## 04 Nov DSRT  Infer Stats in Decision-MakingCourse 2: DSRT professional writing and professional development It is important for stud

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Course 1: DSRT  Infer Stats in Decision-Making

Course 2: DSRT professional writing and professional development

It is important for students to understand how their curriculum is applied to real world workplace environments. How what you are studying can or will benefit you in your current, or future, career. For this assignment, you will need to review your program’s curriculum and goals and compare that to your current job/internship (if unemployed, reflect on a job/career that you desire to obtain in the future).

Assignment Details:Part 1 – Identify a Problem

• Problem solving starts by being able to identify existing problems, gaps in service, inefficient systems/processes, flawed policies, or any other areas of our job/career where improvement is needed. Identify a problem or gap at your workplace and explain why/how this problem exists.

Part 2 – Solve the Problem

• Use your knowledge that you’ve learned in the program (or hope to learn in a future course) to implement a plan to solve the problem.

Please include your Program  PHDIT in the reflection

Introduction and Descriptive Statistics Read:

1, Exploring Statistics – Chapter1: Introduction

2. Exploring Statistics – Chapter 2: Frequency Distributions and Graphs

3. Exploring Statistics – Chapter 3: Central Tendency

4. Exploring Statistics – Chapter 4: Variability

5. Exploring Statistics – Chapter 5: Other Descriptive Statistics

Correlation and Regression Tests Read:

1. Exploring Statistics – Chapter 6: Correlation and Regression
2. Exploring Statistics – Chapter 7: Theoretical Distributions Including the Normal Distribution
3. Exploring Statistics – Chapter 8: Samples

Analysis of Variance (ANOVA) Read:

1. Exploring Statistics – Chapter 11: Analysis of Variance: Independent Samples

Exploring Statistics – Chapter 12: Analysis of Variance: Repeated Measures

2. Exploring Statistics – Chapter 13: Analysis of Variance: Factorial Design