Model assumptions for a regression model

Reference no: EM1322614

Fitting of multiple linear regression equation.

The revenues gained from sales of books from an e-Book supplier, the book price and advertising development costs were recorded for a randomly selected week. Treating sales revenue as the dependent variable with price (X₁) and advertising development costs (X₂) as the predictor variables, a multiple regression model as fitted to these data. An interaction term between X1 and X2 was also included.

a) Expalin why do you believe that states be treated as the dependent variable, with price and advertising development costs as predictor variables.

b) Expalin what the interaction term (X₁ * X₂) represents in this model.

c) The output from regression sales revenue onto price (X₁), advertising development costs (X₂) and an interaction term (X₁ * X₂) are given in table. The values for the t-statistic for the intercept term (t₁), the standard error for price (SE₁), P-value for advertising developmemt costs (P₂) and net regression coefficient for the interaction term (β_1*2) are absent. Calculate these.

d) Provide a list of the model assumptions for a regression model.

e) Assuming that all model assumptions are satisfied. Provide your model for this regression based on the P-values present in table. You must explain what each term represents.

Predictors	Regression Coefficient	Standard error	t-Statictic	P - value
Intercept	87.5	175.9	t₁	0.630
X₁	5.534	SE₁	3.09	0.011
X₂	5.479	1.606	3.41	P₂
X₁ * X₂	β_1*2	0.0158	0.19	0.852

Reference no: EM1322614

Questions Cloud

Provide a brief description of the main features : Provide a brief description of the main features of these data from the scatter plot and Describe why you would use performance as the predictor variable and performane related pay as the dependent variable in such a model.

Probability by normal approximation for data : What is the probability that the next 100 customers will spend an average of less than $56 on dinner?

Simulate the arrival of cars at the service station : Simulate the arrival of cars at the service station for 20 arrivals and compute the average time between stations and Simulate the arrival of the cars at the service station for 1 hour

Finding probability of a rating : If an applicant is randomly selected, find the probability of a rating that is between 200 and 275.

Model assumptions for a regression model : Fitting of multiple linear regression equation - Give a list of the model assumptions for a regression model.

Find the indicated probability : The diameters of bolts produced by a certain machine are normally distributed with a mean of 0.30 Inches and a standard deviation of 0.01 inches.

Challenges on accurate data gathering : Describe methods you would use to analyze one of these three different markets: consumer, industrial or international markets. What challenges would you face to ensure accurate data gathering in the market

Evaluate the average queue length : Random number generation based on discrete distribution and evaluate the average queue length, waiting time, and percentage utilization for each system.

Probability of tossing die-using normal approximation : Use the normal distribution to approximate the desired probability. Find the probability that in 200 tosses of a fair die, we will obtain at exactly 30 faces.

User Account

All Pages