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    the correlation in error terms that arises when the error terms at successive points in time are related is termed _____.

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    multicollinearity

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    In multiple regression analysis, the correlation among the independent variables is termed

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    dependent variable

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    In regression analysis, the variable that is being predicted is the

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    1/26 Created by leeny8918

    Terms in this set (26)

    multicollinearity

    In multiple regression analysis, the correlation among the independent variables is termed

    dependent variable

    In regression analysis, the variable that is being predicted is the

    The expected value of the error term is one.

    In regression analysis, which of the following is not a required assumption about the error term e?

    2

    What value of Durbin-Watson statistic indicates no autocorrelation is present?

    more than one independent variable

    A multiple regression model has

    autocorrelation

    The correlation in error terms that arises when the error terms at successive points in time are related is termed

    cyclical

    The time series component which reflects a regular, multi-year pattern of being above and below the trend line is

    one dependent and one or more independent variable are related

    Regression analysis is a statistical procedure for developing a mathematical equation that describes how

    the mean square error

    One measure of the accuracy of a forecasting model is

    zero

    In a regression analysis, the error term e is a random variable with a mean or expected value of

    used to predict the dependent variable

    In regression analysis, the independent variable is

    The F test and the t test may or may not yield the same conclusion.

    In simple linear regression analysis, which of the following is not true?

    SSR = SST

    In a regression and correlation analysis if r2 = 1, then

    is the dependent variable

    In a regression analysis, the variable that is being predicted

    larger than SST SSE can never be dependent variable

    In regression analysis, the response variable is the

    independent of each other

    In a multiple regression model, the values of the error term,e, are assumed to be

    be normally distributed

    In a multiple regression model, the error term e is assumed to

    the same as autocorrelation

    Serial correlation is

    an F test

    Which of the following tests is used to determine whether an additional variable makes a significant contribution to a multiple regression model?

    0 to 4

    The range of the Durbin-Watson statistic is between

    a dummy variable

    A variable that takes on the values of 0 or 1 and is used to incorporate the effect of qualitative variables in a regression model is called

    multicollinearity

    In multiple regression analysis, the correlation among the independent variables is termed

    Producer's risk

    In acceptance sampling, the risk of rejecting a good quality lot is known as

    Consumer's risk

    In acceptance sampling, the risk of accepting a poor quality lot is known as

    the probability of Type I error

    Consumer's risk is

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    Related questions

    QUESTION

    The coefficient of an independent variable (x1) will be underestimated if an omitted variable is negatively correlated with the independent variable (x1) and the omitted variable is negatively associated with the dependent variable.

    3 answers QUESTION

    A forecasting method that involves selecting a different weight for the most recent k data values values in the time series and then computing a weighted average of the values. The sum of the weights must equal one.

    3 answers QUESTION

    Which one is correct about fixed-effects models and random-effects models?

    2 answers

    Source : quizlet.com

    Error Term Definition

    An error term is a variable in a statistical model when the model doesn't represent the actual relationship between the independent and dependent variables.

    CORPORATE FINANCE & ACCOUNTING FINANCIAL ANALYSIS

    Error Term

    By ADAM HAYES Updated October 19, 2021

    Reviewed by MARGARET JAMES

    Fact checked by SUZANNE KVILHAUG

    What Is an Error Term?

    An error term is a residual variable produced by a statistical or mathematical model, which is created when the model does not fully represent the actual relationship between the independent variables and the dependent variables. As a result of this incomplete relationship, the error term is the amount at which the equation may differ during empirical analysis.

    The error term is also known as the residual, disturbance, or remainder term, and is variously represented in models by the letters e, ε, or u.

    KEY TAKEAWAYS

    An error term appears in a statistical model, like a regression model, to indicate the uncertainty in the model.

    The error term is a residual variable that accounts for a lack of perfect goodness of fit.

    Heteroskedastic refers to a condition in which the variance of the residual term, or error term, in a regression model varies widely.

    Understanding an Error Term

    An error term represents the margin of error within a statistical model; it refers to the sum of the deviations within the regression line, which provides an explanation for the difference between the theoretical value of the model and the actual observed results. The regression line is used as a point of analysis when attempting to determine the correlation between one independent variable and one dependent variable.

    Error Term Use in a Formula

    An error term essentially means that the model is not completely accurate and results in differing results during real-world applications. For example, assume there is a multiple linear regression function that takes the following form:

    \begin{aligned} &Y = \alpha X + \beta \rho + \epsilon \\ &\textbf{where:} \\ &\alpha, \beta = \text{Constant parameters} \\ &X, \rho = \text{Independent variables} \\ &\epsilon = \text{Error term} \\ \end{aligned}

    ​ Y=αX+βρ+ϵ where:

    α,β=Constant parameters

    X,ρ=Independent variables

    ϵ=Error term ​

    When the actual Y differs from the expected or predicted Y in the model during an empirical test, then the error term does not equal 0, which means there are other factors that influence Y.

    What Do Error Terms Tell Us?

    Within a linear regression model tracking a stock’s price over time, the error term is the difference between the expected price at a particular time and the price that was actually observed. In instances where the price is exactly what was anticipated at a particular time, the price will fall on the trend line and the error term will be zero.

    Points that do not fall directly on the trend line exhibit the fact that the dependent variable, in this case, the price, is influenced by more than just the independent variable, representing the passage of time. The error term stands for any influence being exerted on the price variable, such as changes in market sentiment.

    The two data points with the greatest distance from the trend line should be an equal distance from the trend line, representing the largest margin of error.

    If a model is heteroskedastic, a common problem in interpreting statistical models correctly, it refers to a condition in which the variance of the error term in a regression model varies widely.

    Linear Regression, Error Term, and Stock Analysis

    Linear regression is a form of analysis that relates to current trends experienced by a particular security or index by providing a relationship between a dependent and independent variables, such as the price of a security and the passage of time, resulting in a trend line that can be used as a predictive model.

    A linear regression exhibits less delay than that experienced with a moving average, as the line is fit to the data points instead of based on the averages within the data. This allows the line to change more quickly and dramatically than a line based on numerical averaging of the available data points.

    The Difference Between Error Terms and Residuals

    Although the error term and residual are often used synonymously, there is an important formal difference. An error term is generally unobservable and a residual is observable and calculable, making it much easier to quantify and visualize. In effect, while an error term represents the way observed data differs from the actual population, a residual represents the way observed data differs from sample population data.

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    Source : www.investopedia.com

    Multicollinearity in Regression Analysis: Problems, Detection, and Solutions

    Multicollinearity is when independent variables in a regression model are correlated. I explore its problems, testing your model for it, and solutions.

    Multicollinearity in Regression Analysis: Problems, Detection, and Solutions

    By Jim Frost 188 Comments

    Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. If the degree of correlation between variables is high enough, it can cause problems when you fit the model and interpret the results.

    I use regression to model the bone mineral density of the femoral neck in order to, pardon the pun, flesh out the effects of multicollinearity. Image By Henry Vandyke Carter – Henry Gray (1918)

    In this blog post, I’ll highlight the problems that multicollinearity can cause, show you how to test your model for it, and highlight some ways to resolve it. In some cases, multicollinearity isn’t necessarily a problem, and I’ll show you how to make this determination. I’ll work through an example dataset which contains multicollinearity to bring it all to life!

    Why is Multicollinearity a Potential Problem?

    A key goal of regression analysis is to isolate the relationship between each independent variable and the dependent variable. The interpretation of a regression coefficient is that it represents the mean change in the dependent variable for each 1 unit change in an independent variable when you hold all of the other independent variables constant. That last portion is crucial for our discussion about multicollinearity.

    The idea is that you can change the value of one independent variable and not the others. However, when independent variables are correlated, it indicates that changes in one variable are associated with shifts in another variable. The stronger the correlation, the more difficult it is to change one variable without changing another. It becomes difficult for the model to estimate the relationship between each independent variable and the dependent variable independently because the independent variables tend to change in unison.

    There are two basic kinds of multicollinearity:

    Structural multicollinearity: This type occurs when we create a model term using other terms. In other words, it’s a byproduct of the model that we specify rather than being present in the data itself. For example, if you square term X to model curvature, clearly there is a correlation between X and X2.Data multicollinearity: This type of multicollinearity is present in the data itself rather than being an artifact of our model. Observational experiments are more likely to exhibit this kind of multicollinearity.Related post: What are Independent and Dependent Variables?

    What Problems Do Multicollinearity Cause?

    Multicollinearity causes the following two basic types of problems:

    The coefficient estimates can swing wildly based on which other independent variables are in the model. The coefficients become very sensitive to small changes in the model.

    Multicollinearity reduces the precision of the estimated coefficients, which weakens the statistical power of your regression model. You might not be able to trust the p-values to identify independent variables that are statistically significant.

    Imagine you fit a regression model and the coefficient values, and even the signs, change dramatically depending on the specific variables that you include in the model. It’s a disconcerting feeling when slightly different models lead to very different conclusions. You don’t feel like you know the actual effect of each variable!

    Now, throw in the fact that you can’t necessarily trust the p-values to select the independent variables to include in the model. This problem makes it difficult both to specify the correct model and to justify the model if many of your p-values are not statistically significant.

    As the severity of the multicollinearity increases so do these problematic effects. However, these issues affect only those independent variables that are correlated. You can have a model with severe multicollinearity and yet some variables in the model can be completely unaffected.

    The regression example with multicollinearity that I work through later on illustrates these problems in action.

    Do I Have to Fix Multicollinearity?

    Multicollinearity makes it hard to interpret your coefficients, and it reduces the power of your model to identify independent variables that are statistically significant. These are definitely serious problems. However, the good news is that you don’t always have to find a way to fix multicollinearity.

    The need to reduce multicollinearity depends on its severity and your primary goal for your regression model. Keep the following three points in mind:

    The severity of the problems increases with the degree of the multicollinearity. Therefore, if you have only moderate multicollinearity, you may not need to resolve it.

    Multicollinearity affects only the specific independent variables that are correlated. Therefore, if multicollinearity is not present for the independent variables that you are particularly interested in, you may not need to resolve it. Suppose your model contains the experimental variables of interest and some control variables. If high multicollinearity exists for the control variables but not the experimental variables, then you can interpret the experimental variables without problems.

    Source : statisticsbyjim.com

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