0
What else can I help you with?
What is the difference between the logistic regression and regular regression?
in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.
What is a linear graph?
A linear graph is a model of a straight line on the X and Y axis. It represents the equation y=mx+b. A liner graph has a slope. A liner graph cannot be equaled to 0.
What is the difference between classical regression analysis and spatial regression analysis?
how can regression model approach be useful in lean construction concept in the mass production of houses
What does f statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
What can you conclude if the global test of regression does not reject the null hypothesis?
You can conclude that there is not enough evidence to reject the null hypothesis. Or that your model was incorrectly specified. Consider the exact equation y = x2. A regression of y against x (for -a < x < a) will give a regression coefficient of 0. Not because there is no relationship between y and x but because the relationship is not linear: the model is wrong! Do a regression of y against x2 and you will get a perfect regression!
What is residual in linear regression model?
You have a set of data points (x1,y1), (x2,y2), ..., (xn,yn), and you have assumed a line model, y = mx + b + e, where e is random error.You have fit the regression model to obtain estimates of the slope, m, and the intercept, b. Let me call them m and b.Now you can calculate yi - mxi - b for i = 1, 2, ... n. Notice that, for each i, this is an estimate of the error in yi. It's called the residual because it's what's 'left over' in yi after removing the part 'explained' by the regression.Another way of understanding this is to take a set of linearly related (x,y) pairs, graph them, calculate the regression line, plot it on the same graph and then measure the verticaldistances between the regression line and the each of the pairs. Those vertical distances are the residuals.
What is the difference between the logistic regression and regular regression?
in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.
What is a measure of the explanatory power of the regression model?
Regression analysis describes the relationship between two or more variables. The measure of the explanatory power of the regression model is R2 (i.e. coefficient of determination).
What are the key steps to create a regression model using a crate regression technique?
To create a regression model using a crate regression technique, follow these key steps: Define the research question and identify the variables of interest. Collect and prepare the data, ensuring it is clean and organized. Choose the appropriate regression model based on the type of data and research question. Split the data into training and testing sets for model evaluation. Fit the regression model to the training data and assess its performance. Evaluate the model using statistical metrics and adjust as needed. Use the model to make predictions and interpret the results.
What is a linear graph?
A linear graph is a model of a straight line on the X and Y axis. It represents the equation y=mx+b. A liner graph has a slope. A liner graph cannot be equaled to 0.
What if your data doesn't graph into a straight line?
If your data doesn't graph into a straight line, it may suggest a nonlinear relationship between the variables. In such cases, you can try fitting a curve or using a different type of regression analysis, such as polynomial regression or exponential regression, to better capture the underlying relationship in your data. It's important to choose the appropriate model that best fits your data and the underlying theory.
Where ridge regression is used?
Ridge regression is used in linear regression to deal with multicollinearity. It reduces the MSE of the model in exchange for introducing some bias.
What is the difference between classical regression analysis and spatial regression analysis?
how can regression model approach be useful in lean construction concept in the mass production of houses
What does f statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
Can you obtain R-square from standardized regression model?
yes
What is the difference between simple and multiple linear regression?
I want to develop a regression model for predicting YardsAllowed as a function of Takeaways, and I need to explain the statistical signifance of the model.
What does a large F-statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
