iWriteGigs

Fresh Grad Lands Job as Real Estate Agent With Help from Professional Writers

People go to websites to get the information they desperately need.  They could be looking for an answer to a nagging question.  They might be looking for help in completing an important task.  For recent graduates, they might be looking for ways on how to prepare a comprehensive resume that can capture the attention of the hiring manager

Manush is a recent graduate from a prestigious university in California who is looking for a job opportunity as a real estate agent.  While he already has samples provided by his friends, he still feels something lacking in his resume.  Specifically, the he believes that his professional objective statement lacks focus and clarity. 

Thus, he sought our assistance in improving editing and proofreading his resume. 

In revising his resume, iwritegigs highlighted his soft skills such as his communication skills, ability to negotiate, patience and tactfulness.  In the professional experience part, our team added some skills that are aligned with the position he is applying for.

When he was chosen for the real estate agent position, he sent us this thank you note:

“Kudos to the team for a job well done.  I am sincerely appreciative of the time and effort you gave on my resume.  You did not only help me land the job I had always been dreaming of but you also made me realize how important adding those specific keywords to my resume!  Cheers!

Manush’s story shows the importance of using powerful keywords to his resume in landing the job he wanted.

Exam 4

Navigation   » List of Schools  »  Los Angeles Harbor College  »  Statistics  »  Statistics 001 – Elementary Statistics I for the Social Sciences  »  Spring 2020  »  Exam 4

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Below are the questions for the exam with the choices of answers:

Question #1
A  Ranked scores
B  The chi-square value
C  Ordinal data
D  All of the above
Question #2
A  0.14
B  0.86
C  0.92
D  0.63
Question #3
A  Neither
B  Its sign (+ or – )
C  its size
D  Both
Question #4
A  X is a good predictor of Y
B  low scores on X are associated with low scores on Y
C  High scores on X are associated with high scores on Y
D  X is NOT a good predictor of Y
Question #5
A  for nominal data
B  when random sampling has been used
C  for tables that are 2 x 2
D  all of the above
Question #6
A  ordinal level data
B  both nominal and interval
C  interval level data
D  nominal level data
Question #7
A  the points where the regression line crosses the X axis when Y = 0
B  the amount of change in X for each unit change in Y
C  the point where the regression line crosses the Y axis when X = 0
D  the amount of change in Y for each unit change in X
Question #8
A  neither its sign (+ or -) or size
B  its sign (+ or -)
C  both its size and sign (+ or -)
D  its size
Question #9
A  contingency coefficient
B  Pearson’s r
C  Cramer’s V
D  Spearman’s rank order correlation coefficient
Question #10
A  Phi coefficient
B  Spearman’s r
C  Pearson’s r
D  Contingency Coefficient
Question #11
A  Pearson’s r
B  Goodman’s and Kruskal’s Gamma
C  Spearman’s r
D  Cramer’s V
Question #12
A  the table has the same number of rows and columns
B  the given table does not have the same number of row and columns
C  the table is 2 x 2
D  there is a large sample
Question #13
A  one variable is believed to be influenced by the other
B  the independent variable is influenced by the dependent variable
C  the independent variable must be categorical in nature
D  the variables being investigated must be correlated
Question #14
A  the proportion of variance in Y that is NOT explained by X
B  the proporion of variance in Y that is attributed to error
C  the proportion of the variance in Y that is explained by X
D  the proportion of variance in X that is attributed to error
Question #15
A  the ore the independent variable predicts the independent variable
B  the worse the obtained data fit the regression line
C  the less variance has been accounted for by the independent variable
D  the better the obtained data fit the regression line (AKA line of best fit)
Question #16
A  the point where the regression line crosses the Y-axis when X=0
B  the amount of change in Y for each unit change in X
C  the point where the regresssion line corsses the X-axis when Y=0
D  the amount of change in X for each change in Y
Question #17
A  not enough information to answer question
B  a positive relationship
C  a negative relationship
D  curvilienear relationship
Question #18
A  varies from -1.0 to +1.0
B  presents the strength of the relationship
C  presents the direction of the relationship
D  all of the above
Question #19
A  -0.31
B  (+0.58)
C  -0.28
D  (+0.12)
Question #20
A  correlations closer to 0.0 are considered to be weak, while correlations closer to 1.0 are considered to be strong.
B  correlations can be negative
C  correlations provide statements of causation
D  correlations can never exceed 1.0
Question #21
A  negative
B  positive
C  inverse
D  there is not enough information to estimate the nature of the correlation