**using probit model w/ disp_coke & disp_pepsi as dummy variable · probit coke pratio i.disp_pepsi i.disp_coke log likelihood Iteration 1: log likelihood = -711.02196 Iteration 2: log likelihood = -710.94858 Iteration 3: log likelihood = -710.94858 Iteration 0: -783.86028 Number of obs LR chi2(3) Probit regression 1,140 145.82 Prob > chi2 0.0000 Log likelihood = -710.94858 Pseudo R2 0.0930 coke | Coef. Std. Err. P>|z| [95% Conf. Interval] pratio | -1.145963 .1808833 -6.34 0.000 -1.500487 -.791438 1.disp_pepsi | 1.disp_coke | _cons | -.447297 .1014033 -4.41 0.000 -.6460439 -.2485502 .217187 .0966084 2.25 0.025 .027838 .4065359 1.10806 .1899592 5.83 0.000 .7357465 1.480373
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
The table shows the results after regressing probability of buying coke on coke ratio, displaying coke, and displaying pepsi. The latter two
Please interpret the following coefficents and standard errors from the probit model.
![**Probit Model Regression Analysis on Coke Purchase**
This analysis involves the use of a probit regression model, incorporating `disp_coke` and `disp_pepsi` as dummy variables. The model aims to understand the factors influencing coke purchases.
### Iterations and Log Likelihood Values:
The probit model underwent four iterations to achieve optimization, as outlined below:
- **Iteration 0:** Log likelihood = -783.86028
- **Iteration 1:** Log likelihood = -711.02196
- **Iteration 2:** Log likelihood = -710.94858
- **Iteration 3:** Log likelihood = -710.94858
### Regression Results:
The final table lists the results of the probit regression analysis. The columns are defined as follows:
- **Variable:** The independent variable included in the model.
- **Coef.:** The estimated coefficient for each variable.
- **Std. Err.:** The standard error of the coefficient estimate.
- **z:** The z-value, which measures the statistical significance of the coefficient.
- **P>|z|:** The p-value, indicating whether the coefficient is significantly different from zero.
- **[95% Conf. Interval]:** The 95% confidence interval for the coefficient estimate.
### Detailed Regression Output:
| **Variable** | **Coef.** | **Std. Err.** | **z** | **P>|z|** | **[95% Conf. Interval]** |
|------------------|------------|---------------|---------|----------|---------------------------|
| pratio | -1.145963 | 0.1808833 | -6.34 | 0.000 | -1.500487 -0.791438 |
| 1.disp_pepsi | -0.447297 | 0.1014033 | -4.41 | 0.000 | -0.6460439 -0.2485502 |
| 1.disp_coke | 0.217187 | 0.0966084 | 2.25 | 0.025 | 0.027838 0.4065359 |
| _cons | 1.10806 | 0.1899592 | 5.83 | 0.000 | 0.735746](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fc8f166b9-77d1-4e37-8c7d-7ac85592fc3b%2Fe72ef116-e913-49e6-a7e6-25ccc1993022%2Fk24q95w.png&w=3840&q=75)
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 2 images
