value. (Round your answer to two decimal places.) V_____= _____ thousand
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…
The median household incomes in South Carolina from 2013 and 2017 are listed in the table below. (Source: “South Carolina Household Income.” Department of Numbers, https://www.deptofnumbers.com/income/south-carolina/) Year 2013 2014 2015 2016 2017 Median Household Income $46,548 $46,887 $48,876 $50,566 $50,570 Determine the linear regression model, I(t), where t = years since 2013 and I = Median Household Income. A. I(t) = 1172.3t + 46548 B. I(t) = 1172.3t + 46345 C. I(t) = 1172.3t D. I(t) = 1172.3t - 2E+06
At an online diamond trader site, the price for a diamond is dependent upon the weight of the diamond. The pricing information is given in the table below. x: Weight (in carats) 0.5 0.6 0.7 0.8 0.9 1.0 y: Price (in dollars) 2600 3000 3500 3950 4800 5700 a) Find the linear regression equation for this data where x is the weight of a diamond (in carats) and y is the corresponding price of the diamond (in $). The slope and y-intercept for the linear regression equation are as follows: slope = Blank 1. Fill in the blank, read surrounding text. y-intercept = Blank 2. Fill in the blank, read surrounding text. b) Use the linear regression equation to estimate the price of a diamond that weighs 0.65 carats. Blank 3. Fill in the blank, read surrounding text. dollars (Do not use the $ sign in the answer box.)
The table shows the yield (in bushels per acre) and the total production (in millions of bushels) for corn in a country for selected years since 1950. Let x represent years since 1900. Find a logarithmic regression model (y = a +b ln x) for the yield. Estimate the yield in 2023. Year 1950 1960 Yield 50 37 60 1970 1980 60 84 93 70 80 1990 90 116 2000 2010 100 136 110 157 The regression model is y =+( In x. (Round to one decimal place as needed.) bushels/acre. The estimated yield in 2023 is (Round to the nearest integer.)
[Simple Linear Regression] How do you solve this?

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