You have obtained data on test scores and student-teacher ratios in region A and region B. Region B, on average, has lower student-teacher ratios than region A. You decide to run the following regression  Yi=B0+B1X1i+B1X2i+B3X3i+u  where X1 is the class size in region A, X2 is the difference in class size between region A and B, and X3 is the class size in region B. Your regression package shows a message indicating that it cannot estimate the above equation. What is the problem here and how can it be fixed?

Use the p-value criterion to find the best model for predicting the number of points scored per game by football teams using the accompanying National Football League Data. Does the model make logical sense? Click the icon to view the National Football Leaque Data. Determine the best multiple regression model. Let X, represent Rushing Yards, let X, represent Passing Yards, let X, represent Penalties, let X, represent Interceptions, and let Xg represent Fumbles. Enter the terms of the equation so that the Xy-values are in ascending numeral order by base. Select the correct choice below and fill in the answer boxes within your choice. (Type an integer or decimal rounded to three decimal places as needed.) O A. Points/Game = O B. Points/Game = Oc. Points/Game = OD. Points/Game = + ( DX Points/Game = O E.

What does it mean for a regression line to be the "best-fit" line.

The​ least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Interpret the slope.

To determine the effectiveness of group study sessions, a college instructor gathers data on hours of attendance and exam scores for students in the class. Which variable, hours of attendance or exam scores, would be the response variable for a least-squares regression equation? is it hours of attendance or exam scores?

Describe a hypothetical study for which multiple regression with more than two predictor variables would be an appropriate analysis. Your description should include one dependent variable and three or more predictors.

A real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y = sales price (in thousands of dollars) x₁ = total floor area (in square feet) x₂ = number of bedrooms x3 distance to nearest high school (in miles) = The estimated model is as follows. =76+0.098x₁ +16x₂ - 8x3 Answer the questions below for the interpretation of the coefficient of X₂ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each additional bedroom in a house? dollars (b) Is this change an increase or a decrease? O increase O decrease X

A survey of high school students was done to examine whether students had ever driven a car after consuming a substantial amount of alcohol (1=yes, 0=no). Data was collected on their sex (male/female), race (White/non-White), and grade level (9,10,11,12). Researchers realized that the impact of race on consuming alcohol before driving might vary by grade level and decided to fit the following model. Variable Coding = 1 if Intercept   Sex () Female Race () Black Grade level ( 9th grade 10th grade 11th grade [Reference = 12th grade]   Attached is the logistic model  1. Compute the OR of drinking before driving for students who self-reported as Black versus non-Black in the 9th grade, adjusting for gender. 2. Compute the OR of drinking before driving for students who self-reported as Black versus non-Black in the 12th grade, adjusting for gender. 3. Compute the OR of drinking before driving for someone in the 9th grade versus 12th grade for a student who…

A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y = 74.80 + 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with O rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…

What are the assumptions of multiple linear regressions only?