An assumption of linear regression is that the relationship between x and y can be modeled by a straight line Question 2 options: True False
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An assumption of linear regression is that the relationship between x and y can be modeled by a straight line
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- Practice Question for Simple Linear Regression: 1. The following data represent the number of flash drives sold per day at a local computer shop and their prices. Price (x) $34 Units Sold (y) TT 3 36 4 32 6 35 5 Develop a least-squares regression line and explain what the slope of the line indicates. What is the coefficient of determination?A statistical program is recommended. The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly Television Gross Newspaper Advertising Advertising ($1,000s) ($1,000s) Revenue ($1,000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 1 (a) Develop an estimated regression equation with the amount of television advertising as the independent variable. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s and y represent the weekly gross revenue in $1,000s.) y = 88.64 + 1.60x1 X (b) Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s, x₂ represent the amount of…Which of the following is true of a linear regression line? a. Located as close as possible to all the points of a scatter chart. B. Is defined by an equation having 2 parameters: the slope and the intercept c. Provides an approximate relationship between the values of two parameters d. All of the above
- 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…At a large state university, the Statistics department is interested in tracking the progress of its students from entry until graduation. In this example: X represents a student’s final numeric grade (out of 100) in an introductory statistics course Y represents a student’s final numeric grade (out of 100) in an upper-level statistics course The least-squares regression equation for this relationship is: Y = 5.20 + 0.93X What is the slope of the regression line? Provide a numeric value as shown in the equation.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.)
- Which of the multivariate regression parameters listed below would be best interpreted as: the proportion of variation in the dependent variable explain by all the independent variables in the model. a b1 X1 R2Which of the following statements best describes linear regression? A measure of the degree of dependence between two variables. A statistical technique using pairs of data and identifying a line which ‘best fits’ all of the pairs of observations. A measure of the proportion of changes in the dependent variable which can be explained by a change in the independent variable. Shows individual costs against their associated activity levels but makes no attempt to identify a linear relationship between them.
