Respond to one of the following in a minimum of 175 words:
Option A· Pick a topic you would like to research. Explain one variable you could measure in each of the four scales of measurement. These may all be demographic information if that fits your topic.
Option B· Explain what significance tells you in a way a that anyone could understand. What are the levels of significance most commonly seen? What is the most common level of significance used in psychological research?
PART2– ASSIGNMENT AND ITS RESOUREC ARE ATTACHED BELOW
PART3-
- The Statistics Project assignment has been broken down into multiple parts and you will complete these parts throughout the course. This week’s assignment allows you to become familiar with opening data and viewing it in Microsoft® Excel® and using the Analysis Toolpak. In research, the individual data points are entered into databases, but for the purpose of this course, the data is provided in a spreadsheet for you in the Happiness and Engagement Dataset.
Imagine you have been asked to enhance workplace happiness and engagement at your company. You have conducted a survey and gathered data on the gender, age, relationship with direct supervisor, telecommute schedule, relationship with coworkers, along with the ratings for workplace happiness and workplace engagement for 50 individuals in your department. You must determine what variables affect workplace happiness and engagement. The first step is to run descriptive statistics on each variable to learn more about the data you have collected.
Calculate descriptive statistics for the following variables in the provided Microsoft® Excel® dataset:- Gender
- Age
- Relationship with Direct Supervisor
- Telecommute Schedule
- Relationship with Coworkers
- Workplace Happiness Rating
- Workplace Engagement Rating
- Write a 125- to 175-word summary of your interpretation of the descriptive results for each variable. Copy and paste the Microsoft® Excel® output below your summary.
Format your summary according to APA guidelines.
Submit your Assignment.
Note: Frequency tables are best used for nominal and ordinal variables. Other descriptive information (e.g., means and standard deviations) are best used with interval and ratio data.
PART4-ASSIGNMENT IS ATTACHED BELOW. I ONLY HAVE TO DO THE PART THATS HIGHLIGHTED, WHICH IS SECTION 3.
CHAPTER 4
Probability and Hypothesis Testing
Module 7: Probability
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
• Understand how probability is used in everyday life.
• Know how to compute a probability.
• Understand and be able to apply the multiplication rule.
• Understand and be able to apply the addition rule.
• Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court case of The People v. Collins, 1968. In this case, the robbery victim was unable to identify his assailant. All that the victim could recall was that the assailant was female with a blonde pony tail. In addition, he remembered that she fled the scene in a yellow convertible that was driven by an African American male who had a full beard. The suspect in the case fit the description given by the victim, so the question was “Could the jury be sure, beyond a reasonable doubt, that the woman on trial was the robber?” The evidence against her was as follows: She was blonde and often wore her hair in a pony tail; her codefendant friend was an African American male with a moustache, beard, and a yellow convertible. The attorney for the defense stressed the fact that the victim could not identify this woman as the woman who robbed him, and that therefore there should be reasonable doubt on the part of the jury.
The prosecutor, on the other hand, called an expert in probability theory who testified to the following: The probability of all of the above conditions (being blonde and often having a pony tail and having an African American male friend and his having a full beard, and his owning a yellow convertible) co-occurring when these characteristics are independent was 1 in 12 million. The expert further testified that the combination of characteristics was so unusual that the jury could in fact be certain “beyond a reasonable doubt” that the woman was the robber. The jury returned a verdict of “guilty” (Arkes & Hammond, 1986; Halpern, 1996).
As can be seen in the previous example, the legal system operates on probability and recognizes that we can never be absolutely certain when deciding whether an individual is guilty. Thus, the standard of “beyond a reasonable doubt” was established and jurors base their decisions on probability, whether they realize it or not. Most decisions that we make on a daily basis are, in fact, based on probabilities. Diagnoses made by doctors, verdicts produced by juries, decisions made by business executives regarding expansion and what products to carry, decisions regarding whether individuals are admitted to colleges, and most everyday decisions all involve using probability. In addition, all games of chance (for example, cards, horse racing, the stock market) involve probability.
If you think about it, there is very little in life that is certain. Therefore, most of our decisions are probabilistic and having a better understanding of probability will help you with those decisions. In addition, because probability also plays an important role in science, that is another important reason for us to have an understanding of it. As we will see in later modules, the laws of probability are critical in the interpretation of research findings.
Basic Probability Concepts
Probability refers to the number of ways a particular outcome (event) can occur divided by the total number of outcomes (events). (Please note that the words outcome and event will be used interchangeably in this module.) Probabilities are often presented or expressed as proportions. Proportions vary between 0.0 and 1.0, where a probability of 0.0 means the event certainly will not occur and a probability of 1.0 means that the event is certain to occur. Thus, any probability between 0.0 and 1.0 represents an event with some degree of uncertainty to it. How much uncertainty depends on the exact probability with which we are dealing. For example, a probability close to 0.0 represents an event that is almost certain not to occur, and a probability close to 1.0 represents an event that is almost certain to occur. On the other hand, a probability of .50 represents maximum uncertainty. In addition, keep in mind that probabilities tell us about the likelihood of events in the long run, not the short run.
Let’s start with a simplistic example of probability. What is the probability of getting a “head” when tossing a coin? In this example, we have to consider how many ways there are to get a “head” on a coin toss (there is only one way, the coin lands heads up) and how many possible outcomes there are (there are two possible outcomes, either a “head” or a “tail”). So, the probability of a “head” in a coin toss is:
p(head)=NumberofwaystogetaheadNumberofpossibleoutcomes=12=.50p(head)=Number of ways to get a headNumber of possible outcomes=12=
This means that in the long run, we can expect a coin to land heads up 50% of the time.
Let’s consider some other examples. How likely would it be for an individual to roll a 2 in one roll of a die? Once again, let’s put this into basic probability terms. There is only one way to roll a 2, the die lands with the 2 side up. How many possible outcomes are there in a single roll of a die? There are six possible outcomes (any number between 1 and 6 could appear on the die). Hence, the probability of rolling a 2 on a single roll of a die would be 1/6, or about .17. Representing this in a formula as we did for the previous example: