Question

In: Statistics and Probability

4. How should a researcher specify a multiple linear regression model? In answering this question, you...

4. How should a researcher specify a multiple linear regression model? In answering this question, you should be sure to draw on your overall knowledge of econometrics in order to consider not only how a researcher should specify an initial model, but also how a researcher should assess the performance of an initial model and thereby iterate towards a final model.

Solutions

Expert Solution

In predictive analysis, multiple linear regression is used to describing data and to explain the relationship between one dependent variable and two or more independent variables. It consists of three stages: 1) analyzing the correlation and directionality of the data, 2) estimating the model, i.e., fitting the line, and 3) evaluating the validity and usefulness of the model.

Firstly, it might be used to identify the strength of the effect that the independent variables have on a dependent variable.  

Secondly, it can be used to forecast the effects or impacts of changes. That is to say, multiple linear regression analysis helps us to understand how much the dependent variable will change when we change the independent variables.  

Thirdly, multiple linear regression analysis predicts trends and future values. The multiple linear regression analysis can be used to get point estimates.  

orchestra answered 1 year ago
Next > < Previous

Related Solutions

1. A multiple linear regression model should not be used if: A The variables are all...
1. A multiple linear regression model should not be used if: A The variables are all statistically significant. B The coefficient of determination R2 is large. C Both of the above. D Neither of the above. 2. Consider a multiple linear regression model where the output variable is a company's revenue for different months, and the purpose is to investigate how the revenue depends upon the company's advertising budget. The input variables can be time-lagged so that the first input...
When we estimate a linear multiple regression model (including a linear simple regression model), it appears...
When we estimate a linear multiple regression model (including a linear simple regression model), it appears that the calculation of the coefficient of determination, R2, for this model can be accomplished by using the squared sample correlation coefficient between the original values and the predicted values of the dependent variable of this model. Is this statement true? If yes, why? If not, why not? Please use either matrix algebra or algebra to support your reasoning.
Discuss the underlying assumptions of a simple linear regression model; multiple regression model; and polynomial regression.
Discuss the underlying assumptions of a simple linear regression model; multiple regression model; and polynomial regression.
Give an example of omitted variable bias in a multiple linear regression model. Explain how you...
Give an example of omitted variable bias in a multiple linear regression model. Explain how you would figure out the probable direction of the bias even without collecting data on this omitted variable. [3 marks]
The following is the estimation results for a multiple linear regression model: SUMMARY OUTPUT             Regression...
The following is the estimation results for a multiple linear regression model: SUMMARY OUTPUT             Regression Statistics R-Square                                                       0.558 Regression Standard Error (S)                  863.100 Observations                                               35                                Coeff        StdError          t-Stat    Intercept               1283.000    352.000           3.65    X1                             25.228        8.631                       X2                               0.861        0.372           Questions: Interpret each coefficient.
The following is the estimation results for a multiple linear regression model: SUMMARY OUTPUT             Regression...
The following is the estimation results for a multiple linear regression model: SUMMARY OUTPUT             Regression Statistics R-Square                                                       0.558 Regression Standard Error (S)                  863.100 Observations                                               35                                Coeff        StdError          t-Stat    Intercept               1283.000    352.000           3.65    X1                             25.228        8.631                       X2                               0.861        0.372           Question: 1. A. Write the fitted regression equation. B. Write the estimated intercepts and slopes, associated with their corresponding standard errors. C. Interpret each coefficient.
In a multiple linear regression model with 2 predictors (X1and X2),                               &n
In a multiple linear regression model with 2 predictors (X1and X2),                                TRUE     or     FALSE In a multiple linear regression model with 2 predictors (X1and X2), then SSR(X1)+SSR(X2|X1) = SSTO–SSE(X1,X2)   TRUE     or    FALSE In a multiple linear regression model with 2 predictors (X1and X2), if X1and X2are uncorrelated, SSR(X1) = SSR(X1|X2).       TRUE     or    FALSE In a multiple linear regression model with 2 predictors (X1and X2), SSR(X1) + SSR(X2|X1) = SSR(X2) + SSR(X1|X2).       TRUE     or    FALSE In simple linear regression, then (X’X)-1is  2x2.    TRUE    or     FALSE In simple linear regression, the hat-matrix is 2x2.    TRUE    or     FALSE
Question 4 A simple linear regression model was used in order to predict y, duration of...
Question 4 A simple linear regression model was used in order to predict y, duration of relief from allergy, from x, dosage of medication. A total of n=10 subjects were given varying doses, and their recovery times noted. Here is the R output. summary(lmod4) ## ## Call: ## lm(formula = y ~ x) ## ## Residuals: ##     Min      1Q Median      3Q     Max ## -3.6180 -1.9901 -0.4798 2.2048 3.7385 ## ## Coefficients: ##             Estimate Std. Error t value Pr(>|t|)    ## (Intercept)...
for stat students, model ( linear regression, multiple regression,factorial experiments,liner model) for each statistical method ,...
for stat students, model ( linear regression, multiple regression,factorial experiments,liner model) for each statistical method , why is the underlying statistical model important ? more than 4 reasons. please explain in clear way , i will discuss that with my class . Thx
Explain why you choose multiple regression with dummy variables but not linear trend model and why...
Explain why you choose multiple regression with dummy variables but not linear trend model and why do you believe this technique is appropriate to forecast your data?
ADVERTISEMENT
Subjects
ADVERTISEMENT
Latest Questions
ADVERTISEMENT