Which of the following is not a condition that needs to be assessed in multiple linear regression? o residuals are normally distributed o observations are independent of each other O residuals have constant variation o explanatory variables are linearly related with each other o high correlation among explanatory variables

Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose that someone wants to predict the cost of dinner at a restaurant in a city based on the Zagat food quality ratings. If 10 restaurants in a city are sampled and the regression output is given below, what can we conclude about the slope of food quality? Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0.   2)  The slope is 2.521 and therefore differs from 0.   3)  The slope significantly differs from 0.   4)  Not enough evidence was found to conclude the slope differs significantly from 0.   5)  The slope is equal to 0.

The data from the table below gives a regression that is a) reliable.   b) unreliable.   c) unable to determine the reliability.

The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year.  The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage   Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782     Total 2628.025 49       a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints   Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357   4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…

True or false: A proper line regression can be used to predict values of the response variable from absurd values of the explanatory variable.

Hi, please help with this assignment. Please show the work step by step as I am having trouble understanding how to compute them.

The fish in my pond have mean length 10 inches and mean weight 4 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 10 inches, then what should we predict for the weight of that fish using simple linear regression? 4 10 2.5 NOT ENOUGH INFORMATION GIVEN NONE OF THE OTHERS

What is the slope of the regression line (slope and intercept) Please put the answer in bold.

Do a linear regression between ‘Weight’ vs ‘Performance.’ Let ‘Performance’ be the dependent/response variable and ‘Weight’ be the independent/predictor variable.

Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Coefficients 1186 213.48 Coefficients - 130.46 294.11 Standard Error Does the sign of the coefficient for the variable safety rating make sense? 123.87 21.89 14.26 356.37 t Stat P-value 9.575 0.0000 9.752 0.0000 -9.149 0.0000 0.825 0.4128