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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
How many data points for regression?
The number of data points needed for regression analysis depends on several factors, including the complexity of the model and the number of predictor variables. A common rule of thumb is to have at least 10 to 15 data points per predictor variable to ensure reliable estimates. However, more data is generally better, as it can improve the model's accuracy and robustness. Ultimately, the specific context and objectives of the analysis will also influence the required sample size.
How a scatter diagram can be used to identfy what type of regression to use?
A scatter diagram visually represents the relationship between two variables, allowing you to observe patterns, trends, and potential correlations. By examining the shape of the data points, you can determine if the relationship is linear, quadratic, or exhibits another form. For instance, if the points roughly form a straight line, a linear regression may be appropriate; if they curve, a polynomial regression could be better suited. Additionally, the presence of clusters or outliers can inform the choice of regression model and its complexity.
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 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.
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.
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
How many data points for regression?
The number of data points needed for regression analysis depends on several factors, including the complexity of the model and the number of predictor variables. A common rule of thumb is to have at least 10 to 15 data points per predictor variable to ensure reliable estimates. However, more data is generally better, as it can improve the model's accuracy and robustness. Ultimately, the specific context and objectives of the analysis will also influence the required sample size.
How a scatter diagram can be used to identfy what type of regression to use?
A scatter diagram visually represents the relationship between two variables, allowing you to observe patterns, trends, and potential correlations. By examining the shape of the data points, you can determine if the relationship is linear, quadratic, or exhibits another form. For instance, if the points roughly form a straight line, a linear regression may be appropriate; if they curve, a polynomial regression could be better suited. Additionally, the presence of clusters or outliers can inform the choice of regression model and its complexity.
What is the variance of intercept in a simple regression model when all observations on x axis are identical?
In a simple regression model, if all observations on the x-axis are identical, the variance of the intercept becomes undefined. This is because the lack of variability in the independent variable (x) means that the model cannot estimate the relationship between x and the dependent variable (y). As a result, the regression line is essentially vertical, leading to an inability to determine a meaningful slope or intercept. Thus, the model fails to provide a valid statistical analysis.
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.
