Find the required linear model using least-squares regression.The following table shows the number of operating federal credit unions in a certain country for several years.20112012201320144497432341563976YearNumber of federal creditunions(a) The linear model for these data is y =(Round to the nearest tenth as needed.)(a) Find a linear model for these data with x = 11 corresponding to the year 2011.(b) Assuming the trend continues, estimate the number of federal credit unions in the year 2018.X +2015(b) The estimated number of credit unions for the year 2018 is(Round to the nearest integer as needed.)3807

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Answered: Find the required linear model using…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers): A.What would be the typical linear regression equation for the number of sales? B.Make the sales forecast for period 15 of next year. C. Make the sales forecast for period 17 of next year. D. What is the standard error for the data?
How do you figure this out using conjoint analysis in a linear regression? Determine how various attributes impact the purchase of a car. There are four attributes, each with three levels: Brand: Ford = 1, Chrysler = 2, GM = 3 MPG: 15 MPG = 1, 20 MPG = 2, 25 MPG = 3 Horsepower (HP): 100 HP = 1, 150 HP = 2, 200 HP = 3 Price: $18,000 = 1, $21,000 = 2, $24,000 = 3 The nine product profiles ranked in the data file were evaluated by a consumer. (a) For this market segment, rank the product attributes from most important to least important. Below is the data provided. Brand MPG Power Price Rank 1 1 1 1 4 1 2 2 3 7 1 3 3 2 3 2 1 2 2 6 2 2 3 1 2 2 3 1 3 9 3 1 3 3 8 3 2 1 2 5 3 3 2 1 1
Report the equation of the regression line and interpret it in the context of the problem
. Extent of Arctic Sea Ice The National Snow and Ice Data Center monitors the amount of ice in the Arctic year round. The table below gives approximate values for the sea ice extent in millions of square kilometers from 1986 to 2012, in two-year intervals. (a) Make a scatter plot of the data. (b) Find and graph the regression line. (c) Use the linear model in part (b) to estimate the ice extent in the year 2016. Ice extent Ice extent Year (million km?) Year (million km?) 1986 7.5 2000 6.3 1988 7.5 2002 6.0 1990 6.2 2004 6.0 1992 7.5 2006 5.9 1994 7.2 2008 4.7 1996 7.9 2010 4.9 1998 6.6 2012 3.6 Source: National Snow and Ice Data Center
Infant Mortality Rate. A social scientist wants to predict the infant mortality rate in cities in the United States. a. Suggest five predictor variables (characteristics associated with a city) that might be useful in predicting infant mortality rate. b. Write the multiple linear regression model for the conditional mean of the response variable, infant mortality rate.
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 $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…
Linear regression is a highly effective data analysis method that accurately estimates the value of unknown data using related and known data. This is achieved using a linear equation that accurately models the quantitative relationship between the dependent (unknown) and independent (known) variables. In other words, by looking at how one variable affects another, this method can accurately forecast the value of the dependent variable. It involves choosing the variable to forecast and using another variable to make informed predictions about its value. In experiments, the independent variable is the cause, and its value remains constant while other variables have been modified. On the other hand, the dependent variable is the effect, and changes influence its value in the independent variable. As I began my business, I struggled with determining the appropriate pricing for my products. It was important that the prices were reasonable while allowing for a profit. The pricing had to…
he table shows the minimum wage rates for the United States during different years. Year 1978 1979 1980 1990 1991 1996 1997 2007 2008 2009 Minimum hourly wage ($) 2.65 2.90 3.35 3.80 4.25 4.75 5.15 5.85 6.55 7.25 Write the least squares regression equation that models the data. Let x = time in years since 1900 and let y = minimum hourly wage. Use the equation to estimate the minimum hourly wage of a U.S. worker in 2025. Show your work. Answer:

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