Find the multiple regression equation with weight as the response variable and the dummy variable of sex and the variable of age as the explanatory variables. The accompanying table provides data for the sex, age, and weight of bears. For sex, let 0 represent female and let 1 represent male. Letting the response (y) variable represent weight, use thedummy variable of sex and the variable of age and to find the multiple regression equation. Use the equation to find the predicted weight of a bear with the characteristics given below. Does sexappear to have much of an effect on the weight of a bear?a. Female bear that is 24 months of ageb. Male bear that is 24 months of ageClick the icon to view the bear measurement data.Find the multiple regression equation with weight as the responseWeight= +Sex+Age(Round to one decimal place as needed.)Bear Measurement DataSex (1=M)11110011001101011011Oo.00Age (Months)2256831151031015453555370* *222*NES8442929486241785682Weight803394143531612292653522131463333815017097169202201214321231250-X

The accompanying table provides data for the sex, age, and weight of bears. For sex, let 0 represent female and let 1 represent male. Letting the response (y) variable represent weight, use dummy variable of sex and the variable of age and to find the multiple regression equation. Use the equation to find the predicted weight of a bear with the characteristics given below. Does s appear to have much of an effect on the weight of a bear? a. Female bear that is 24 months of age b. Male bear that is 24 months of age Click the icon to view the bear measurement data. Find the multiple regression equation with weight as the response Weight = + ( Sex + Age (Round to one decimal place as needed.) Bear Measurement Data Sex (1=M) 1 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 0 1 1 Age (Months) 22 56 83 115 103 ខ្ល ៖ z ឌ 2 × ៥ ឌ ន ន ៖ “ ៩ ៩ ម 101 54 53 55 53 70 8 44 29 22 29 48 6 24 178 гр Weight 80 339 414 353 161 229 265 352 213 146 333 38 150 170 97 169 202 20 121 432 400 X

The accompanying table provides data for the sex, age, and weight of bears. For sex, let 0 represent female and let 1 represent male. Letting the response (y) variable represent weight, use dummy variable of sex and the variable of age and to find the multiple regression equation. Use the equation to find the predicted weight of a bear with the characteristics given below. Does s appear to have much of an effect on the weight of a bear? a. Female bear that is 24 months of age b. Male bear that is 24 months of age Click the icon to view the bear measurement data. Find the multiple regression equation with weight as the response Weight = + ( Sex + Age (Round to one decimal place as needed.) Bear Measurement Data Sex (1=M) 1 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 0 1 1 Age (Months) 22 56 83 115 103 ខ្ល ៖ z ឌ 2 × ៥ ឌ ន ន ៖ “ ៩ ៩ ម 101 54 53 55 53 70 8 44 29 22 29 48 6 24 178 гр Weight 80 339 414 353 161 229 265 352 213 146 333 38 150 170 97 169 202 20 121 432 400 X

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

a) what is the regression equation? b) what is the predicted weight of the bear? c) is the result close to the actual weight of 528 pounds?
Use the given data to find the best predicted value of the response variable. Use a significance level of 0.05The regression equation relating attitude rating (x) and job performance rating (y) for the employees of a company is y=11.3+1.24x. Ten pairs of data were used to obtain the equation. The same data yield r=0.852 and y¯=80.14. What is the best predicted job performance rating for a person whose attitude rating is 72?
Last years Data Management class decided to see if there was a relationship between the score (out of 10) a student got on the two-variable stats quiz, and their score (out of 30) on the unit test. Use the given data to Quiz Score Test Score 6 8 10 9 10 20 26 29 26 30 a) Calculate the correlation coefficient. b) Perform a linear regression.
Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable. Regression Statistics Multiple R 0.963 R Square Adjusted R Square Standard Error Observations 20 ANOVA df SS MS F Significance F Regression 24686354.49 6171589 47.8 2.3289E-08 Residual 129243 Total 26625000…
Why was the variable “# of customer service representatives” dropped from the model? Write the regression equation. Sales of men’s clothing (predicted) = Are the regression coefficients significantly different from 0? If one mails 10,000 catalogs and has 15 phone lines open, what would the predicted sales of men’s clothing be? How would you interpret the regression coefficient for number of catalogs mailed? What is the final R2 of the model? How would you interpret this? Which of the two independent variables is the most important predictor of the dependent variable? Why?
The Conde Nast Traveler Gold List for 2012 provided rating for the top 20 small cruise ships. The data from annual Readers’ Choice Survey are the overall scores(Y) each ship received based on several criteria, including Itineraries/Schedule (X1), Shore Excursions(X2), and Food/Dinning(X3). The estimated regression equation to predict the overall scores is Y= 35.6184+0.1105 X1+0.2445 X2+0.2474 X3. Part of the regression results is shown below. Coefficients Standard Error Intercept 35.6184 13.2308 Itineraries/Schedule(X1) 0.1105 0.1297 Shore Excursions(X2) 0.2445 0.0434 Food/Dinning(X3) 0.2474 0.0621 Use the T test to determine whether or not the coefficient of X1 is significant. Use Level of significance=.05? Be sure to state null and alternative hypotheses.…
There are 6 parts for this Question. Please help
A researcher plans to study the causal effect of police on crime using data from a random sample of U.K. counties. He plans to regress the county’s crime rate on the (per capita) size of the county’s police force. Explain why this regression is likely to suffer from omitted variable bias. Which variable would you add to the regression to controlfor important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?
The adjusted R-squared accounts for the amount of variance explained while also adjusting for the number of independent variables in the linear regression equation. True or False
Give an example of how a nonrepresentative sample can severely affect a regression analysis.
Which statement is not correct for multiple regression model? When we interpret this categorical variable, we would say the change we observe when one switches from the reference category to the specific category. The reference category will not be displayed in our model results. If we include a categorical variable with more than two values (e.g., religion) as an independent variable, we want to include a dummy variable for all categories except for two. We can include more than two categorical variables in the model. The value of the independent variable not included in the model is called "reference category."
If you have 2 datasets on which you have conducted a regression analysis, how would you determine whether you can combine them into one dataset?