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  • [Solved] math

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  • [Solved] Micro System Questions-JJ-

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    [>Algebra homework help](https://www.sweetstudy.com/fields/mathematics-algebra) I have a few questions – 2 years ago

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    [>Algebra homework help](https://www.sweetstudy.com/fields/mathematics-algebra)

    I have a few questions

    – 2 years ago

  • [Solved] Math questions

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    [>Numerical analysis homework help](https://www.sweetstudy.com/fields/mathematics-numerical-analysis) Help with 20 questions Due today!!! – 2 years ago

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    [>Numerical analysis homework help](https://www.sweetstudy.com/fields/mathematics-numerical-analysis)

    Help with 20 questions Due today!!!

    – 2 years ago

  • [Solved] one page

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics) Submit a NOT MORE THAN one page response to the following question. Your submission must be a Microsoft Word or PDF file, formatted in APA style complete with Cover and Reference (with at least one entry – the text…

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics)

    Submit a NOT MORE THAN one page response to the following question.  Your submission must be a Microsoft Word or PDF file, formatted in APA style complete with Cover and Reference (with at least one entry – the textbook) pages. Your submission will be checked for plagiarism using Turnitin.  Evidence of plagiarism will at the minimum result in an automatic '0' for the assignment. Additional penalties may be levied.  Please also see SWU policies on Academic Honesty.

    All policies of the  [Benson School of Business Writing Manual](https://swu.instructure.com/courses/1789630/pages/benson-school-of-business-writing-manual) must be observed.

    15-62. Discuss what is meant by the least squares criterion as it pertains to multiple regression analysis. Is the least squares criterion any different for simple regression analysis? Discuss.

    – 2 years ago

  • [Solved] case study 8

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics) Complete Session 8 Case Study Case 14.4 from the textbook (Continental Trucking). Submit a Word document formatted in APA style complete with cover and reference pages. The reference page must have at least one ent…

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics)

    Complete Session 8 Case Study Case 14.4 from the textbook (Continental Trucking).

    Submit a Word document formatted in APA style complete with cover and reference pages.  The reference page must have at least one entry:  the textbook.

    The submission must be a form Research Report with the following parts:

    1. **Background information about the case:**  The section must provide information about the people, places, operations, business, etc. and the research question or practical business challenges for which the current research will be used to provide a solution .  This section need not be long; up to two well written paragraphs are usually sufficient.
    2. **Descriptive Statistics and Data illustration:**  In this section, the submission must summarize how the data was obtained (see chapter 1 of the text). Graphs and charts illustrating the main features of the data are also required – please see chapter 2 of the textbook.   Finally, a table of descriptive statistics accompanied by  description and explanations of the main features of the data are also required.
    3. **Statistical Method:** This part of the submission must explain the main statistical method(s) used in the case.   What is the theory behind it? What are the working hypotheses (null and alternative, etc.), what main measures, statistics, or parameters etc.,  are to be estimated or calculated using the method; how does it measures or calculates these measures, statistics, or parameters?  Finally, this section must describe how the results of the statistical method are interpreted. That is, what is it about the results that would indicate that the working hypotheses or research questions are confirmed or rejected or perhaps need to be modified and tested again?
    4. **Results:**  The actual results of the statistical method applied to the data are now presented in this section.  The section requires the student to describe the results and their theoretical interpretation based on the description of the method described in the previous section.
    5. **Conclusion and Recommendation:**  This section restates the statistical results and applies it to the business situation in the case.  It also makes practical recommendations about what steps the business in the case can take going forward based on the results of the statistical analysis.  The application and recommendation can take one or more of these general forms.

    1. The statistical results of the data analysis provided an answer(s) to the issues that the business is facing.  As a result specific policies or practical steps can be implemented in the business.
    2.  The statistical results of the data analysis provided only partial answers.  More research and tests need to be done to come up with a more useful answers for the business situation.
    3. The statistical results did not provide any useful information for the challenges that a business(s) is facing.  Recommendations for conducting new research with new approaches, data, variables, etc. may be required.
    4. Note:  there is always room for 'further research' since it is impossible for a single research case to answer every possible question.

    1. The textbook provides narratives, questions, or actual steps/questions for each case study.  While these are useful for conducting actual research, the submission must take the form of a research report and follow the above pattern.
    2. Consistent with the emphasis of this course on interpretation, an [edited version of the answer key for the case study has been provided here](https://swu.instructure.com/courses/4178430/files/302833059?wrap=1).  Students are required to use the information to enhance their understanding of the statistical methods and the results obtained.

    – [\
    \
    Case14.4EditedAnswerKeyGuide.docx](https://www.sweetstudy.com/files/case14-4editedanswerkeyguide-docx "Case14.4EditedAnswerKeyGuide.docx")

    – 2 years ago

  • [Solved] help with math tst due in 8 hours

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    [>Algebra homework help](https://www.sweetstudy.com/fields/mathematics-algebra) attached – 2 years ago

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    [>Algebra homework help](https://www.sweetstudy.com/fields/mathematics-algebra)

    attached

    – 2 years ago

  • [Solved] 5015..Can this be done in 24 hours?..10pg

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics) – 2 years ago **files (3)** – ## cf_teen_parenting_case_study2.pdf – ## cf_teen_parenting_dataset.jasp – wk9..5015assn2.docx **## cf_teen_parenting_case_study2.pdf** \f CAPELLA UNIVERSITY Teen Parenting…

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics)

    – 2 years ago

    **files (3)**

    – ## cf_teen_parenting_case_study2.pdf

    – ## cf_teen_parenting_dataset.jasp

    – wk9..5015assn2.docx

    **## cf_teen_parenting_case_study2.pdf**

    \f CAPELLA UNIVERSITY

    Teen Parenting Case Study
    Your agency is providing eight-week parenting classes for teen parents in an urban
    environment. Services are open to both teen mothers and teen fathers, and their children can
    be any age. You get referrals from local schools, hospitals, social service agencies, and the
    Department of Children and Family Services. The interventions of the program include a weekly
    parenting class, dealing with topics such as child development, effective communication with
    children, stress management, and compassionate behavior management strategies. The
    anticipated outcomes of the program include:

    • Increased knowledge about child development.

    • Increased sense of competence in parenting.

    • Decreased reported stress levels.

    • Beliefs in line with compassionate behavior management strategies rather than using
    corporal punishment.

    1

    – Teen Parenting Case Study

    **## cf_teen_parenting_dataset.jasp**

    ## META-INF/MANIFEST.MF

    Manifest-Version: 1.0
    Created-By: JASP 0.16.3
    Data-Archive-Version: 1.0.2
    JASP-Archive-Version: 3.1

    ## metadata.json

    {
    "computedColumns" : [],
    "dataFilePath" : "C:/Users/swowr/OneDrive/Desktop/SWK5015/cf_teen_parenting_dataset.csv",
    "dataFileReadOnly" : false,
    "dataFileTimestamp" : 1664372915,
    "dataSet" :
    {
    "columnCount" : 9,
    "emptyValuesMap" :
    {
    "Age" : {},
    "CDTPost" : {},
    "CDTPre" : {},
    "Complete" : {},
    "PCSPost" : {},
    "PCSPre" : {},
    "Sex" : {},
    "TPSSPost" : {},
    "TPSSPre" : {}
    },
    "fields" :
    [
    {
    "measureType" : "Nominal",
    "name" : "Sex",
    "type" : "integer"
    },
    {
    "measureType" : "Ordinal",
    "name" : "Age",
    "type" : "integer"
    },
    {
    "measureType" : "Nominal",
    "name" : "Complete",
    "type" : "integer"
    },
    {
    "measureType" : "Continuous",
    "name" : "CDTPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "CDTPost",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "PCSPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "PCSPost",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "TPSSPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "TPSSPost",
    "type" : "number"
    }
    ],
    "filterVector" :
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    true,
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    "rowCount" : 50
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    "NaN",
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    "filterConstructorJSON" : "{\"formulas\":[]}",
    "filterData" : "# Add filters using R syntax here, see question mark for help.\n\ngeneratedFilter # by default: pass the non-R filter(s)"
    }

    ## xdata.json

    {
    "Age" :
    {
    "labels" :
    [
    [
    14,
    "14",
    true
    ],
    [
    15,
    "15",
    true
    ],
    [
    16,
    "16",
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    [
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    "orgStringValues" : null
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    "Sex" :
    {
    "labels" :
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    [
    2,
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    true
    ]
    ],
    "orgStringValues" : null
    }
    }

    ## data.bin

    ## index.html

    ## Results

    ### Paired Samples T-Test

    | Paired Samples T-Test |
    | — |
    | Measure 1 | | Measure 2 | t | df | p |
    | — | — | — | — | — | — |
    | CDTPre | | – | | CDTPost | | -6.538 | | 33 | | < .001 | |
    | PCSPre | | – | | PCSPost | | -6.295 | | 33 | | < .001 | |
    | TPSSPre | | – | | TPSSPost | | 1.345 | | 33 | | 0.188 | |
    | |
    | Note.<br> Student's t-test. |

    #### Assumption Checks

    | Test of Normality (Shapiro-Wilk) |
    | — |
    | | | | W | p |
    | — | — | — | — | — |
    | CDTPre | | – | | CDTPost | | 0.961 | | 0.256 | |
    | PCSPre | | – | | PCSPost | | 0.970 | | 0.474 | |
    | TPSSPre | | – | | TPSSPost | | 0.931 | | 0.032 | |
    | |
    | Note.<br> Significant results suggest a deviation from normality. |

    #### Descriptives

    | Descriptives |
    | — |
    | | N | Mean | SD | SE | Coefficient of variation |
    | — | — | — | — | — | — |
    | CDTPre | | 34 | | 33.971 | | 16.823 | | 2.885 | | 0.495 | |
    | CDTPost | | 34 | | 58.235 | | 24.676 | | 4.232 | | 0.424 | |
    | PCSPre | | 34 | | 17.853 | | 6.725 | | 1.153 | | 0.377 | |
    | PCSPost | | 34 | | 26.088 | | 8.043 | | 1.379 | | 0.308 | |
    | TPSSPre | | 34 | | 15.588 | | 3.870 | | 0.664 | | 0.248 | |
    | TPSSPost | | 34 | | 14.882 | | 3.780 | | 0.648 | | 0.254 | |
    | |

    ## analyses.json

    {
    "analyses" : [],
    "meta" : null
    }

    **wk9..5015assn2.docx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/wk9-5015assn2-docx)

    **wk9..5015assn2.docx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/wk9-5015assn2-docx)

    **## cf_teen_parenting_case_study2.pdf**

    \f CAPELLA UNIVERSITY

    Teen Parenting Case Study
    Your agency is providing eight-week parenting classes for teen parents in an urban
    environment. Services are open to both teen mothers and teen fathers, and their children can
    be any age. You get referrals from local schools, hospitals, social service agencies, and the
    Department of Children and Family Services. The interventions of the program include a weekly
    parenting class, dealing with topics such as child development, effective communication with
    children, stress management, and compassionate behavior management strategies. The
    anticipated outcomes of the program include:

    • Increased knowledge about child development.

    • Increased sense of competence in parenting.

    • Decreased reported stress levels.

    • Beliefs in line with compassionate behavior management strategies rather than using
    corporal punishment.

    1

    – Teen Parenting Case Study

    **## cf_teen_parenting_dataset.jasp**

    ## META-INF/MANIFEST.MF

    Manifest-Version: 1.0
    Created-By: JASP 0.16.3
    Data-Archive-Version: 1.0.2
    JASP-Archive-Version: 3.1

    ## metadata.json

    {
    "computedColumns" : [],
    "dataFilePath" : "C:/Users/swowr/OneDrive/Desktop/SWK5015/cf_teen_parenting_dataset.csv",
    "dataFileReadOnly" : false,
    "dataFileTimestamp" : 1664372915,
    "dataSet" :
    {
    "columnCount" : 9,
    "emptyValuesMap" :
    {
    "Age" : {},
    "CDTPost" : {},
    "CDTPre" : {},
    "Complete" : {},
    "PCSPost" : {},
    "PCSPre" : {},
    "Sex" : {},
    "TPSSPost" : {},
    "TPSSPre" : {}
    },
    "fields" :
    [
    {
    "measureType" : "Nominal",
    "name" : "Sex",
    "type" : "integer"
    },
    {
    "measureType" : "Ordinal",
    "name" : "Age",
    "type" : "integer"
    },
    {
    "measureType" : "Nominal",
    "name" : "Complete",
    "type" : "integer"
    },
    {
    "measureType" : "Continuous",
    "name" : "CDTPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "CDTPost",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "PCSPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "PCSPost",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "TPSSPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "TPSSPost",
    "type" : "number"
    }
    ],
    "filterVector" :
    [
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
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    true,
    true,
    true,
    true,
    true,
    true
    ],
    "rowCount" : 50
    },
    "emptyValues" :
    [
    "NaN",
    "nan",
    ".",
    "NA"
    ],
    "filterConstructorJSON" : "{\"formulas\":[]}",
    "filterData" : "# Add filters using R syntax here, see question mark for help.\n\ngeneratedFilter # by default: pass the non-R filter(s)"
    }

    ## xdata.json

    {
    "Age" :
    {
    "labels" :
    [
    [
    14,
    "14",
    true
    ],
    [
    15,
    "15",
    true
    ],
    [
    16,
    "16",
    true
    ],
    [
    17,
    "17",
    true
    ],
    [
    18,
    "18",
    true
    ]
    ],
    "orgStringValues" : null
    },
    "Complete" :
    {
    "labels" :
    [
    [
    1,
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    ],
    [
    2,
    "2",
    true
    ]
    ],
    "orgStringValues" : null
    },
    "Sex" :
    {
    "labels" :
    [
    [
    1,
    "1",
    true
    ],
    [
    2,
    "2",
    true
    ]
    ],
    "orgStringValues" : null
    }
    }

    ## data.bin

    ## index.html

    ## Results

    ### Paired Samples T-Test

    | Paired Samples T-Test |
    | — |
    | Measure 1 | | Measure 2 | t | df | p |
    | — | — | — | — | — | — |
    | CDTPre | | – | | CDTPost | | -6.538 | | 33 | | < .001 | |
    | PCSPre | | – | | PCSPost | | -6.295 | | 33 | | < .001 | |
    | TPSSPre | | – | | TPSSPost | | 1.345 | | 33 | | 0.188 | |
    | |
    | Note.<br> Student's t-test. |

    #### Assumption Checks

    | Test of Normality (Shapiro-Wilk) |
    | — |
    | | | | W | p |
    | — | — | — | — | — |
    | CDTPre | | – | | CDTPost | | 0.961 | | 0.256 | |
    | PCSPre | | – | | PCSPost | | 0.970 | | 0.474 | |
    | TPSSPre | | – | | TPSSPost | | 0.931 | | 0.032 | |
    | |
    | Note.<br> Significant results suggest a deviation from normality. |

    #### Descriptives

    | Descriptives |
    | — |
    | | N | Mean | SD | SE | Coefficient of variation |
    | — | — | — | — | — | — |
    | CDTPre | | 34 | | 33.971 | | 16.823 | | 2.885 | | 0.495 | |
    | CDTPost | | 34 | | 58.235 | | 24.676 | | 4.232 | | 0.424 | |
    | PCSPre | | 34 | | 17.853 | | 6.725 | | 1.153 | | 0.377 | |
    | PCSPost | | 34 | | 26.088 | | 8.043 | | 1.379 | | 0.308 | |
    | TPSSPre | | 34 | | 15.588 | | 3.870 | | 0.664 | | 0.248 | |
    | TPSSPost | | 34 | | 14.882 | | 3.780 | | 0.648 | | 0.254 | |
    | |

    ## analyses.json

    {
    "analyses" : [],
    "meta" : null
    }

    **wk9..5015assn2.docx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/wk9-5015assn2-docx)

    **## cf_teen_parenting_case_study2.pdf**

    \f CAPELLA UNIVERSITY

    Teen Parenting Case Study
    Your agency is providing eight-week parenting classes for teen parents in an urban
    environment. Services are open to both teen mothers and teen fathers, and their children can
    be any age. You get referrals from local schools, hospitals, social service agencies, and the
    Department of Children and Family Services. The interventions of the program include a weekly
    parenting class, dealing with topics such as child development, effective communication with
    children, stress management, and compassionate behavior management strategies. The
    anticipated outcomes of the program include:

    • Increased knowledge about child development.

    • Increased sense of competence in parenting.

    • Decreased reported stress levels.

    • Beliefs in line with compassionate behavior management strategies rather than using
    corporal punishment.

    1

    – Teen Parenting Case Study

    **## cf_teen_parenting_dataset.jasp**

    ## META-INF/MANIFEST.MF

    Manifest-Version: 1.0
    Created-By: JASP 0.16.3
    Data-Archive-Version: 1.0.2
    JASP-Archive-Version: 3.1

    ## metadata.json

    {
    "computedColumns" : [],
    "dataFilePath" : "C:/Users/swowr/OneDrive/Desktop/SWK5015/cf_teen_parenting_dataset.csv",
    "dataFileReadOnly" : false,
    "dataFileTimestamp" : 1664372915,
    "dataSet" :
    {
    "columnCount" : 9,
    "emptyValuesMap" :
    {
    "Age" : {},
    "CDTPost" : {},
    "CDTPre" : {},
    "Complete" : {},
    "PCSPost" : {},
    "PCSPre" : {},
    "Sex" : {},
    "TPSSPost" : {},
    "TPSSPre" : {}
    },
    "fields" :
    [
    {
    "measureType" : "Nominal",
    "name" : "Sex",
    "type" : "integer"
    },
    {
    "measureType" : "Ordinal",
    "name" : "Age",
    "type" : "integer"
    },
    {
    "measureType" : "Nominal",
    "name" : "Complete",
    "type" : "integer"
    },
    {
    "measureType" : "Continuous",
    "name" : "CDTPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "CDTPost",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "PCSPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "PCSPost",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "TPSSPre",
    "type" : "number"
    },
    {
    "measureType" : "Continuous",
    "name" : "TPSSPost",
    "type" : "number"
    }
    ],
    "filterVector" :
    [
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true
    ],
    "rowCount" : 50
    },
    "emptyValues" :
    [
    "NaN",
    "nan",
    ".",
    "NA"
    ],
    "filterConstructorJSON" : "{\"formulas\":[]}",
    "filterData" : "# Add filters using R syntax here, see question mark for help.\n\ngeneratedFilter # by default: pass the non-R filter(s)"
    }

    ## xdata.json

    {
    "Age" :
    {
    "labels" :
    [
    [
    14,
    "14",
    true
    ],
    [
    15,
    "15",
    true
    ],
    [
    16,
    "16",
    true
    ],
    [
    17,
    "17",
    true
    ],
    [
    18,
    "18",
    true
    ]
    ],
    "orgStringValues" : null
    },
    "Complete" :
    {
    "labels" :
    [
    [
    1,
    "1",
    true
    ],
    [
    2,
    "2",
    true
    ]
    ],
    "orgStringValues" : null
    },
    "Sex" :
    {
    "labels" :
    [
    [
    1,
    "1",
    true
    ],
    [
    2,
    "2",
    true
    ]
    ],
    "orgStringValues" : null
    }
    }

    ## data.bin

    ## index.html

    ## Results

    ### Paired Samples T-Test

    | Paired Samples T-Test |
    | — |
    | Measure 1 | | Measure 2 | t | df | p |
    | — | — | — | — | — | — |
    | CDTPre | | – | | CDTPost | | -6.538 | | 33 | | < .001 | |
    | PCSPre | | – | | PCSPost | | -6.295 | | 33 | | < .001 | |
    | TPSSPre | | – | | TPSSPost | | 1.345 | | 33 | | 0.188 | |
    | |
    | Note.<br> Student's t-test. |

    #### Assumption Checks

    | Test of Normality (Shapiro-Wilk) |
    | — |
    | | | | W | p |
    | — | — | — | — | — |
    | CDTPre | | – | | CDTPost | | 0.961 | | 0.256 | |
    | PCSPre | | – | | PCSPost | | 0.970 | | 0.474 | |
    | TPSSPre | | – | | TPSSPost | | 0.931 | | 0.032 | |
    | |
    | Note.<br> Significant results suggest a deviation from normality. |

    #### Descriptives

    | Descriptives |
    | — |
    | | N | Mean | SD | SE | Coefficient of variation |
    | — | — | — | — | — | — |
    | CDTPre | | 34 | | 33.971 | | 16.823 | | 2.885 | | 0.495 | |
    | CDTPost | | 34 | | 58.235 | | 24.676 | | 4.232 | | 0.424 | |
    | PCSPre | | 34 | | 17.853 | | 6.725 | | 1.153 | | 0.377 | |
    | PCSPost | | 34 | | 26.088 | | 8.043 | | 1.379 | | 0.308 | |
    | TPSSPre | | 34 | | 15.588 | | 3.870 | | 0.664 | | 0.248 | |
    | TPSSPost | | 34 | | 14.882 | | 3.780 | | 0.648 | | 0.254 | |
    | |

    ## analyses.json

    {
    "analyses" : [],
    "meta" : null
    }

    **wk9..5015assn2.docx**

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics) For this discussion, post something to the discussion board related to making decisions using data information systems or your Week 9 readings. Here are some ideas to get you started: – Discuss your experience with…

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