Tag: Data Analysis

Choose one specific Institution (Say Wells Fargo) or one specific market (say, Cryptocurrency market)
Choose a recent fraud activity (past 5 years), a major fraud ($100 million or above)
Choose an activity for one where financial information or data is publicly available
Identify and analyses the metric and framework for fraud using past / historical pattern
Take a financial and analytical approach; not a descriptive, historical or psychological approach
Do extensive research by going to scholar.google.com or other internet sites and identify ten major, relevant and focused article on this fraud
Do analysis and explain in your own words why it happened, how it happened, and what is being done to prevent such a fraud recurrence.

Tensile Test
Using the data given on the Excel sheet find the followings:
Sample 1 : % 0.4 Carbon Steel, oil Quenched
Initial or original Length of the sample = 33.81 mm
Final length of the sample after fracture = 35.15 mm
Initial or original diameter of the sample = 3.58 mm
Final diameter of the sample after fracture = 3 mm
Force (KN) Displacement (mm)
0 0
6.7 0.53
10.5 0.78
12.3 0.94
13.5 1.1
14.3 1.28
14.8 1.52
14.8 1.78
13.8 2.1
Sample 2: Aluminium alloy
solution treated at 505 centigrade – water quenched and aged at 185 for 6
hours
Initial length of the sample = 37.30 mm
Final length of the sample = 40.38
Initial diameter of the sample = 5.03
Final diameter of the sample = 4.14 mm
Force (KN)

Displacement (mm)
0 0
5.1 0.51
5.4 0.88
5.5 1.28
5.5 1.73
5 2.27
Sample 3: Brass (60% Copper- 40% Zinc) – Cold Worked and annealed
Initial length of the sample = 36.32 mm
Final length of the sample = 49.98 mm
Intitial Diameter of the sample = 3.8 mm
Final Diameter of the sample = 3.28
Force (KN) Displacement (mm)
0 0
2.3 1.85
3.9 4.5
4.6 6.5
4.7 6.85
4.9 7.2
4.9 7.9
5 8.4
5.1 8.7
5.1 9.3
5.2 9.73
5.2 10.3
5.2 10.8
5.2 11.3
5.2 11.8
5.1 12.3
5 12.7
4.1 13.5
1. Draw force-displacement diagrams for all four samples
on EXCEL
sheet
1. Calculate engineering stress and strain for all samples
and draw the stress-strain diagrams for all four samples
on one EXCEL sheet
σ = F / A
σ is the engineering stress in (MPa)
F is the force in (KN)
A is the cross sectional area of the sample = πd²/ 4
Ɛ = (lf – l0) / l0
Ɛ is the engineering strain (no unit)
l0 is the original length of the sample in (mm)
lf is the final length of the sample in (mm)

2. On the stress- strain diagrams for all samples show:
2.1 Yield point
2.2 Ultimate tensile stress
2.3 Breaking stress
2.4 Toughness
2.5 Elastic region
3. Calculate modulus f elasticity, percentage elongation
(%EL) and percentage reduction in area (%RA)
% EL = (lf – l0) / l0 % RA = (A0 – Af) / A0

Discuss the following in the Discussion Board:

Think of a purpose for creating a database and describe the database. Explain why you would want to use a database for this purpose.
What example could you offer that would demonstrate using a subquery?
What example could you offer that would demonstrate using a join?
In your view, would you prefer using subqueries, or joins, and why?
What are some performance considerations when determining whether to use subqueries or joins?

The data base that we’ve used is in the following links (http://gander.wustl.edu.) (https://www.ncbi.nlm.nih.gov/gene)

Results section Plan:

Objectives:

• In the beginning I will start by organizing and choosing which data file matches the chosen control strategy to convert it to SPSS and try to understand the scores.
• Decide on type of analyses.
• Then I will start by plotting the mean scores for each of the 4 conditions.
• Thirdly I will look at which conditions have the highest similarity ratings, and which have the lowest.
• I will a conduct a repeated measures ANOVA.
• Then I will look at mean scores to understand the effect and see which relevant IV has a higher score and if there is an interaction.
• If necessary and I found out that there is a significant interaction, after that I plan to use paired t-tests, follow up tests, Bonferroni correction to the alpha level.
• Calculate a difference measure for each to get a measure of change in specificity/valence from before to after.
• Subtract time 1 scores from time 2 scores = differences
• analyse those difference scores in a 2×2 ANOVA
• Look at the output to understand the scores
• Gather my tables and results in the SPSS output file.
• Prepare to write up and present the results section including and reporting the (mean, standard deviation, p value etc.)
• Relating and testing my results to my hypothesis.
• ‘If there are notable effects in the application of the memory control strategy, then the participants with different memories should depict distinct changes on memory content, emotions and feelings.

Notes from meeting and points to follow:

we have a 2×2 factorial design with two IVs/factors (Think/No-Think manipulation x Morally Right/Wrong memory type)

• looking at the similarity ratings as the main DV
• because they can capture any changes in memories that might occur as a result of the TNT manipulation, or the memory type, or the interaction between these two factors.
• start by plotting the mean scores for each of these 4 conditions

• then look at which conditions have the highest similarity ratings, which have the lowest
• we need to conduct inferential statistics to test if the main effects of the two factors and their interaction are significant.

• Conduct the ANOVA,( with descriptive statistics like the pdf example) you need to look at the mean scores to understand the effect – if there are significant main effects.
• which level of the relevant IV has a higher score?

• If there is an interaction, how do the scores seem to differ depending on the combination of the two factors?

• If (and only if) there is a significant interaction, you need to follow up with t-tests

• use paired t-tests to do follow up tests, and apply your own Bonferroni correction to the alpha level based on the number of follow up tests you are conducting

Choose the data from this website: https://github.com/rfordatascience/tidytuesday. And follow this website to see which code you may need to use: http://ritsokiguess.site/STAC32/notes.html. Last, please provide the word docs file at the end. Thank you so much.

Use this Indo-European database (https://lrc.la.utexas.edu/lex) as well as Vasmer’s dictionary (https://www.etymonline.com/search?q=t&page=38). HW #4 (due end of week 4): Establish PIE sources for all vowels on the list; HW #5 (due end of week 5): Find as many present-day Slavic equivalents of the lexemes from the list and explain the development of all vowels; HW #6 (due end of week 6): Establish PIE sources for all consonants on the list; HW #7 (due end of week 7): Explain the consonantal and distribution changes from CSL to the present-day Slavic languages. Submit your work in a separate Word file (all four assignments together). Look for explanations video here: https://we.tl/t-wNHdhU9Yj4 bergъ hill, bank blǫdъ mistake, sin bratrъ brother bykъ bull cѣna price desętь ten elenь dear (the animal) golva head gordъ castle, town grѣhъ sin kry blood lьnъ flax med’a border melko milk merti die mьgla fog noktь night oko eye pedso on foot, pedestian pǫtь road rǫka hand sestra sister svѣt’a candle sьrdce heart sъto hundred stolъ table sѣmę seed uxo ear vьlkъ wolf voda water vъšь louse zemja earth zima winter

Steps for data analysis:
1. Rename my variables into something that is easily recognizable.
2. Use the frequencies command on my categorical variables to get an idea of how my data set looks – this will be important for methods section to write about stuff such as how many men vs women you sampled.
3. Use the compute command to create a Mean or a Sum score for participants’ ratings of YouTube, using only those who responded to every question on my survey.
4. Next you need to filter out people who didn’t meet the criteria for my analysis, such as those who didn’t attend the University of Hail.
5. If you run the descriptives command for my rating variable, you should have an N (sample size) of 742. This is also where you’ll find the mean/standard deviation.
6. Next you need to run the following analysis: a Factorial ANOVA.
7. Now, run the same analysis, but remove any nonsignificant interaction terms. Make sure to include post-hoc tests for predictors with more than two levels that are significant. You need also use the plot function to graph anything you feel you need to. Also you have to talk about specific means (such as men vs women), report estimated marginal means. You also want to include measures of effect size. You would also run homogeneity tests to see if my data fits the assumptions of the test.
8. If you ran the first analysis according to these steps, you should have 742 under the “df” column in the “total” row of the between-subjects table. For the second analysis, this should be 804. These are degrees of freedom, and they correspond to my sample size.
you should end up with 742 sample sizes and if you did get there then you did what I did exactly and you’re on the right track and good to go.

Steps for writing results:

1. You essentially you need to start by specifying exactly what type of test you ran, such as: “A 2 x 2 x 4 mixed factorial analysis of variance was conducted.” This is determined by the number of levels your variables have, and whether they are within or between subjects.
2. You then need to state what your outcome variable was, and what your independent variables were, along with the levels of those variables.
3. Next, you should talk about significant interactions if they are there. If not, mention that there were no significant interactions and talk about main effects (report significant and nonsignificant effects). Here are examples of the format for how to report F statistics from previous work I have done:
There was no main effect of biological sex, F(1,603)= .095,p>.05,η ̂^2= .002.
There was also a main effect of perception source, with victims reporting greater relationship violence than perpetrators, as well as a main effect of relationship duration, F(1,603)=25.95,p< .001,η ̂^2= .04 and F(4,603)=7.64,p< .001,η ̂^2= .05, respectively.
4. Here’s what the different pieces are/mean:
F(1,603) – this is denoting that you ran an F test, with the degrees of freedom for the variable on the left, and the total degrees of freedom on the right
= 25.95 – this is the value of the F test
p < .001 – this is the significance of the F test. Generally you report one of the following numbers: > .05 if the result was nonsignificant, or < .05, < .01, < .001, whichever is closest to your result.
η ̂^2= .05 – This is called “eta squared”, it is a measure of effect size. Basically, how much your variable influences the outcome.
5. If you have a significant variable with more than two levels, you should talk about those differences using post-hoc tests of pairwise comparisons. Make sure to specify which post-hoc tests you ran and how you controlled for familywise Type I error (not all tests do this automatically). Here’s an example of how to write about these from previous work:
Among perpetrators, males reported less average relationship violence than females, p < .001.
6. For the violations of the test assumptions, consider mentioning them somewhere.
Ibecause I want to know more about what these violations are/how to identify and remedy them, a search for “ANOVA test assumptions” should help.

Here’s an example of an APA format graph – you can modify the labels and font, etc, in SPSS:

Figure 1. Estimated mean ratings of perceived violence in relationships for victims by biological sex and perceived relationship duration (N =61

Interpret the results from the analyses you have performed thus far. Some of this may involve some repetition from your earlier submission of your preliminary analysis. The purpose of this submission is to demonstrate that you have moved further along in your analysis and are ready to provide an interpretation of results that are useful for your original questions in your proposal. In your previous submission, you may have encountered issues in your analysis that have hopefully by now been resolved. The paper should look pretty close to the completed paper.

This submission should include:

A brief summary of the problem you are addressing
A complete discussion of all analyses you have conducted thus far. Much of this can be taken from your earlier submission on your preliminary analysis, along with any changes you have made to your earlier analyses and any additional analyses you have conducted.
An interpretation of the results of your analyses, including:
Interpretation of any statistical results (e.g. coefficients from regression, odds rations, ANOVA tables, etc.)
Interpretation of fit statistics of the results (p-values, R-squared values, AUC, etc.)
Visualizations to help clarify the output of your analyses. These could include (but are not limited to):
Plots of dependent versus independent variables
Average values of target variables for different categories of independent variables
Heat maps
Discussion of the implications of these results.
How are they valuable in answering your original questions?
Do the results provide answers to the questions you originally asked?

Comments from Customer
This is the requirement for preliminary analyses:
Briefly discuss the problem you are addressing and the questions you are trying to answer. Much of this can be carried over from your proposal with some modification.
Discuss the data that you are using
Where did you get it?
What fields does it contain?
What does each row in the data contain
What did you have to do to get the data into a format that is ready for analysis
Discuss the types of analysis that you have run to attempt to answer your questions.
Provide some output of each analysis. This could include:
Statistical output, if a statistical output is conducted
Aggregate tables
Charts
A brief discussion of the preliminary findings
Are the analyses you have run helpful in answering your question?
Has your preliminary analysis made you re-think any of your original assumptions or the questions you are trying to answer
What is your next step? What additional analyses will you run?

Using some of the intake questions we came up with as a class in Week 2, select someone to interview briefly.
Once you have completed the interview, write a Progress Note using either the DAP/SOAP. Please see
What is it about?
A DAP (Data, Assessment, Plan) or SOAP (Subjective, Objective, Assessment, Plan) are the two industry
standards of writing notes. It is important to break down the actions and observations of an interaction with a
client versus an analysis and assessment. This helps prevent bias in the note writing and allows others who
read the note to see exactly what happened and perhaps come up with their own assessment.
The Data or Objective piece is to be written about just the observed facts. This is the section where you write
about the intention of your encounter and what is observed. You do not use assumptions in this section. So for
example, if you suspect a client is nervous (an assumption based on observations) you would write the actions
that they did to cause you to assume they were nervous. You might write that “client was sitting in the chair.
She was twitching her left foot and both of her hands at a rapid rate and was looking all around the room. She
did not make eye contact as we spoke.” This section is as if you were explaining action by action of what the
person was doing.