Define Multicollinearity in the following terms a. In what .pdf
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Define Multicollinearity in the following terms: a. In what type of regression is it likely to occur? b. Why is multicollinearity in a regression a difficulty to be resolved? c. How can multicollinearity be determined in a regression?. d. If multicollinearity is found in a regression, how is it eliminated? Solution Define Multicollinearity in the following terms: a. In what type of regression is it likely to occur? Multicollinearity exists in almost all regressions, but is only a big problem when some of the independent variables are highly correlated. So, if you have many independent variables, there\'s a good chance some of them will be highly correlated. b. Why is multicollinearity in a regression a difficulty to be resolved? Multicollinearity causes the coefficients of the independent variables in question to fluctuate wildly when any of them is put into, or taken out of, the regression. A coefficient tells you the impact of changing one \"X\" variable when all others are HELD CONSTANT. But that is impossible when the indepedent variables are themselves highly correlated: you can\'t change one without changing the others, at least not easily. c. How can multicollinearity be determined in a regression?. One can examine the CORRELATION MATRIX of the independent variables. Although there is no perfect rule, once the correlations go up to .5-.6 or more, you are very likely to have collinearity problems. You can also see whether the coefficient on one X variable changes a lot when another one is removed. d. If multicollinearity is found in a regression, how is it eliminated? The easiest way is to remove all but one of any set of X variables that are highly correlated with one another. For example, you wouldn\'t put the number of bedrooms, number of bathrooms, and square footage of a house as separate independent variables, since they tend to be VERY highly correlated. You\'d try to remove two of them..
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Perfect and imperfect multicollinearitya) Define perfect multicol.pdf
Perfect and imperfect multicollinearity:
a) Define perfect multicollinearity either mathematically or explain it intuitively.
b) Explain how imperfect multicollinearity differs from perfect multicollinearity (you may, but
don
Solution
Multicollinearity is a high degree of correlation (linear dependency) among several independent
variables. It commonly occurs when a large number of independent variables are incorporated in
a regression model. It is because some of them may measure the same concepts or phenomena.
Only existence of multicollinearity is not a violation of the OLS assumption. However, a perfect
multicollinearity violates the assumption that X matrix is full ranked, making OLS impossible.
When a model is not full ranked, that is, the inverse of X cannot be defined, there can be an
infinite number of least squares solutions.
Symptoms of mulitcollinearity may be observed in situations: (1) small changes in the data
produce wide swings in the parameter estimates; (2) coefficients may have very high standard
errors and low significance levels even though they are jointly significant and the 2 R for the
regression is quite high; (3) coefficients may have the.
Multicollinearity PPT
This document discusses multicollinearity in regression analysis. It defines multicollinearity as an exact or near-exact linear relationship between explanatory variables. In cases of perfect multicollinearity, individual regression coefficients cannot be estimated. Near or imperfect multicollinearity is more common in real data and can lead to less precise coefficient estimates with wider confidence intervals. The document discusses various methods for detecting multicollinearity, such as auxiliary regressions and variance inflation factors, and potential remedies like dropping or transforming variables. However, multicollinearity diagnosis depends on the specific data sample and goals of the analysis.
LEC11 (1).pptx
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and corporate maturity. Voluntary settlements are initiated by the debtor and
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A researcher in attempting to run a regression model noticed a neg.docx
A researcher in attempting to run a regression model noticed a negative beta sign for an explanatory variable when s/he was expecting a positive sign based on theoretical considerations. What advice would you give to the researcher as to what is going on and what specific diagnostics would you look at? Explain conceptually and statisticallythe different ways you cancorrect for this problem.
Reason
One of the most common and important reasons for such situations is the existence of multicollinearity. Multicollinearity can happen if some of the independent variables are highly correlated to each other or to another variable that is not in the model.
Multicollinearity also has other symptoms such as
· Large variance for regression coefficients
· Non-significant individual coefficients while the general model is significant
· Change of marginal contributions depending on the variables in the model
· Large correlation coefficients in the correlation matrix of variables
It should however be noted that the general model can preserve its predictive ability and it is only the explanatory power that is lost
Before going to the solutions and measures the researcher can take it is wise to take a step back and see the underlying reason for the multicollinearity. An extreme case where two variables are identical gives the best understanding of problem
In this case we are trying to define y as a function of and while in reality . Therefore any linear combination of and is replaceable by infinite other linear combinations (ie )
It is simply understandable that while the y is predicted correctly in all the instances individual coefficients for and are meaningless.
Diagnosis
One of the most common diagnoses for multicollinearity is the variance inflation factor (VIF)
Where
And is the coefficient of multiple determination of regression of on other variables
The variance inflation factor therefore determines how much the variance of each coefficient inflates. when equals zero VIF equals 1 which suggests zero multicollinearity heuristic is that any value of VIF larger than 10 is alerting and a case of strong multicollinearity exists.
Solution
s
There are a few solutions for the multi Collinearity problem:
1- Ignoring the problem completely is possible for cases where we only care about the final model fit and prediction capability rather than individual coefficients and explanation power
2- Removing some of the correlated variables from the model, this can be justified since we can argue the effect of variable is however seen by similar highly correlated variables that are kept in the model
3- Principle component analysis (or any orthogonal transformation) can reduce the number of factors to a few orthogonal factors with no collinearity; however we should note that the interpretation of variables after a PC transformation is hard.
4- For cases where we intend to keep all the variables in the model without any major transformation, the Ridge regr.
Multicolinearity
The document discusses multicollinearity in regression analysis. It defines multicollinearity as a statistical phenomenon where two or more predictor variables are highly correlated. The presence of multicollinearity can cause problems with estimating coefficients and interpreting results. The document outlines symptoms of multicollinearity, causes, consequences, detection methods, and remedial measures to address multicollinearity issues.
Tempest in teapot
This document discusses constraints and assumptions of regression models with time series data. It argues that violations of assumptions like heteroskedasticity, autocorrelation, and non-normal residuals may not invalidate a model if robust standard errors are used. It also downplays issues like unit roots, multicollinearity, and lack of variable stationarity if residuals are stationary. The document emphasizes that high in-sample fit does not guarantee out-of-sample accuracy, recommending techniques like holdouts and evaluating economic turning points.
Multicollinearity1
This document discusses multicollinearity in regression analysis. It defines multicollinearity as a near linear relationship between predictor variables, which violates an assumption of classical linear regression. It provides an example of multicollinearity between product price and competitor prices. The effects of multicollinearity include indeterminate regression coefficients and infinite variance and covariance of coefficients when multicollinearity is perfect. Sources of multicollinearity include the data collection method, constraints in the population, model specification, and having more predictors than observations.
Multiple regression .pptx
Multiple regression analyzes the relationship between one dependent variable and multiple independent variables. It assumes:
1) Normal distribution of dependent variable for each independent variable value
2) Equal variance of dependent variable for each independent value (homoscedasticity)
3) Linear relationship between dependent and independent variables
4) Independence of errors and no autocorrelation between errors over time
Violations can be addressed by modifying variables or model specification. The Durbin-Watson test evaluates autocorrelation.
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Perfect and imperfect multicollinearitya) Define perfect multicol.pdf
Perfect and imperfect multicollinearity:
a) Define perfect multicollinearity either mathematically or explain it intuitively.
b) Explain how imperfect multicollinearity differs from perfect multicollinearity (you may, but
don
Solution
Multicollinearity is a high degree of correlation (linear dependency) among several independent
variables. It commonly occurs when a large number of independent variables are incorporated in
a regression model. It is because some of them may measure the same concepts or phenomena.
Only existence of multicollinearity is not a violation of the OLS assumption. However, a perfect
multicollinearity violates the assumption that X matrix is full ranked, making OLS impossible.
When a model is not full ranked, that is, the inverse of X cannot be defined, there can be an
infinite number of least squares solutions.
Symptoms of mulitcollinearity may be observed in situations: (1) small changes in the data
produce wide swings in the parameter estimates; (2) coefficients may have very high standard
errors and low significance levels even though they are jointly significant and the 2 R for the
regression is quite high; (3) coefficients may have the.
Multicollinearity PPT
This document discusses multicollinearity in regression analysis. It defines multicollinearity as an exact or near-exact linear relationship between explanatory variables. In cases of perfect multicollinearity, individual regression coefficients cannot be estimated. Near or imperfect multicollinearity is more common in real data and can lead to less precise coefficient estimates with wider confidence intervals. The document discusses various methods for detecting multicollinearity, such as auxiliary regressions and variance inflation factors, and potential remedies like dropping or transforming variables. However, multicollinearity diagnosis depends on the specific data sample and goals of the analysis.
LEC11 (1).pptx
Understand the types and major causes of business failure and the use of
voluntary settlements to sustain or liquidate the failed firm. A firm may fail
because it has negative or low returns, is insolvent, or is bankrupt. The major
causes of business failure are mismanagement, downturns in economic activity,
and corporate maturity. Voluntary settlements are initiated by the debtor and
can result in sustaining the firm via an extension, a composition, creditor control of the firm, or a combination of these strategies. If creditors do not agree to
a plan to sustain a firm, they may recommend voluntary liquidation, which
requirements and costs of bankruptcy proceedings
A researcher in attempting to run a regression model noticed a neg.docx
A researcher in attempting to run a regression model noticed a negative beta sign for an explanatory variable when s/he was expecting a positive sign based on theoretical considerations. What advice would you give to the researcher as to what is going on and what specific diagnostics would you look at? Explain conceptually and statisticallythe different ways you cancorrect for this problem.
Reason
One of the most common and important reasons for such situations is the existence of multicollinearity. Multicollinearity can happen if some of the independent variables are highly correlated to each other or to another variable that is not in the model.
Multicollinearity also has other symptoms such as
· Large variance for regression coefficients
· Non-significant individual coefficients while the general model is significant
· Change of marginal contributions depending on the variables in the model
· Large correlation coefficients in the correlation matrix of variables
It should however be noted that the general model can preserve its predictive ability and it is only the explanatory power that is lost
Before going to the solutions and measures the researcher can take it is wise to take a step back and see the underlying reason for the multicollinearity. An extreme case where two variables are identical gives the best understanding of problem
In this case we are trying to define y as a function of and while in reality . Therefore any linear combination of and is replaceable by infinite other linear combinations (ie )
It is simply understandable that while the y is predicted correctly in all the instances individual coefficients for and are meaningless.
Diagnosis
One of the most common diagnoses for multicollinearity is the variance inflation factor (VIF)
Where
And is the coefficient of multiple determination of regression of on other variables
The variance inflation factor therefore determines how much the variance of each coefficient inflates. when equals zero VIF equals 1 which suggests zero multicollinearity heuristic is that any value of VIF larger than 10 is alerting and a case of strong multicollinearity exists.
Solution
s
There are a few solutions for the multi Collinearity problem:
1- Ignoring the problem completely is possible for cases where we only care about the final model fit and prediction capability rather than individual coefficients and explanation power
2- Removing some of the correlated variables from the model, this can be justified since we can argue the effect of variable is however seen by similar highly correlated variables that are kept in the model
3- Principle component analysis (or any orthogonal transformation) can reduce the number of factors to a few orthogonal factors with no collinearity; however we should note that the interpretation of variables after a PC transformation is hard.
4- For cases where we intend to keep all the variables in the model without any major transformation, the Ridge regr.
Multicolinearity
The document discusses multicollinearity in regression analysis. It defines multicollinearity as a statistical phenomenon where two or more predictor variables are highly correlated. The presence of multicollinearity can cause problems with estimating coefficients and interpreting results. The document outlines symptoms of multicollinearity, causes, consequences, detection methods, and remedial measures to address multicollinearity issues.
Tempest in teapot
This document discusses constraints and assumptions of regression models with time series data. It argues that violations of assumptions like heteroskedasticity, autocorrelation, and non-normal residuals may not invalidate a model if robust standard errors are used. It also downplays issues like unit roots, multicollinearity, and lack of variable stationarity if residuals are stationary. The document emphasizes that high in-sample fit does not guarantee out-of-sample accuracy, recommending techniques like holdouts and evaluating economic turning points.
Multicollinearity1
This document discusses multicollinearity in regression analysis. It defines multicollinearity as a near linear relationship between predictor variables, which violates an assumption of classical linear regression. It provides an example of multicollinearity between product price and competitor prices. The effects of multicollinearity include indeterminate regression coefficients and infinite variance and covariance of coefficients when multicollinearity is perfect. Sources of multicollinearity include the data collection method, constraints in the population, model specification, and having more predictors than observations.
Multiple regression .pptx
Multiple regression analyzes the relationship between one dependent variable and multiple independent variables. It assumes:
1) Normal distribution of dependent variable for each independent variable value
2) Equal variance of dependent variable for each independent value (homoscedasticity)
3) Linear relationship between dependent and independent variables
4) Independence of errors and no autocorrelation between errors over time
Violations can be addressed by modifying variables or model specification. The Durbin-Watson test evaluates autocorrelation.
2. If you have a nonlinear relationship between an independent varia.pdf
2. If you have a nonlinear relationship between an independent variable x and a dependent
variable y, how can you apply the least -squares fit method? Does this work for all nonlinear
relationships? if not please give an example of a nonlinear equation where the fit method cannot
be used.(No need to use Matlab on this question).
Solution
A)The assumptions of linearity and additivity are both implicit in this specification. • Additivity
= assumption that for each IV X, the amount of change in E(Y) associated with a unit increase in
X (holding all other variables constant) is the same regardless of the values of the other IVs in
the model. That is, the effect of X1 does not depend on X2; increasing X1 from 10 to 11 will
have the same effect regardless of whether X2 = 0 or X2 = 1. • With non-additivity, the effect of
X on Y depends on the value of a third variable, e.g. gender. As we’ve just discussed, we use
models with multiplicative interaction effects when relationships are non-additive.
Linearity = assumption that for each IV, the amount of change in the mean value of Y associated
with a unit increase in the IV, holding all other variables constant, is the same regardless of the
level of X, e.g. increasing X from 10 to 11 will produce the same amount of increase in E(Y) as
increasing X from 20 to 21. Put another way, the effect of a 1 unit increase in X does not depend
on the value of X. • With nonlinearity, the effect of X on Y depends on the value of X; in effect,
X somehow interacts with itself. This is sometimes refered to as a self interaction. The
interaction may be multiplicative but it can take on other forms as well, e.g. you may need to
take logs of variables.
Dealing with Nonlinearity in variables. We will see that many nonlinear specifications can be
converted to linear form by performing transformations on the variables in the model. For
example, if Y is related to X by the equation E Yi Xi ( ) = + 2 and the relationship between the
variables is therefore nonlinear, we can define a new variable Z = X2 . The new variable Z is
then linearly related to Y, and OLS regression can be used to estimate the coefficients of the
model. There are numerous other cases where, given appropriate transformations of the
variables, nonlinear relationships can be converted into models for which coefficients can be
estimated using OLS. We’ll cover a few of the most important and common ones here, but there
are many others. Detecting nonlinearity and nonadditivity. The key question is whether the slope
of the relationship between an IV and a DV can be expected to vary depending on the context. •
The first step in detecting nonlinearity or nonadditivity is theoretical rather than technical. Once
the nature of the expected relationship is understood well enough to make a rough graph of it, the
technical work should begin. Hence, ask such questions as, can the slope of the relationship
between Xi and E(Y) be expected to have the same sign for all value.
Assumptions of Linear Regression - Machine Learning
There are 5 key assumptions in linear regression analysis:
1. There must be a linear relationship between the dependent and independent variables.
2. The error terms cannot be correlated with each other.
3. The independent variables cannot be highly correlated with each other.
4. The error terms must have constant variance (homoscedasticity).
5. The error terms must be normally distributed. Violations of these assumptions can result in poor model fit or inaccurate predictions. Various tests can be used to check for violations.
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2) Galton observed that the heights of children tended to "regress" toward the average height in the population, rather than matching their parents' exact heights. This was called "regression to mediocrity".
3) Simple linear regression analyzes the relationship between a single dependent variable and a single independent variable. It estimates the average value of the dependent variable based on the independent variable.
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Here are the steps to perform linear regression on the advertising dataset to predict sales based on TV spend:
1. Import necessary libraries
2. Split data into training and test sets
3. Create linear regression object
4. Fit the model on training data
5. Predict on test data
6. Calculate accuracy on test data
7. Print coefficient and intercept values
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42
Introduction to Limited Dependent variable
Limited dependent variables are dependent variables whose range is restricted, such as binary, count, ordinal, or censored variables. Models for limited dependent variables differ from linear regression models in that they are intrinsically nonlinear and estimated using maximum likelihood rather than least squares. Common models for limited dependent variables include logit and probit models for binary variables, ordered logit/probit for ordinal variables, Poisson regression for count variables, and tobit models for censored variables. These models are needed because linear regression may produce invalid predictions outside the variable's range and violate assumptions like homoscedasticity.
Estimating Models Using Dummy VariablesYou have had plenty of op.docx
Estimating Models Using Dummy Variables
You have had plenty of opportunity to interpret coefficients for metric variables in regression models. Using and interpreting categorical variables takes just a little bit of extra practice. In this Discussion, you will have the opportunity to practice how to recode categorical variables so they can be used in a regression model and how to properly interpret the coefficients. Additionally, you will gain some practice in running diagnostics and identifying any potential problems with the model.
To prepare for this Discussion:
Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.
Create a research question using the General Social Survey dataset that can be answered by multiple regression. Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables.
Estimate a multiple regression model that answers your research question. Post your response to the following:
What is your research question?
Interpret the coefficients for the model, specifically commenting on the dummy variable.
Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
Regression Diagnostics and Model Evaluation
Regression Diagnostics and Model Evaluation
Program Transcript
[MUSIC PLAYING]
MATT JONES: We've gone over estimating bivariate and multiple regression
models, but one thing we haven't talked about up to this point are some of the
assumptions of multiple regression models. It's very important to adhere to these
assumptions to have proper interpretation of our models. These assumptions
include linearity, independence of error, homoscedasticity, multicollinearity,
undue influence, and normal distribution of errors. Let's go back to SPSS to see
how we can test these assumptions and evaluate our models.
Let's go ahead and estimate a multiple regression model using respondent's
socioeconomic status index is the dependent variable, respondent's highest
education as an independent variable, and occupational prestige score as an
independent variable. But this time, let's request some additional information to
perform some diagnostics around our model.
Go to analyze, regression, and linear, since we are still using an ordinary least
squares method. We'll scroll down and enter my dependent variable first,
respondent socioeconomic index. My independent variables of occupational
prestige and highest year of school completed. I want to go over to statistics and
request some additional information. I will request collinearity ...
A Topic on REGRESSION Analysis conducted pptx
Regression analysis is a statistical method used to determine the relationship between variables. It allows one to predict the value of a dependent or outcome variable based on the value of one or more independent or explanatory variables. There are different types of regression including linear, polynomial, logistic, and quantile regression. Regression requires assumptions like linearity and homoscedasticity. Coefficients are calculated to obtain the regression line or equation that best models the relationship between the variables. Limitations include that it can be complex, not work for qualitative data, and assumptions of causality may not always hold.
Requirements.docxRequirementsFont Times New RomanI NEED .docx
Requirements.docx
Requirements:
Font: Times New Roman
I NEED 7 APA Style reference and In-text citation
Spacing: SINGLE
All the number of words are included next to the questions.
__________________________________________________________________________________
BSBLDR511 - Develop and use emotional intelligence
Questions:
1. Explain emotional intelligence principles and strategies (100 words)
2. Describe the relationship between emotionally effective people and the attainment of business objectives (100 words)
3. Explain how to communicate with a diverse workforce which has varying cultural expressions of emotion (100 words)
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Quiz 8 Notes
Scatterplots, Correlation and Regression
We are turning to our last quiz topic; regression. To get to regression, we need to understand several concepts first.
To start with, we will be working with two quantitative variables. The goal is to see if there is a relationship/association between the two variables. As one variable increases, what does the second variable do? If the second variable makes a consistent change then a relationship may exist. MAJOR POINT: saying a relationship exists does NOT mean there is Causation. The greatest abuse of statistical work is here, when a person runs a regression then says Variable A causes Variable B to change. You must have experimental results to establish causation.
Looking at the two variables that will be in a regression you need to know that each variable plays a specific role. One of the variables, X, will be the independent/explanatory variable and the other, Y, will be the dependent/ response variable. In a regression we are looking to see if changes in, Y; occur as X changes. It is very important that you establish at the beginning which of your variables will be X and which will be Y. Swapping the places for the two variables may not work. Let’s do an example.
In economics, we discuss the relationship of the quantity demand and the price of a good. Which one would be the X in a regression, and which would be, Y? The Law of Demand says, “as the price of a good increases, the quantity demanded decreases”. Which is allow.
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variable y, how can you apply the least -squares fit method? Does this work for all nonlinear
relationships? if not please give an example of a nonlinear equation where the fit method cannot
be used.(No need to use Matlab on this question).
Solution
A)The assumptions of linearity and additivity are both implicit in this specification. • Additivity
= assumption that for each IV X, the amount of change in E(Y) associated with a unit increase in
X (holding all other variables constant) is the same regardless of the values of the other IVs in
the model. That is, the effect of X1 does not depend on X2; increasing X1 from 10 to 11 will
have the same effect regardless of whether X2 = 0 or X2 = 1. • With non-additivity, the effect of
X on Y depends on the value of a third variable, e.g. gender. As we’ve just discussed, we use
models with multiplicative interaction effects when relationships are non-additive.
Linearity = assumption that for each IV, the amount of change in the mean value of Y associated
with a unit increase in the IV, holding all other variables constant, is the same regardless of the
level of X, e.g. increasing X from 10 to 11 will produce the same amount of increase in E(Y) as
increasing X from 20 to 21. Put another way, the effect of a 1 unit increase in X does not depend
on the value of X. • With nonlinearity, the effect of X on Y depends on the value of X; in effect,
X somehow interacts with itself. This is sometimes refered to as a self interaction. The
interaction may be multiplicative but it can take on other forms as well, e.g. you may need to
take logs of variables.
Dealing with Nonlinearity in variables. We will see that many nonlinear specifications can be
converted to linear form by performing transformations on the variables in the model. For
example, if Y is related to X by the equation E Yi Xi ( ) = + 2 and the relationship between the
variables is therefore nonlinear, we can define a new variable Z = X2 . The new variable Z is
then linearly related to Y, and OLS regression can be used to estimate the coefficients of the
model. There are numerous other cases where, given appropriate transformations of the
variables, nonlinear relationships can be converted into models for which coefficients can be
estimated using OLS. We’ll cover a few of the most important and common ones here, but there
are many others. Detecting nonlinearity and nonadditivity. The key question is whether the slope
of the relationship between an IV and a DV can be expected to vary depending on the context. •
The first step in detecting nonlinearity or nonadditivity is theoretical rather than technical. Once
the nature of the expected relationship is understood well enough to make a rough graph of it, the
technical work should begin. Hence, ask such questions as, can the slope of the relationship
between Xi and E(Y) be expected to have the same sign for all value.
Assumptions of Linear Regression - Machine Learning
There are 5 key assumptions in linear regression analysis:
1. There must be a linear relationship between the dependent and independent variables.
2. The error terms cannot be correlated with each other.
3. The independent variables cannot be highly correlated with each other.
4. The error terms must have constant variance (homoscedasticity).
5. The error terms must be normally distributed. Violations of these assumptions can result in poor model fit or inaccurate predictions. Various tests can be used to check for violations.
Chapter 2 Simple Linear Regression Model.pptx
1) Regression analysis examines the relationship between a dependent variable and one or more independent variables. It was first developed by Francis Galton to study inheritance of traits.
2) Galton observed that the heights of children tended to "regress" toward the average height in the population, rather than matching their parents' exact heights. This was called "regression to mediocrity".
3) Simple linear regression analyzes the relationship between a single dependent variable and a single independent variable. It estimates the average value of the dependent variable based on the independent variable.
ML4 Regression.pptx
Here are the steps to perform linear regression on the advertising dataset to predict sales based on TV spend:
1. Import necessary libraries
2. Split data into training and test sets
3. Create linear regression object
4. Fit the model on training data
5. Predict on test data
6. Calculate accuracy on test data
7. Print coefficient and intercept values
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42
Introduction to Limited Dependent variable
Limited dependent variables are dependent variables whose range is restricted, such as binary, count, ordinal, or censored variables. Models for limited dependent variables differ from linear regression models in that they are intrinsically nonlinear and estimated using maximum likelihood rather than least squares. Common models for limited dependent variables include logit and probit models for binary variables, ordered logit/probit for ordinal variables, Poisson regression for count variables, and tobit models for censored variables. These models are needed because linear regression may produce invalid predictions outside the variable's range and violate assumptions like homoscedasticity.
Estimating Models Using Dummy VariablesYou have had plenty of op.docx
Estimating Models Using Dummy Variables
You have had plenty of opportunity to interpret coefficients for metric variables in regression models. Using and interpreting categorical variables takes just a little bit of extra practice. In this Discussion, you will have the opportunity to practice how to recode categorical variables so they can be used in a regression model and how to properly interpret the coefficients. Additionally, you will gain some practice in running diagnostics and identifying any potential problems with the model.
To prepare for this Discussion:
Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.
Create a research question using the General Social Survey dataset that can be answered by multiple regression. Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables.
Estimate a multiple regression model that answers your research question. Post your response to the following:
What is your research question?
Interpret the coefficients for the model, specifically commenting on the dummy variable.
Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
Regression Diagnostics and Model Evaluation
Regression Diagnostics and Model Evaluation
Program Transcript
[MUSIC PLAYING]
MATT JONES: We've gone over estimating bivariate and multiple regression
models, but one thing we haven't talked about up to this point are some of the
assumptions of multiple regression models. It's very important to adhere to these
assumptions to have proper interpretation of our models. These assumptions
include linearity, independence of error, homoscedasticity, multicollinearity,
undue influence, and normal distribution of errors. Let's go back to SPSS to see
how we can test these assumptions and evaluate our models.
Let's go ahead and estimate a multiple regression model using respondent's
socioeconomic status index is the dependent variable, respondent's highest
education as an independent variable, and occupational prestige score as an
independent variable. But this time, let's request some additional information to
perform some diagnostics around our model.
Go to analyze, regression, and linear, since we are still using an ordinary least
squares method. We'll scroll down and enter my dependent variable first,
respondent socioeconomic index. My independent variables of occupational
prestige and highest year of school completed. I want to go over to statistics and
request some additional information. I will request collinearity ...
A Topic on REGRESSION Analysis conducted pptx
Regression analysis is a statistical method used to determine the relationship between variables. It allows one to predict the value of a dependent or outcome variable based on the value of one or more independent or explanatory variables. There are different types of regression including linear, polynomial, logistic, and quantile regression. Regression requires assumptions like linearity and homoscedasticity. Coefficients are calculated to obtain the regression line or equation that best models the relationship between the variables. Limitations include that it can be complex, not work for qualitative data, and assumptions of causality may not always hold.
Requirements.docxRequirementsFont Times New RomanI NEED .docx
Requirements.docx
Requirements:
Font: Times New Roman
I NEED 7 APA Style reference and In-text citation
Spacing: SINGLE
All the number of words are included next to the questions.
__________________________________________________________________________________
BSBLDR511 - Develop and use emotional intelligence
Questions:
1. Explain emotional intelligence principles and strategies (100 words)
2. Describe the relationship between emotionally effective people and the attainment of business objectives (100 words)
3. Explain how to communicate with a diverse workforce which has varying cultural expressions of emotion (100 words)
4. List at least five (5) examples of emotional strengths and weaknesses. Explain all. (100 words)
5. Identify at least three (3) examples of emotional states you might identify in co-workers in the workplace, and outline the common cues for each. (100 words)
6. Why is it essential to consider varying cultural expressions of emotions when working and responding to emotional cues in a diverse workforce? (100 words)
7. There are a variety of opportunities you may provide in your workplace for others to express their thoughts and feelings. List two (2). ( 100 words)
8. Why is it important to assist others to understand the effect of their behavior and emotions on others in the workplace? ( 100 words)
9. What information will you need to consider to ensure you use the strengths of workgroup members to achieve workplace outcomes? (100 words)
Quiz 8 Notes
Scatterplots, Correlation and Regression
We are turning to our last quiz topic; regression. To get to regression, we need to understand several concepts first.
To start with, we will be working with two quantitative variables. The goal is to see if there is a relationship/association between the two variables. As one variable increases, what does the second variable do? If the second variable makes a consistent change then a relationship may exist. MAJOR POINT: saying a relationship exists does NOT mean there is Causation. The greatest abuse of statistical work is here, when a person runs a regression then says Variable A causes Variable B to change. You must have experimental results to establish causation.
Looking at the two variables that will be in a regression you need to know that each variable plays a specific role. One of the variables, X, will be the independent/explanatory variable and the other, Y, will be the dependent/ response variable. In a regression we are looking to see if changes in, Y; occur as X changes. It is very important that you establish at the beginning which of your variables will be X and which will be Y. Swapping the places for the two variables may not work. Let’s do an example.
In economics, we discuss the relationship of the quantity demand and the price of a good. Which one would be the X in a regression, and which would be, Y? The Law of Demand says, “as the price of a good increases, the quantity demanded decreases”. Which is allow.
Econometrics_ch11.ppt
This document discusses multicollinearity in regression analysis. It begins by defining multicollinearity as a correlation between explanatory variables and distinguishes between perfect and imperfect multicollinearity. It then examines the consequences of multicollinearity, such as imprecise coefficient estimates with large variances. The document explores what happens under conditions of perfect versus imperfect multicollinearity and how it affects the estimation of regression coefficients.
Bus 173_6.pptx
This lecture discusses multiple regression analysis using two independent variables. Multiple regression faces two challenges: determining the influence of each variable and identifying which variables should be included. The model examines determinants of earnings using years of schooling and cognitive ability scores to predict earnings. Omitted variable bias can occur if an omitted variable is correlated with the included variables and influences earnings. Measures like R-squared, adjusted R-squared and F-tests evaluate how well the regression model fits the data.
Multiple Regression.ppt
Multiple regression analysis is used to understand the relationship between a dependent variable and multiple independent variables. It allows us to determine the effect of independent variables like age and work experience on a dependent variable like income. The analysis examines metrics like the coefficient of determination (R2), F-test, t-test and assumptions around normality, multicollinearity, homoscedasticity and autocorrelation. Properly conducted, multiple regression can be used to predict the value of a dependent variable based on the values of independent variables.
Econometrics ch11
This document discusses multicollinearity, which occurs when explanatory variables are linearly correlated. It addresses the nature of multicollinearity, whether it is a problem, its practical consequences, how to detect it, and ways to alleviate it. In the case of perfect multicollinearity, regression coefficients are indeterminate and have infinite standard errors. With imperfect but high multicollinearity, coefficients can be estimated but have large variances, leading to wide confidence intervals and insignificant t-ratios despite a high R-squared value. Regressions also become sensitive to small data changes.
Glm
This document describes the general linear model (GLM) and its foundations in multiple regression analysis. It discusses the historical development of the GLM from the theory of algebraic invariants in the 19th century. Multiple regression aims to quantify relationships between predictor and dependent variables. Computations for multiple regression involve estimating a linear equation relating the dependent variable to multiple independent variables and their regression coefficients. The GLM extends this approach to analyze different types of statistical designs.
biv_sssssssssssssssssssssssssssssssssssmult.ppt
This document discusses the differences between bivariate and multivariate analyses and their results. Bivariate correlations examine the relationship between two variables, while multivariate regression looks at relationships between a criterion and multiple predictors. The key differences are that bivariate correlations do not account for other predictors, while regression weights from multivariate analysis represent a predictor's unique contribution while controlling for other predictors. There are five common patterns that can emerge between a predictor's bivariate correlation and its weight in multivariate regression.
biv_mult.ppt
This document discusses the differences between bivariate and multivariate analyses and their results. It explains that a bivariate correlation shows the relationship between two variables, while regression weights from simple regression show the relationship between a predictor and criterion while holding other predictors constant. Regression weights from multiple regression also show this relationship between a predictor and criterion while controlling for other predictors. The document provides examples of different patterns that can emerge between bivariate and multivariate results and discusses factors like collinearity that can influence weights. It also addresses issues like proxy variables that may be standing in for other causal factors.
Modelo Generalizado
This document provides an introduction to generalized linear mixed models (GLMMs). GLMMs allow for modeling of data that violates assumptions of linear mixed models, such as non-normal distributions and non-constant variance. The document discusses the components of a GLMM, including the linear predictor, inverse link function, and variance function. It also describes how to derive estimating equations for GLMMs and provides an example for a univariate logit model. Estimation of variance components is also briefly discussed.
Logistic regression analysis
Binary logistic regression analysis is used to predict a dichotomous dependent variable from continuous and/or categorical independent variables. SPSS is used to conduct binary logistic regression by entering the dependent variable as 1/0 and independent variables as predictors, and the output provides coefficients, odds ratios, classification tables, and goodness of fit tests. Factors like multicollinearity between predictors and sample size need to be considered to develop the best fitting and most predictive logistic regression model.
Graph variables.ppt
This document defines and provides examples of independent and dependent variables, continuous and discrete data, and correlation. It discusses:
- Independent variables are what happens first and affect dependent variables, which depend on and are tested by independent variables.
- Continuous data involves measuring something that happens continuously, while discrete data involves counting whole numbers. Points are connected for continuous data and not for discrete.
- Correlation can be positive when both variables increase or decrease together, negative when one increases as the other decreases, or no correlation when the relationship is unclear.
Graph variables.ppt
This document defines and provides examples of independent and dependent variables, continuous and discrete data, and correlation. It discusses:
- Independent variables are what happens first and affect dependent variables. Dependent variables depend on independent variables and are what happens second.
- Continuous data involves measuring something that happens continuously like time, distance, or height. Discrete data involves counting things like people or cars.
- Correlation describes the relationship between independent and dependent variables. Positive correlation means both variables increase together. Negative correlation means one decreases as the other increases. No correlation means the variables do not change together.
Graph variables.ppt
This document defines and provides examples of independent and dependent variables, continuous and discrete data, and correlation. It discusses:
- Independent variables are what happens first and affect dependent variables. Dependent variables depend on independent variables and are what happens second.
- Continuous data involves measuring something that happens continuously like time, distance, or height. Discrete data involves counting things like people or cars.
- Correlation describes the relationship between independent and dependent variables. Positive correlation means the variables increase together. Negative correlation means one increases as the other decreases. No correlation means the relationship is unclear.
Similar to Define Multicollinearity in the following terms a. In what .pdf (20)
2. If you have a nonlinear relationship between an independent varia.pdf
2. If you have a nonlinear relationship between an independent varia.pdf
Assumptions of Linear Regression - Machine Learning
Assumptions of Linear Regression - Machine Learning
Estimating Models Using Dummy VariablesYou have had plenty of op.docx
Estimating Models Using Dummy VariablesYou have had plenty of op.docx
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began with meteorological missions
and has included a series of increasingly
sophisticated radars that have
provided valuable data about climate
and the changing environment. The
ESA’s current and future Earth observing
is under its Living Planet
Programme and includes the Earth
Explorer, meteorological, and Sentinel
missions.
JAXA has supported a wide range
of satellite-based instruments and
platforms that have and will provide
global Earth observations for water,
carbon, and climate. Of particular
note for the future is the commitment
to the Global Change Observation
Mission (GCOM) that will launch a
series of two types of satellites (water
and climate) to provide consistent and
continuous observations of key variables
over a 15-year period.
In addition to the United States,
ESA, and JAXA programs, there are
strong satellite-based Earth observing
programs in Canada, China, Argentina,
Taiwan, and Brazil.
In this Special Issue some of the
most significant recent and future Earth
observing satellites planned to monitor
water, carbon and global clima.
Describe an algorithm that takes as input a list of n integers in no.pdf
Describe an algorithm that takes as input a list of n integers in nondecreasing order and produces
the list of all values that occur more than once.
Solution
input Array [1...n]
for (n = 1 n <= array size; n++)
do
{
compare corresponding element with all other elements of the array
if (element matches with some other element in the list)
{
if the element that matched is not present already in our list
{
print that element
}
}
else
{
do nothing
}
}
Just want to explain this line
if the element that matched is not present already in our list
i.e. when we input an array, we create a new list , initially that list is empty. when loop runs for
the first time, it will check taking the first element of the array and compare it with all other
elements of the array. Let\'s suppose that element is found. Now, when that element is found,
then, it will be added to our list.
Now, observe that when loop runs again, there comes an instance when the element that was
found more than once will again run with for loop. that\'s why we are checking here to see that if
that element is not already a part of current list, then only add that element. If it is present in our
list, then do nothing and move on with the for loop..
Describe an initial public offering for a global firm (Red Bull). 15.pdf
Describe an initial public offering for a global firm (Red Bull). 150 - 200 words with reference
A discussion of some of the risks involved in the public offering and how the securities laws deal
with them
Please do not plagiarize. Only original work needed.
Solution
Initial public offering(IPO)
Initial public offering (IPO) is a public offering in which shares or stock of a company are sold to
institutional investors that in turn, sell to the general public, on a security exchange, for the first
time. A company selling shares is never required to repay the capital to its public investors. After
the IPO, when shares trade freely in the open market, money passes between public investors.
Initial public offerings are mostly used by companies to raise the expansion of capital, possibly
to monetize the investments of early private investors, and to become publicly traded enterprises.
Risks involved in the public offering and how the securities laws deal with them
There are many risks involved in IPO. Many companies that are going public for the first time
are new. Therefore, when one invests in public offering stocks they are sharing the risks of the
company. If the company goes under, the investor can stand the chance of losing his or her
investment .Initial public offering (IPO) may be a risky investment because of the following
reason
How the securities laws deal with them
The SEC does not regulate how the business decisions and how IPO shares are allocated(\"U.S.
Securities And Exchange Commission\", 2014). However, once a company decides to go public,
they must register with the SEC under the Securities Exchange Act of 1934.
At the time of the registration process they provide information regarding their financial state
and business operations. In addition to federal securities law, a company may also have to
comply with state securities laws, also known as \"blue sky\" laws (Inc., 2014).
Foreign Exchange Risks a Company Can Face and How to Mitigate Them Like any investment,
either buying or selling, there are inherent risks involved. The foreign exchange risk incorporates
not only the inflation of the parent country, but the inflation, economic stability, and political
standings of the foreign countries. The initial evaluation of any risk is to identify the source of
the risk and then find a way to minimize it..
Describe activities that the Engineer can perform throughout the pro.pdf
Describe activities that the Engineer can perform throughout the project life cycle in support of
the First canon (e.g. in Design Phase, Bid Package Preparation, and Construction Phase
Services). You can summarize your list of activities/examples in a table, specifying for each
activity which requirements/sub-section(s) a) through f) are addressed by such activity. Listing
of activities addressing most sub-sections a through f is a plus. ( 30 points)
Solution
After first starting of a carrier the civil engineer is placed into the construction site for the
execution of the design.
With the experience he will appointed as a senior excecutive engineer.Later he will be placed in
to the construction management department where he has to design the phase, tendering for the
new projects, Estimation of the costs and the cost and time for the project , Designing of the
structures etc.
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