Question

In: Math

A two-variable model involving one quantitative explanatory variable and one categorical (binary) explanatory variable (and no...

A two-variable model involving one quantitative explanatory variable and one categorical (binary) explanatory variable (and no interaction), results in two regression lines that are:

A.     Always parallel.

B.     Could be parallel but, depending on the data, may not.

C.      Never parallel.

D.     Always horizontal.

The two methods of including a binary categorical variable in a regression model are to use indicator coding or effect coding. For indicator coding in the two-variable model (with no interaction):

A.     The binary variable is coded (-1,1) and the coefficient for the binary variable in the corresponding regression equation is the difference between the two group means.

B.     The binary variable is coded (-1,1) and the coefficient for the binary variable in the corresponding regression equation is the difference between one of the group means and the least-squares mean (the overall mean).

C.      The binary variable is coded (0,1) and the coefficient coefficient for the binary variable in the corresponding regression equation is the difference between the two group means.

D.     The binary variable is coded (0,1) and the coefficient for the binary variable in the corresponding regression equation is the difference between one of the group means and the least-squares mean (the overall mean).

Solutions

Expert Solution

1) A two-variable model involving one quantitative explanatory variable and one categorical (binary) explanatory variable (and no interaction), results in two regression lines that are (A)always parallel because the coefficient of binary variable makes the difference in intercept not in slope. lines with two different intercepts would always be parallel to each other but with different positions.

2) C.      The binary variable is coded (0,1) and the coefficient for the binary variable in the corresponding regression equation is the difference between the two group means.

In regression, the binary variable is always coded as 0/1 with 1 means presence and 0 means absence of the reference category. In the regression equation with the binary variable, the coefficient of the binary variable represents the difference between two group means and this difference would be added to the overall mean to make any change in the intercept of the regression equation. So the intercepts differ for two groups keeping the slope constant which makes the two regression equation parallel.


Related Solutions

classify each variable as quantitative or categorical. for categorical- state whether its ordinal or nominal for...
classify each variable as quantitative or categorical. for categorical- state whether its ordinal or nominal for quantitative- state whether its continuous or discrete and whether the level of measurement is ratio or interval VARIABLES: Marital Status Happiness Cholestoral Change Blood Pressure Change Vision Change Age Male
Consider a binary response variable y and two explanatory variables x1 and x2. The following table...
Consider a binary response variable y and two explanatory variables x1 and x2. The following table contains the parameter estimates of the linear probability model (LPM) and the logit model, with the associated p-values shown in parentheses. Variable LPM Logit Constant −0.40 −2.20 (0.03 ) (0.01 ) x1 0.32 0.98 (0.04 ) (0.06 ) x2 −0.04 −0.20 (0.01 ) (0.01 ) a. At the 5% significance level, comment on the significance of the variables for both models. Variable LPM Logit...
What are the SPSS procedures for: 4A Categorical and Categorical 4B Categorical with Quantitative 4C Quantitative...
What are the SPSS procedures for: 4A Categorical and Categorical 4B Categorical with Quantitative 4C Quantitative with Quantitative
Consider a binary response variable y and an explanatory variable x. The following table contains the...
Consider a binary response variable y and an explanatory variable x. The following table contains the parameter estimates of the linear probability model (LPM) and the logit model, with the associated p-values shown in parentheses. Variable LPM Logit Constant −0.78 −5.90 (0.03 ) (0.03 ) x 0.04 0.28 (0.07 ) (0.02 ) a. Test for the significance of the intercept and the slope coefficients at the 5% level in both models. Coefficients LPM Logit Intercept Slope b. What is the...
Consider a binary response variable y and an explanatory variable x. The following table contains the...
Consider a binary response variable y and an explanatory variable x. The following table contains the parameter estimates of the linear probability model (LPM) and the logit model, with the associated p-values shown in parentheses. Variable LPM Logit Constant −0.70 −6.60 (0.03 ) (0.03 ) x 0.04 0.18 (0.04 ) (0.03 ) a. Test for the significance of the intercept and the slope coefficients at the 5% level in both models. coefficient LPM Logit intercept slope b. What is the...
Label the type of each variable listed in the dataset as either quantitative or categorical, and...
Label the type of each variable listed in the dataset as either quantitative or categorical, and denote its scale of measurement (ratio, interval, ordinal, nominal). Wage (average hourly earnings) Education (years of education) Experience (years of experience) Tenure (length of time employed) Race Gender Married Dependents
17.3 Consider a binary response variable y and an explanatory variable x. The following table contains...
17.3 Consider a binary response variable y and an explanatory variable x. The following table contains the parameter estimates of the linear probability model (LPM) and the logit model, with the associated p-values shown in parentheses.   Variable     LPM     Logit   Constant −0.69      −6.30     (0.04)     (0.06)       x 0.05      0.15      (0.07)     (0.06)     a. Test for the significance of the intercept and the slope coefficients at a 5% level in both models.   Coefficients LPM         Logit...
1. Give and example of a case that has both a categorical and quantitative variable someone...
1. Give and example of a case that has both a categorical and quantitative variable someone might be interested in examining. 2. Describe an instance where a graph/chart/histogram/etc. or a median/mean given that was misleading and did not reveal the whole situation.
Determine if each variable included in the data set is quantitative (continuous, numerical) or qualitative (categorical)...
Determine if each variable included in the data set is quantitative (continuous, numerical) or qualitative (categorical) and then give its level of measurement: Gender Age Marital Status Highest education level Weight Height Rate general health Rate physical fitness Rate current weight Do you smoke How many cigarettes per day How many alcoholic drinks per day How many caffeine drinks per day Hours sleep week nights Hours sleep weekends How many hours sleep needed Trouble falling asleep? Trouble staying asleep Wake...
Collect data on one response (dependent or y) variable and two different explanatory (independent or x)...
Collect data on one response (dependent or y) variable and two different explanatory (independent or x) variables. This will require a survey with three questions. For example: To predict a student’s GPA (y), you might collect data on two x variables: SAT score and age. So we would be trying to determine if there was a linear correlation between someone’s SAT score and their GPA, as well as their age and their GPA. (Note: students may not choose GPA as...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT