Bright Academic Experts

Category: Statistics

  • Organizing Data and Graphical Displays

    MODULE 2: ORGANIZING DATA ASSIGNMENT Questions are taken directly from Brase, Brase, Dolor, and Seibert Chapter 2, page 79. In case an eBook page number differs, the questions are listed below: Discuss each of the following topics in class or review the topics on your own. Then, write a brief but complete essay in which you summarize the main points. Please include formulas and graphs as appropriate. 1. In your own words, explain the differences among histograms, relative-frequency histograms, bar graphs, circle graphs, time-series graphs, Pareto charts, and stem-and-leaf displays. If you have nominal data, which graphic displays might be useful? What if you have ordinal, interval, or ratio data? 2. What do we mean when we say a histogram is skewed to the left? To the right? What is a bimodal histogram? Discuss the following statement: A bimodal histogram usually results if we draw a sample from two populations at once. Suppose you took a sample of weights of college football players, and with this sample, you included the weights of cheerleaders. Do you think a histogram made from the combined weights would be bimodal? Explain. 3. Discuss the statement that stem-and-leaf displays are quick and easy to construct. How can we use a stem-and-leaf display to make the construction of a frequency table easier? How does a stem-and-leaf display help you spot extreme values quickly? General Instructions As doctoral students, your assignments are expected to follow the principles of high-quality scientific standards and promote knowledge and understanding in the field of criminal justice. You should apply a rigorous and critical assessment of a body of theory and empirical research, articulating what is known about the phenomenon and ways to advance research about the topic under review. Research syntheses should identify significant variables, a systematic and reproducible search strategy, and a clear framework for studies included in the larger analysis. Assignments may be written in first person (I). All assignments should be clearly and concisely written, with technical material set off. Please do not use jargon, slang, idioms, colloquialisms, or bureaucratese. Use acronyms sparingly and spell them out the first time you use them. Please do not construct acronyms from phrases you repeat frequently in the text. Structure of Assignment Paper For purposes of this assignment, there is no layout structure required as far as the setup of this paper, with one exception. I would appreciate it if you used separate headers for question 1 and question 2. Sub-headers are also allowed but not required. Questions should strive to be no less than 500 words each with no maximum limit. I expect all papers to be in the latest APA edition, properly cited, and additional resources used besides classroom textbooks. Note: Your assignment will be checked for originality via the Turnitin plagiarism tool.

    Attached Files (PDF/DOCX): Organizing Data Assignment.docx

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  • Project one

    check attachments

    Attached Files (PDF/DOCX): MAT 240 Project One Template.docx

    Note: Content extraction from these files is restricted, please review them manually.

  • Descriptive statistics of ACT scores and related variables

    Using JASP/SSPS/excel Use one of the three options described above to complete this homework assignment. In the RSH Data Set there are seven variables. First Attempt Grade Level: Grade level student was in during his or her first ACT attempt. Composite ACT Score First Attempt: The ACT composite score for his or her first attempt. ACT Prep: 1 = student completed a semester long ACT prep course, 0 = Did not complete prep Composite ACT Score Second Attempt: The ACT composite score for his or her second attempt. Attendance: 1 = Attendance greater than 90%, 2= Attendance greater than 95% F/R Lunch: 1 = Free and Reduced Lunch Eligible, 0 = Not eligible GPA = Cumulative GPA 1. For each variable, identify the data as nominal, ordinal, interval, or ratio (3 pt): Composite ACT Score First Attempt: The ACT composite score for his or her first attempt. ACT Prep: 1 = student completed a semester long ACT prep course, 0 = Did not complete prep Composite ACT Score Second Attempt: The ACT composite score for his or her second attempt Attendance: 1 = Attendance greater than 90%, 2= Attendance greater than 95% F/R Lunch: 1 = Free and Reduced Lunch Eligible, 0 = Not eligible GPA = Cumulative GPA 2. For each variable, report the following (3 pt): Composite ACT Score First Attempt Mean (all students) Composite ACT Score First Attempt Mean for students taking ACT prep Composite ACT Score First Attempt Mean for students not taking ACT prep Composite ACT Score Second Attempt Mean (all students) Composite ACT Score Second Attempt Mean for students taking ACT prep Composite ACT Score Second Attempt Mean for students not taking ACT prep For each variable, report the following (3 pt): Composite ACT Score First Attempt Variance (all students) Composite ACT Score First Attempt Variance for students taking ACT prep Composite ACT Score First Attempt Variance for students not taking ACT prep Composite ACT Score Second Attempt Variance (all students) Composite ACT Score Second Attempt Variance for students taking ACT prep Composite ACT Score Second Attempt Variance for students not taking ACT prep. 4. For each variable, report the following (3 pt): Composite ACT Score First Attempt Skew (all students) Composite ACT Score First Attempt Skew for students taking ACT prep. Composite ACT Score First Attempt Skew for students not taking ACT prep Composite ACT Score Second Attempt Skew (all students) Composite ACT Score Second Attempt Skew for students taking ACT prep Composite ACT Score Second Attempt Skew for students not taking ACT prep. 5. For each variable, report the following (3 pt): Composite ACT Score First Attempt Kurtosis (all students) Composite ACT Score First Attempt Kurtosis for students taking ACT prep Composite ACT Score First Attempt Kurtosis for students not taking ACT prep Composite ACT Score Second Attempt Kurtosis (all students) Composite ACT Score Second Attempt Kurtosis for students taking ACT prep Composite ACT Score Second Attempt Kurtosis for students not taking ACT prep. 6. Descriptive statistics provides general information about data prior to performing inferential statistics. General information is critical because many inferential statistics maintain assumptions about the data. For example, to do an independent t-test, the data must be normally distributed and the variances for each group must be similar. Normality of data can be established by ensuring that the skew and kurtosis is between +/- 1.0. Similarity of variances can be established by ensuring that one group variance is not more than double any other group variance (5 pt). Based on establishing the assumptions of normality and equal variances: o Could composite ACT Score First Attempt be statistically compared between students who took the test prep and those that did not based upon variance, skew, and kurtosis? Explain. o Could composite ACT Score Second Attempt be statistically compared between students who took the test prep and those that did not based upon variance, skew, and kurtosis? Explain. Statistics Homework 2 part Graphs and Charts of Categorical (nominal/ordinal) and Continuous Data (interval/ratio) Categorical data does not need to be normally distributed. Assumptions of normality and variance are not considerations when comparing categorical data among groups. Categorical data might be binary, discrete, or ordinal. Analysis of binary data requires non-parametric statistics. The term non-parametric essentially means that the assumptions of normality and variance do not need to be met. Refer to page 38 and 39 for additional information regarding parametric and non-parametric statistics. First read Chapter Six, Distributions and Graphs In the RSH Data Set there are seven variables. First Attempt Grade Level: Grade level student was in during his or her first ACT attempt. Composite ACT Score First Attempt: The ACT composite score for his or her first attempt. ACT Prep: 1 = student completed a semester long ACT prep course, 0 = Did not complete prep Composite ACT Score Second Attempt: The ACT composite score for his or her second attempt. Attendance: 1 = Attendance greater than 90%, 2= Attendance greater than 95% F/R Lunch: 1 = Free and Reduced Lunch Eligible, 0 = Not eligible GPA = Cumulative GPA 1. Create an appropriately labelled histogram of composite ACT score First Attempt. (3 pt) 2. Create an appropriately labelled histogram of composite ACT score Second Attempt. (3 pt) 3. 3. Create an appropriately labelled pie chart for attendance. (3 pt) 4. Create an appropriate labelled pie chart for ACT Prep. (3 pt) 5. Create an appropriately labelled pie chart for F/R Lunch. (3 pt)

  • Optimism project

    Attached Files (PDF/DOCX): Stat Project.pdf

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  • on your laptop

    please find attached

    Requirements: 3 hours   |   .doc file

  • Statistics Question

    I have a class activity for MGMT 4543/5543.

    I need one interesting statistic shown in a clear, cool graph.

    Please send me the graph image and 23 short points explaining:

    why the graph is interesting

    why it explains the information well.

    Requirements: one hour   |   .doc file

  • Experiment project

    You are going to develop an experiment like we discussed in class. Then you will write a quasi-experiment, and then a non-experiment (retro and prospective). Begin by writing your experiment below, then continue with the quasi and non-experiments on the following pages. You can use the same topic or a different topic for each, but they must be new and not used during class.

    Experimental Project Title:

    Description of your Study:

    List the three main components to an experiment and clearly write about them. Ensure I know what you are describing, so dont simply use bullets.

    Independent variable(s): ______________________________________________________

    Dependent variable(s):________________________________________________________

    Extraneous (Confounding) variable(s): ______________________________________________________

    Quasi-Experimental Project Title:

    Description of your Study. Describe your study and what is different compared to the experiment. Why does that make it a quasi-experiment?

    Independent variable(s): ______________________________________________________

    Dependent variable(s):________________________________________________________

    Extraneous (Confounding) variable(s): ______________________________________________________

    Non-Experimental Project Title:

    Description of your Study. One is Retrospective and one is Prospective (give an example of each, in detail).

    Independent variable(s): ______________________________________________________

    Dependent variable(s):________________________________________________________

    Extraneous (Confounding) variable(s): ______________________________________________________

    Attached Files (PDF/DOCX): week 6 Assignment Example.docx

    Note: Content extraction from these files is restricted, please review them manually.

  • Undefined

    Sample dataset to be completed

  • Data Types and Data Collection

    Use the research question you came up with in the previous class

    Requirements: 2hours

  • Statistics Question

    The question requirement to provided ( plagiarism+ AI ) report for All of the file

    The two plagiarism files required for All of the file

    • (File one) Turnitin plagiarism report for All the file must less than 15%)
    • (File two) AI plagiarism report for All of the file must zero percentage)
    • File three ( copy and paste any original paragraph you take the information from it in separate file )

    The question : I need to do two question the

    I attached the file excel need to correct them to do all variable measure do it quantitatively like I explain the point need to correct them to make the table quantitatively make sure all data available and public comply with IRB approval department then second make in word file each company separate table in separate page do the table like the same table in I attached excel file but make table separate for each company then under the table for each company write the page number for each variable reference number from original link and write small paragraph under each company how you collected the data

    Example Capex page 6 paragraph number Ai page 2 paragraph number cloud page 8 paragraph number .for all variable

    Here is your strict 7-point checklist to make your Excel file 100% quantitative and ready for statistical analysis.

    • Standardize Financial Units (The “M” vs. “B” Rule)
      • Action: Choose one unit (e.g., Millions) and convert everything.
      • Correction: Change 10.3 B to 10300. Change 4820 (if raw) to 4.82.
      • Why: You cannot mathematically subtract “Billions” from “Millions” unless they are in the same magnitude.
    • Strip All Non-Numeric Characters
      • Action: Use “Find & Replace” to remove all $, ,, M, B, and ~.
      • Correction: Change $3,671 M to 3671.
      • Why: Statistical software sees “$3,671” as text (like a word), not a number.
    • Convert Tech Columns to Binary (Dummy Variables)
      • Action: For columns ERP, Cloud, AI, IoT, Automation.
      • Correction: Replace “High”, “Medium”, “Yes”, or “Core Business” with 1. Replace “No”, “N/A”, or “None” with 0.
      • Why: You cannot calculate the average of “High” and “Medium”. You can calculate the frequency of “1”.
    • Quantify “Breadth” (Text to Integer)
      • Action: Delete words like “High”, “Diverse”, or “Focused”.
      • Correction: Replace them with the actual count of technologies found for that firm (e.g., change “High” to 7 if they have 7 techs).
      • Why: A regression model needs a continuous integer (0, 1, 2… 10) to measure correlation.
    • Clean “IT Intangibles” & “CAPEX”
      • Action: Remove all explanatory text.
      • Correction: Delete sentences like “Internally-developed software capitalized as…”. Keep only the number. If the number is not explicitly stated, leave the cell empty (do not write “Not isolated”).
      • Why: Text in a numeric column causes errors in calculation formulas.
    • Standardize Missing Data (Null Handling)
      • Action: Decide on a code for missing data.
      • Correction: If a company doesn’t report a number, leave it completely blank or use a specific code like -99. Do not use 0 unless the value is actually zero (e.g., zero debt).
      • Why: A blank cell is treated as “missing” by software; a 0 skews your average downwards.
    • Verify with the “Sum Test”
      • Action: At the bottom of every column (Revenue, Breadth, AI), try to calculate a =SUM() or =AVERAGE().
      • Correction: If Excel gives you an error (#VALUE!) or the count looks wrong, you still have text hidden in that column. Fix it until the formula works.

    Second question:

    I need to answer this question in separate file based in your answer and file attached name chapter 1-3

    • Explain how you will collect secondary data and its sources………
    • You need to list the names of data sources and their URLs……..
    • You need to list the names of data sources and their URLs….. and explain which type of data you will collect based on the conceptual model quantitatively I attached file name chapter 1-3 the have inside the conceptual model

    Data source

    Appendix A: Public Data Sources and Access Links
    a. SEC EDGAR Search (Forms 10-K, 10-Q, 8-K; ):

    b. SEC EDGAR Company Browse (issuer filing history):

    c. SEC Search Filings Hub (EDGAR tools directory):

    d. SEC EDGAR Search Assistance (user guide):

    e. U.S. Bureau of Economic Analysis (BEA) industry/macro series (contextual controls):

    f. Federal Reserve Economic Data (FRED) macro series (contextual controls):

    g. Texas Comptroller of Public Accounts economic data (state-level context where needed):

    h. AnnualReports.com (public access to company annual report PDFs where available):

    Appendix B: SEC EDGAR Filing Links for the 40 Sampled Firms (Directly Verifiable Public Access)
    These issuer-specific EDGAR links provide a direct, public route to each firms 10-K/10-Q/8-K filings used to extract all .

    i. Texas Instruments Inc. (TXN) SEC filings: .

    j. Dell Technologies Inc. (DELL) SEC filings: .

    k. Flex Ltd. (FLEX) SEC filings: .

    l. Cirrus Logic Inc. (CRUS) SEC filings: .

    m. Silicon Laboratories Inc. (SLAB) SEC filings:
    .
    n. NXP Semiconductors USA Inc. (NXPI) SEC filings: .

    o. Diodes Incorporated (DIOD) SEC filings:

    p. Qorvo Inc. (QRVO) SEC filings: .

    q. Leidos Holdings Inc. (LDOS) SEC filings: .

    r. Lockheed Martin Aeronautics (LMT) SEC filings:

    s. L3Harris Technologies ISR (LHX) SEC filings:

    t. Raytheon Technologies (RTX) SEC filings:

    u. Bell Textron Inc. (TXT) SEC filings: .

    v. Trinity Industries Inc. (TRN) SEC filings:

    .
    w. TechnipFMC (FTI) SEC filings:

    x. National Oilwell Varco (NOV) SEC filings:

    y. Schlumberger Ltd. (SLB) SEC filings: .

    z. Baker Hughes Company (BKR) SEC filings:

    aa. Phillips 66 (PSX) SEC filings:

    ab. HF Sinclair Corporation (DINO) SEC filings:

    ac. ConocoPhillips (COP) SEC filings:

    ad. Oceaneering International Inc. (OII) SEC filings: .

    ae. Nabors Industries Ltd. (NBR) SEC filings: .

    af. Flowserve Corporation (FLS) SEC filings:

    ag. Caterpillar Inc. (CAT) SEC filings: .

    ah. Lennox International Inc. (LII) SEC filings: .

    ai. IES Holdings Inc. (IESC) SEC filings: .

    aj. Heidelberg Materials North America (HEI) SEC filings:

    ak. Daikin Comfort Technologies (DAIKIN) SEC filings:

    al. Commercial Metals Company (CMC) SEC filings:

    am. CompX International Inc. (CIX) SEC filings: .

    an. Air Products and Chemicals Inc. (APD) SEC filings: .

    ao. Air Liquide USA LLC (AIQUY) SEC filings:

    ap. Kimberly-Clark Corporation (KMB) SEC filings:

    aq. McKesson Corporation (MCK) SEC filings:

    ar. Keurig Dr Pepper Inc. (KDP) SEC filings:

    .
    as. Fossil Group Inc. (FOSL) SEC filings: .

    at. Tandy Leather Factory Inc. (TLF) SEC filings: .

    au. Flowtrend Inc. (FLOW) SEC filings: .

    av. Thermon Group Holdings Inc. (THR) SEC filings:

    Requirements: do and cover all instruction