What are the Standard Errors for Fixed Effects Regression?

Q: Explain the assumptions are needed to calculate the various inferential statistics of linear…

A: The assumptions needed for inferential statistics of linear Regression is  The dependent variable…

Q: What are the assumption and observation of regression analysis?

A: Regression analysis: Regression is the mathematical method that determines the mean association…

Q: Is it reasonable to interpret the y-intercept of the regression line ? Why or why not?

A: In the given case, a regression model is fit between the carpet area of a house and its…

Q: How do you determine if a regression model is showing a case of suppression?

A: Suppressions:  It can be defined as “a variable which increases the predictive validity of another…

Q: Explain the Two stage Least Square estimator in the general IV regression model?

A: Regression analysis of twostage minus squares (2SLS) is a statistical technique used in the analysis…

Q: State the four conditions required for making inferences in multiple linear regression analysis

A: Solution: We have to state the four conditions required for making inferences in multiple linear…

Q: What are the objects of regression?

A: Regression: Regression analysis estimates the effect that changing one independent variable has on…

Q: What is the coefficient of determination and what is its significance? How prediction can be done…

A: The coefficient of determination (r2) defines the percent of variation of the response variable…

Q: Explain assessing the validity of a regression model for forecasting?

A: Predictions for regression are true only for the data set used to estimate the model. The…

Q: What is regression? why are there in general two regression lines? under what conditions properties…

A: Regression is a statistical method used in finance, investing, and otherdisciplines which attempts…

Q: Define Internal and External Validity When the Regression Is Used for Forecasting?

A: A statistical analysis has internal validity when the statistical inference regarding causal effects…

Q: What is the connection between cointegration and spurious regression?

A: In general, regression models for non- stationary variables give spurious results .only  exception…

Q: The random error term represents the influences of all of the unobserved factors that are not…

A: The general form of the regression model is as shown below: Y=β0+β1X1+β2X2+...+βkXk+U Here U…

Q: Define the ADL and GLS Estimators of Regression.

A:

Q: Define Linear Regression Errors?

A: The residual or errors is defined as the difference between the observed value of the dependent…

Q: What type of standard errors would you use to estimate regression? Does your inference about the…

A: Given information: A multiple linear regression equation is given.

Q: What is Instrumental Variables Regression?

A: An instrumental variable (sometimes referred to as a "instrument" variable) is a third variable, Z,…

Q: What are the possible consequences of the presence of outliers in the data used for conducting a…

A: An outlier is an observation that is distant from the other values in a data set. It causes serious…

Q: What is A REGRESSION LINE?

A: A regression line is a line which is generally used to estimate a dependent or response variable…

Q: If the error term in a linear regression model is normally distributed, then the distribution of the…

A:

Q: What does the term "extrapolation" mean for regression problems?

A: The regression lines are used to make predictions for the x values.

Q: You will not obtain a high R-squared value in a multiple regression if all the partial slope…

A: We need to use the concept of R square and regression.

Q: What are the assumptions of Inferential statistics in regression?

A: Regression analysis is used to predict the dependent variable by using the one or more independent…

Q: Explain why selecting a regression equation using stepwise regression is considered superior to…

A:

Q: What is.the multiple linear regression equation for predicting Annual Income (as the response…

A: The regression analysis is conducted using EXCEL. The software procedure is given below: Enter the…

Q: State the four conditions required for making regression inferences.

A:

Q: What are the assumptions of the classical regression model(CRM)?

A: Assumption 1: Linear Parameter and correct model specification Assumption 1 requires that the…

Q: Explain why we might sometimes consider explanatory variables in a regression model to be random.

A: Hint: Here we need to write why sometime we use explanatory variables.

Q: Show that an interaction term of a dummy variable and a regressor changes the slope of a regression…

A: Please find the explanation below. Thank you

Q: What is a simple way to check for normality of residuals in a regression model?

A: First of all, testing of normality of residual is important for any regression model because it it…

Q: Describe about the need of mathematical solution for least square regression line?

A:

Q: Calculate the equation of the regression line and calculate the correlation coefficient

A: Correlation is used to measure the mean significance between the two numerical variable. It take…

Q: What is measured by the standard error of estimaate for a regression equation?

A: Standard Error of estimate(SEE) is given by, SEE=(Y-Y^)2NY: Actual ValueY^: predicted valuesN:…

Q: What are the Assumptions of Extended Least Squares in the Multiple Regression Model?

A:

Q: What are the assumptions of multiple linear regressions only?

A: The assumptions for multiple linear regression inferences are; 1. Linearity: There must be linear…

Q: What is the prediction interval equation for multiple linear regression when given σ ?

A: Prediction interval for multiple linear regression is given by Prediction interval

Q: When would a difference in difference regression model be an appropriate regression model to…

A: let us first try to understand the basis of regression analysis Definition: Regression analysis is a…

Q: What is the standard error of estimate?

A:

Q: Discuss the advantages of using data collected from a designed experiment in performing a regression…

A: In a designed experiment the experimenter collects the data according to their requirements.

Q: Do you think the regression results have been affected by the multicollinearity issue? Why or why…

A: What is multicollinearity Multicollinearity is problem where one or more than one independent…

Q: What are the different ways to reduce the error in least square regression?

A: The error term follows normal distribution with mean 0 and constant variance. The error term is same…

Q: Why is the null hypothesis for regression usually B- 0?

A: Regression analysis: The regression analyzes the relationship between the predictor or independent…

Q: True or false: “If the errors in a regression model contain ARCH, they must be serially correlated.”

A: In general, the ARCH effect test allows the occurrence of autocorrelation in the variance of error…

Q: IS the following statment true or false, please explain why    For each x term in the multiple…

A:

Q: What are the different measures used for the OLS estimate of the multiple regression line?

A: Multiple linear regression (MLR) is used among a number of random variables to determine the…

Q: Explain why all-subsets regression is considered superior to stepwise regression in selecting a…

A: All-subsets regression: The all-subsets regression considers all the predictors available to predict…

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.