Perfect and imperfect multicollinearitya) Define perfect multicol.pdf
•
0 likes•19 views
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.
Report
Share
Report
Share
Download to read offline
Recommended
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
Define Multicollinearity in the following terms a. In what .pdf
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..
Assumptions of Linear Regression - Machine Learning
Description of the assumptions made by Linear Regression.
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.
Recommended
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
Define Multicollinearity in the following terms a. In what .pdf
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..
Assumptions of Linear Regression - Machine Learning
Description of the assumptions made by Linear Regression.
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.
Financial Risk Mgt - Lec 10 by Dr. Syed Muhammad Ali Tirmizi
These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.
Data Analyst - Interview Guide
Contains
a.Statistics-1
b. SAS-1
c. Statistics-2
d. Market Research
e. MS Excel
f. SAS-2
g. Data Audit & Data Sanitization
h. SQL
i. Model Building
j. HR
Heteroscedasticity
Brief notes on heteroscedasticity, very helpful for those who are bigners to econometrics. i thought this course to the students of BS economics, these notes include all the necessary proofs.
Introduction to Limited Dependent variable
A brief introduction to LDV and truncated and censored regression and various models for LDV
Data Science - Part IV - Regression Analysis & ANOVA
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Logistic regression and analysis using statistical information
Logistic regression and analysis using statistical information
Regression Analysis
Regression analysis is the most often applied technique of statistical analysis and modeling.
Tempest in teapot
Exploring what matters vs. what does not when developing regression models using time series data
7 classical assumptions of ordinary least squares
A brief overview of the assumptions used in working out the classical theory of least square towards predicting data in statistics
Logistic regression
Logistic model (or logit model) is used to model the probability of events realized in two classes such as alive/dead or healthy/sick etc. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1, the sum adding to one.
Part 2 of 3Suppose the value of technology adoption to a given use.pdf
Part 2 of 3
Suppose the value of technology adoption to a given user is log(n) where n is the total number of
users who have already adopted the technology.
Suppose the cost of technology adoption per user is c. Assume there are N users in the market.
Assume c > 0.
What is socially optimal level of adoption? What would be social value at that level? [2]
Solution
One of the most persistent themes in the social sciences, history, and the humanities is the impact
of technology and technological change on all aspects of social life. Major changes in human life
have been associated with major technological changes, such as the.
Paid-in capital from treasury stock would appear on a balance sheet .pdf
Paid-in capital from treasury stock would appear on a balance sheet under the category A) capital
stock. B) treasury stock. C) additional paid-in capital. D) contra to owners\' equity.
Solution
C) additional paid-in capital
actually i am also giving the c option but it is due to typing mistake
my explanation is
Paid in capital (Paid-in capital or Contributed capital) refers to capital contributed to a
corporation by investors through purchase of stock from the corporation (primary market) (not
through purchase of stock in the open market from other stockholders (secondary market)). It
includes share capital (i.e. capital stock) as well (as additional paid-in capital.).
More Related Content
Similar to Perfect and imperfect multicollinearitya) Define perfect multicol.pdf
Financial Risk Mgt - Lec 10 by Dr. Syed Muhammad Ali Tirmizi
These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.
Data Analyst - Interview Guide
Contains
a.Statistics-1
b. SAS-1
c. Statistics-2
d. Market Research
e. MS Excel
f. SAS-2
g. Data Audit & Data Sanitization
h. SQL
i. Model Building
j. HR
Heteroscedasticity
Brief notes on heteroscedasticity, very helpful for those who are bigners to econometrics. i thought this course to the students of BS economics, these notes include all the necessary proofs.
Introduction to Limited Dependent variable
A brief introduction to LDV and truncated and censored regression and various models for LDV
Data Science - Part IV - Regression Analysis & ANOVA
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Logistic regression and analysis using statistical information
Logistic regression and analysis using statistical information
Regression Analysis
Regression analysis is the most often applied technique of statistical analysis and modeling.
Tempest in teapot
Exploring what matters vs. what does not when developing regression models using time series data
7 classical assumptions of ordinary least squares
A brief overview of the assumptions used in working out the classical theory of least square towards predicting data in statistics
Logistic regression
Logistic model (or logit model) is used to model the probability of events realized in two classes such as alive/dead or healthy/sick etc. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1, the sum adding to one.
Similar to Perfect and imperfect multicollinearitya) Define perfect multicol.pdf (20)
Financial Risk Mgt - Lec 10 by Dr. Syed Muhammad Ali Tirmizi
Financial Risk Mgt - Lec 10 by Dr. Syed Muhammad Ali Tirmizi
Data Science - Part IV - Regression Analysis & ANOVA
Data Science - Part IV - Regression Analysis & ANOVA
Logistic regression and analysis using statistical information
Logistic regression and analysis using statistical information
More from akagencies2014
Part 2 of 3Suppose the value of technology adoption to a given use.pdf
Part 2 of 3
Suppose the value of technology adoption to a given user is log(n) where n is the total number of
users who have already adopted the technology.
Suppose the cost of technology adoption per user is c. Assume there are N users in the market.
Assume c > 0.
What is socially optimal level of adoption? What would be social value at that level? [2]
Solution
One of the most persistent themes in the social sciences, history, and the humanities is the impact
of technology and technological change on all aspects of social life. Major changes in human life
have been associated with major technological changes, such as the.
Paid-in capital from treasury stock would appear on a balance sheet .pdf
Paid-in capital from treasury stock would appear on a balance sheet under the category A) capital
stock. B) treasury stock. C) additional paid-in capital. D) contra to owners\' equity.
Solution
C) additional paid-in capital
actually i am also giving the c option but it is due to typing mistake
my explanation is
Paid in capital (Paid-in capital or Contributed capital) refers to capital contributed to a
corporation by investors through purchase of stock from the corporation (primary market) (not
through purchase of stock in the open market from other stockholders (secondary market)). It
includes share capital (i.e. capital stock) as well (as additional paid-in capital.).
Pangbourne Whitchurch has preferred stock outstanding. The stock pay.pdf
Pangbourne Whitchurch has preferred stock outstanding. The stock pays a dividend of $12 per
share, and sells for $75. What is the percentage cost of the preferred stock? (Enter your answer
as a whole percent.)
Cost of preferred stock %
Solution
Cost of Preferred Stock = D / P
D = Dividend per share
P = Market/isssue Price per share
Kp (Cost of preferred Stock ) = 12 / 75 = 16%.
Pepsi uses advertising to create the impression that Pepsi is superi.pdf
Pepsi uses advertising to create the impression that Pepsi is superior to any other soft drink.
Pepsi is attempting to:
A) sell less Pepsi so they can raise the price of Pepsi.
B) differentiate Pepsi from other types of soft drinks.
C) convince consumers that Pepsi is identical to other soft drinks.
D) lower the marginal cost of producing for Pepsi.
Solution
B) differentiate Pepsi from other types of soft drinks..
People with an external locus of control are a.more achievement o.pdf
People with an external locus of control are:
a.more achievement oriented.
b.better able to handle complex information and problem solving.
c.more independent.
d.easier to lead.
Solution
d) are easier to lead.
People with an external locus of control usually attribute their success or failure with outside
influences. Thus, they are easier to lead as they rely more on outside resources..
People with O-negative blood are universal donors. That is, any pati.pdf
People with O-negative blood are universal donors. That is, any patient can recieve tansfusion of
O-negative blood. Only 7.2% of the American population have O-negative blood. If 10 people
appear at random to give blood, what is the probablity that at least 1 of them is universal donor?
Solution
10C0 * .072^ 0 * .928^10 = 0.473674235 This is the probability that NONE of
them are the universal donor. It turns out, the event we are interested in, is just the compliment.
Then 1 - 0.473674235 = 0.526325765 Hence, 53% probability..
People enter a building at an average rate of 7 people per 15 minute.pdf
People enter a building at an average rate of 7 people per 15 minutes. (Their arrivals follow the
Poisson distribution) Suppose someone just arrived. What is the chance that at least 3 minutes
will elapse before the next person shows up?
Solution
c=7/15
let x = no. of persons per minute
P(x>3)=1- P(x<30
so it is 1-(P(1)+P(2)+P(3))
P(x=1)=(7/15*e^-(7/15))/1
P(x=2)=((7/15)^2*e^-(7/15))/2
P(x=3)=((7/15)^3*e^-(7/15))/6
answer is 1-0.29-0.068-0.0106=0.6314.
Pages 6 and 27 are on the same (double) sheet of a newspaper. What a.pdf
Pages 6 and 27 are on the same (double) sheet of a newspaper. What are the page numbers on the
opposite side of the sheet? How many pages are there..
in the newspaper altogether?
Solution
The third sheet bears 6 and 27.The backside of the sheet contains 5 and 28.
The second sheet has 3 , 4 pages and 29 ,30 pages.
The first sheet takes 1,2 and 31,32.
The number of pages : 32..
pCan you get a mixed number for a answer pSolution .pdf
Can you get a mixed number for a answer ?
Solution
For a probability problem you should not get a mixed or improper fraction.
Remember the probability of something is always a fraction that is less than 1 but greater than 0
Example: Probability when tossing a heads on a coin is 1/2.
Paytons indoor soccer team consists of 10 girls, but only 5 can be.pdf
Payton\'s indoor soccer team consists of 10 girls, but only 5 can be on the field at any given time
(four fielders and a goalie). How many starting lineups are possible if Payton or Jordyn must
play goalie?
Solution
total starting lineups = 2! * (9!)^4 =.
Payroll subjectHoyico Company has five employees, each of whom ea.pdf
Payroll subject:
Hoyico Company has five employees, each of whom earn $1,600 per month and are paid on the
last day of each month. All five have been employed continuously at this amount since January
1. On June 1, the following accounts and ballances exist in the General Ledger:
FICA-Social Security taxes payable; $992 (both Employee and Employer match)
FICA-Medicare taxes payable $232 (both Employee and Employer match)
Employee Federal Income tax payable; $1,050 (liability for May only)
Federal Unemployment taxes payable; $66 (liability for April and May)
State Unemployment taxes payable; $440 (liability for April and May)
*During June and July the company had the following payroll transactions:
June 25: Issued check payable to America Bank, a federal depository bank authorized to accept
employer\'s payments of FICA taxes and employee income tax withholdings. The $2,274 check
is in payment of the May FICA and employee income taxes (think about the required journal
entry, with all the extra info provided).
June 30: Recorded the Journal entry for the June salaries payable. Record the cash payment of
the June payroll (the company issued checks payable to each employee in payment of the June
payroll.
The payroll register shows the following summary totals for the June pay period:
SALARIES:
Office Salaries: $3,800
Shop Salaries: $4,200
Gross Pay $8,000
FICA taxes: $496 Social security
$116 Medicare
Federal Income taxes: $1,050
Net Pay: $6,338
June 30: Recorded the employer\'s payroll taxes resulting from the June payroll. The company
has a merit rating that reduces its state unemployment tax rate to 4.0% of the first $7,000 paid to
each employee. The Federal rate is 0.6%.
July 15: Issued check payable to America Bank in payment of the June FICA and employee
incoem taxes.
July 15: Issued check payable to State Tax Commision for the April, May and June state
unemployment taxes. Mailed the check and the second-quarter tax return to the State
Commisions.
July 31: Issued check payable to America Bank in payment of the employer\'s FUTA taxes for
the first quarter of the year.
Mailed Form 941 to the IRS, reporting the FICA taxes and the employee\'s income taxes and
employees\' federal income tax withholdings for the second quarter. (NOT SURE if journal entry
is needed to record this Forms, and/or required to transfer any funds with this report?).
REQUIRED: Prepare journal entries to record the transactions and events for both June and July.
Please make sure to post your calculations and comments on how to get there since it\'s really
important for me to understand this concepts. I really appreciate your help.
Solution
Accounting Entries
Date
Account Details and Explanation
Debit
Credit
June 25
FICA- Social Security Tax Payable
992
FICA- Medicare Tax Payable
232
Employee Federal Income Tax Payable
1050
Cash
2,274
To record the payment of FICA taxes for May and Employee federal income tax for May.
June 30
Office Salaries
3800
Shop Salaries
4200
FICA- Soc.
Paul Sablic is the supervisor of the purchasing department for Pine .pdf
Paul Sablic is the supervisor of the purchasing department for Pine Village Community Medical
Center ( CMC). ( See the Values and Beliefs Statement in Figure 1.8.) Paul had an opening in his
department. The human resources de-partment first used an internal recruiting method through
which current employees were asked to identify potential candidates from their social and
professional networks. 1 In addition, HR posted the position in several professional pub-lications
and in area newspapers and message boards. The goal was to attract and hire the better person
available.
HR prescreened all applicants ( more than 140 persons applied for the position) and reduced the
list to eight. Sablic and the HR personal had previously conducted a job and task analysis, and
identified the specific types of SKAs found in employees who perform successfully in that
position. Sample tests were administered in which applicants were asked to perform real job- like
activities. A series of work sample tests were developed and given to those employees currently
holding the position. The results affirmed that the tests measured the factors im-portant to the
position. Each of the final applicants was also given a broad personality inventory that included
integrity questions designed to weed out applicants likely to lie, steal, cheat, or defraud the
medical center. 2 Each applicant for the vacant position received written guidelines explaining
CMC
Solution
Hello Pal,
First of all a very nice as well as intersting question from your side.
So now straight to the question, as asked above with all the information available from your side
and from mine, it is very much true and practical to do so that to sooner than later. Fuller is a
potential candidate for this post. But, there must be something that Paul considered before
rejecting Fuller and taking the other, Helen Hoczyk, on board for this job.
To reinforce his claims, Fuller described several technological adaptive aids he used in his work
and contended that he had done well on the work sample tests. He also showed Holbrook papers
that documented the long hours he had put in at his previous job. He also shared several
recommendation letters in which former employers spoke highly of his abilities. Holbrook
listened patiently, making notes as Fuller talked. She was startled by Fuller.
Patients arrive at the emergency room of Costa Valley Hosipital at a.pdf
Patients arrive at the emergency room of Costa Valley Hosipital at an average of 5 per day. The
demand for emergency room treatment at Costa Valley follows a Poisson distribution.
(a) Using a Poisson appendix, compute the probability of exactly 0,1,2,3,4 and 5 arrivals per day.
(b) What is the sum of these probabilities, and why is the number less than 1?
Please show work. Thank you..
Solution
Mean value for the poisson\'s distribution = 5
So, the probability for various numbers is as follows:
P(X= 0) = 50e-5 / 0! = e-5 = 0.0067
P(X = 1) = 51e-5/ 1! = 5e-5 = 0.0336
P(X = 2) = 52e-5/2! = 25e-5/2 = 0.0842
P(X = 3) = 53e-5/3! = 125e-5/6 = 0.1403
P(X=4) = 54e-5/4! = 625e-5/24 = 0.1754
P(X=5) = 55e-5/5! = 3125e-5/120 =0.1754
Sum of al these probabilities = 0.6157 which is less than one because X can take even more
values like 6,7,8,9, etc. and so on each having a significant probability value.
Hope this helps. Please Rate it ASAP. Thanks..
Partner Bonuses, Statement of Partners EquityThe following addit.pdf
Partner Bonuses, Statement of Partners\' Equity
The following additional partner transactions took place during the year:
1. In early January, Randy Campbell is admitted to the partnership by contributing $75,000 cash
for a 20% interest.
2. Net income of $150,000 was earned in 2014. In addition, Dennis Overton received a salary
allowance of $40,000 for the year. The three partners agree to an income-sharing ratio equal to
their capital balances after admitting Campbell.
3. The partners\' withdrawals are equal to half of the increase in their capital balances from salary
allowance and income.
Solution
solution:-
Capital ratio:-180000:120000:75000 i.e :12:8:5ParticularDennisBenRandyOpening
Capital$180,000$120,000$75,000+Salary$40,00000+income sharing (150,000-
40,000)$52800$35200$22000-Drawings$46400$17600$11000Closing
capital$226,400$137600$86000.
Part of an Excel Summary Output relating X (independent Variable) an.pdf
Part of an Excel Summary Output relating X (independent Variable) and Y (dependent variable)
is shown below. Fill in all the blanks marked with %u201C?%u201D.
Summary Output
Regression Statistics
Multiple R
0.1347
R Square
?
Adjusted R Square
?
Standard Error
3.3838
Observations
?
ANOVA
df
SS
MS
F
Significance F
Regression
?
2.7500
?
?
0.632246859
Residual
?
?
11.45
Total
14
?
Coefficients
Standard Error
t Stat
P-value
Intercept
8.6
2.2197
?
0.0019
x
0.25
0.5101
?
0.6322
Summary Output
Regression Statistics
Multiple R
0.1347
R Square
?
Adjusted R Square
?
Standard Error
3.3838
Observations
?
ANOVA
df
SS
MS
F
Significance F
Regression
?
2.7500
?
?
0.632246859
Residual
?
?
11.45
Total
14
?
Coefficients
Standard Error
t Stat
P-value
Intercept
8.6
2.2197
?
0.0019
x
0.25
0.5101
?
0.6322
Solution
r square=r multiple*2=0.1347^2=0.018144
r adjusted squre=MSREGRESSIOM/MSTOTAL=(2.75/(151.6/14))=0.2539
OBSERVATIONS=DFTOTAL+1=14+1=15
DF of regression=1
df of RESIDUAL=14-1=13
SS residual=MS residual*df=13*11.45=148.85
SS total= SS RESIDUAL+ SS regression=151.6
MS regression= SS REGRESSION/DF=2.75
F=MS regression/MS residual=0.24017
T stat=coefficient/standard error
t stat intercept=8.6/2.2197=3.874397
t stat x=0.25/0.5101=0.49009998.
PART I The shape of the distribution of sample means is always tha.pdf
PART I
The shape of the distribution of sample means is always that of an approximately normal
distribution. T-F, Why or why not?
Solution
False: The shape of the distribution of sample means can´t be approximated by a
normal distribution wnen the sample size is small and the distribution of the population is not
similar to a normal distribution.
Part II. DefinitionsMatching. Please match terms under column A wit.pdf
Part II. Definitions/Matching. Please match terms under column A with their definitions in
column B
Solution
Macroeconomics: The branch of economics concerned with the overall performance of the
economy
Inputs: Commodities or services, such as land, labor, or capital that are used by firms in their
production process.
Opportunity cost: Measurement of cost based on the value of the next-best forgone alternative.
Efficiency: Using the economy’s resources as effectively as possible to satisfy people’s needs
and desires.
Positive economics: describes the facts and behavior of an economy.
Fallacy of composition: Assuming that what is true for part of a system is also true for the whole
system.
Scarcity: Commodities and resources that we value are limited in supply.
Outputs: Goods and services that results from the production process.
Laissez-faire: A market economy in which the government has almost no role.
Economic goods: Goods that we value are limited in supply.
Normative economics: Involves value judgements and ethical perspective about an economy.
Free goods: Commodities and resources that is available without limit.
Microeconomics: Branch of economics concerned with the behavior of individual entities such
as markets, firms, and households..
Part AWhat is the ratio of the intensities of an earthquake P wave.pdf
Part A
What is the ratio of the intensities of an earthquake P wave passing through the Earth and
detected at two points 13km and 42km from the source?
Express your answer using two significant figures.
Part B
What is the ratio of the amplitudes?
Express your answer using two significant figures.
Solution
we know that, intensity I = P/A = P/4?r2
from this equation if power (P) is same then
intensity of two sound waves ratio can be write as
I1/I2 =r22/r12 .................... (1)
A)
I27/I13 = (13 km)2/(42 k)2
= 0.0958
B)
intesity (I) is proportional to square of the amplitude ,
i.e. , I ? A2
I1/I2 = (A1/A2)2
(A1/A2) = (I1/I2)1/2
therefore ,
A45 /A13 = (I45/I13)1/2
= (0.0958)1/2
= 0.31.
PART B Record the journal entry to show the effect of the accrual t.pdf
PART B: Record the journal entry to show the effect of the accrual that should have been made
as of March 31, 2013. Journal Entry Worksheet Record the cash collection for season ticket sales
made in advance. Event General Journal DebitCredit Enter debits before credits done clear entry
record entry
Solution
Part A Event Particulars Debit $ Credit $ Cash 178,860 Unearned revenue
178,860 (To record advance received) 1 Unearned revenue 29,810
Revenue account 29,810 (To record entry to recognise revenue) Part B Payroll
tax expense XXX FICA Taxes payable XXX State unemployment taxes payable XXX
Federal unemployment taxes payable XXX.
pAdjusted Rsup2  supdoes not necessarily increase a.pdf
Adjusted R2 does not necessarily increase as additional regressors are introduced into the
model.
True/False?
Solution
True. Because the adjusted R2 has a built-in penalty for the number of regressors, it can go down
when irrelevant variables are added to the model..
More from akagencies2014 (20)
Part 2 of 3Suppose the value of technology adoption to a given use.pdf
Part 2 of 3Suppose the value of technology adoption to a given use.pdf
Paid-in capital from treasury stock would appear on a balance sheet .pdf
Paid-in capital from treasury stock would appear on a balance sheet .pdf
Pangbourne Whitchurch has preferred stock outstanding. The stock pay.pdf
Pangbourne Whitchurch has preferred stock outstanding. The stock pay.pdf
Pepsi uses advertising to create the impression that Pepsi is superi.pdf
Pepsi uses advertising to create the impression that Pepsi is superi.pdf
People with an external locus of control are a.more achievement o.pdf
People with an external locus of control are a.more achievement o.pdf
People with O-negative blood are universal donors. That is, any pati.pdf
People with O-negative blood are universal donors. That is, any pati.pdf
People enter a building at an average rate of 7 people per 15 minute.pdf
People enter a building at an average rate of 7 people per 15 minute.pdf
Pages 6 and 27 are on the same (double) sheet of a newspaper. What a.pdf
Pages 6 and 27 are on the same (double) sheet of a newspaper. What a.pdf
pCan you get a mixed number for a answer pSolution .pdf
pCan you get a mixed number for a answer pSolution .pdf
Paytons indoor soccer team consists of 10 girls, but only 5 can be.pdf
Paytons indoor soccer team consists of 10 girls, but only 5 can be.pdf
Payroll subjectHoyico Company has five employees, each of whom ea.pdf
Payroll subjectHoyico Company has five employees, each of whom ea.pdf
Paul Sablic is the supervisor of the purchasing department for Pine .pdf
Paul Sablic is the supervisor of the purchasing department for Pine .pdf
Patients arrive at the emergency room of Costa Valley Hosipital at a.pdf
Patients arrive at the emergency room of Costa Valley Hosipital at a.pdf
Partner Bonuses, Statement of Partners EquityThe following addit.pdf
Partner Bonuses, Statement of Partners EquityThe following addit.pdf
Part of an Excel Summary Output relating X (independent Variable) an.pdf
Part of an Excel Summary Output relating X (independent Variable) an.pdf
PART I The shape of the distribution of sample means is always tha.pdf
PART I The shape of the distribution of sample means is always tha.pdf
Part II. DefinitionsMatching. Please match terms under column A wit.pdf
Part II. DefinitionsMatching. Please match terms under column A wit.pdf
Part AWhat is the ratio of the intensities of an earthquake P wave.pdf
Part AWhat is the ratio of the intensities of an earthquake P wave.pdf
PART B Record the journal entry to show the effect of the accrual t.pdf
PART B Record the journal entry to show the effect of the accrual t.pdf
pAdjusted Rsup2  supdoes not necessarily increase a.pdf
pAdjusted Rsup2  supdoes not necessarily increase a.pdf
Recently uploaded
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...Nguyen Thanh Tu Collection
https://app.box.com/s/hqnndn05v4q5a4k4jd597rkdbda0fnii1.4 modern child centered education - mahatma gandhi-2.pptx
Child centred education is an educational approach that priorities the interest, needs and abilities of the child in the learning process.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar KanvariaSupporting (UKRI) OA monographs at Salford.pptx
How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Operation Blue Star - Saka Neela Tara
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Introduction to AI for Nonprofits with Tapp Network
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A Survey of Techniques for Maximizing LLM Performance.pptx
A Survey of Techniques for Maximizing LLM Performance
Advantages and Disadvantages of CMS from an SEO Perspective
Advantages and Disadvantages of CMS from an SEO Perspective
Best Digital Marketing Institute In NOIDA
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH A...
TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH ANSWERS.
special B.ed 2nd year old paper_20240531.pdf
Instagram:-
https://instagram.com/special_education_needs_01?igshid=YmMyMTA2M2Y=
WhatsApp:-
https://chat.whatsapp.com/JVakNIYlSV94x7bwunO3Dc
YouTube:-
https://youtube.com/@special_education_needs
Teligram :- https://t.me/special_education_needs
Slide Shere :-
https://www.slideshare.net/shabnambano20?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
MASS MEDIA STUDIES-835-CLASS XI Resource Material.pdf
mass media studies
CBSE
Full Book
for class 11th
Digital Artifact 2 - Investigating Pavilion Designs
Digital Artifact 2 - Pavilions
NGV Architecture Commission Competition
MPavilion Commission Competition
Other Pavilion Designs
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
"Protectable subject matters, Protection in biotechnology, Protection of othe...
Protectable subject matters, Protection in biotechnology, Protection of other biological materials, Ownership and period of protection
Recently uploaded (20)
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
1.4 modern child centered education - mahatma gandhi-2.pptx
1.4 modern child centered education - mahatma gandhi-2.pptx
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Pride Month Slides 2024 David Douglas School District
Pride Month Slides 2024 David Douglas School District
Introduction to AI for Nonprofits with Tapp Network
Introduction to AI for Nonprofits with Tapp Network
A Survey of Techniques for Maximizing LLM Performance.pptx
A Survey of Techniques for Maximizing LLM Performance.pptx
Advantages and Disadvantages of CMS from an SEO Perspective
Advantages and Disadvantages of CMS from an SEO Perspective
TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH A...
TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH A...
Multithreading_in_C++ - std::thread, race condition
Multithreading_in_C++ - std::thread, race condition
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
MASS MEDIA STUDIES-835-CLASS XI Resource Material.pdf
MASS MEDIA STUDIES-835-CLASS XI Resource Material.pdf
Digital Artifact 2 - Investigating Pavilion Designs
Digital Artifact 2 - Investigating Pavilion Designs
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
"Protectable subject matters, Protection in biotechnology, Protection of othe...
"Protectable subject matters, Protection in biotechnology, Protection of othe...
Perfect and imperfect multicollinearitya) Define perfect multicol.pdf
- 1. 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