Something you should know:

Evidence that you will pose the question

Read the links carefully and use the material to answer the question

Screencapture to create your own table

Understand the reading and communicate

Start with an intro not too long, restate the question, why is this an important question, explain what the essay will talk about

Then follow what ur intro said, my essay first examine this…

Be straightforward

Brief conclusion

2-3 tables or charts, but have to cite it, like this is from here

Citation with author’s name and date

Using the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the spreadsheet (both found in the Supporting Materials section) for your statistical analysis.

Note: Present your data in a clearly labeled table and using clearly labeled graphs.

Specifically, include the following in your report:

Introduction

1. Describe the report: Give a brief description of the purpose of your report.
2. Define the question your report is trying to answer.
3. Explain when using linear regression is most appropriate.
4. When using linear regression, what would you expect the scatterplot to look like?
5. Explain the difference between predictor (x) and response (y) variables in a linear regression to justify the selection of variables.

Data Collection

1. Sampling the data: Select a random sample of 50 houses. Describe how you obtained your sample data (provide Excel formulas as appropriate).
2. Identify your predictor and response variables.
3. Scatterplot: Create a scatterplot of your predictor and response variables to ensure they are appropriate for developing a linear model.

Data Analysis

1. Histogram: Create a histogram for each of the two variables.
2. Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.
3. Interpret the graphs and statistics:
4. Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for house sales and square footage.
5. Compare and contrast the center, shape, spread, and any unusual characteristic for your sample of house sales with the national population (under Supporting Materials, see the National Summary Statistics and Graphs House Listing Price by Region PDF). Determine whether your sample is representative of national housing market sales.

Develop Your Regression Model

1. Scatterplot: Provide a scatterplot of the variables with a line of best fit and regression equation.
2. Based on your scatterplot, explain if a regression model is appropriate.
3. Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.
4. Identify any possible outliers or influential points and discuss their effect on the correlation.
5. Discuss keeping or removing outlier data points and what impact your decision would have on your model.
6. Calculate r: Calculate the correlation coefficient (r).
7. Explain how the r value you calculated supports what you noticed in your scatterplot.

Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.

1. Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.
2. Interpret regression equation: Interpret the slope and intercept in context. For example, answer the questions: what does the slope represent in this situation? What does the intercept represent? Revisit the Scenario above.
3. Strength of the equation: Provide and interpret R-squared.
4. Determine the strength of the linear regression equation you developed.
5. Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the assumed square footage of your home at 1500 square feet.

Conclusions

1. Summarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.
2. Did you see the results you expected, or was anything different from your expectations or experiences?
3. What changes could support different results, or help to solve a different problem?
4. Provide at least one question that would be interesting for follow-up research.

CJ 675 Module One Journal Guidelines and Rubric

Overview

In this assignment, you will consider how you would approach decision making within a criminal justice agency. To complete this assignment, you must first complete the assigned textbook reading for this module.

Prompt

Imagine that your supervisor approaches you and asks you to explain why there has been an increase in community complaints about your criminal justice agency and what can be done to reduce the number of complaints being received.

Prepare a journal assignment that thoroughly addresses the following.

• What are the differences between qualitative and quantitative research methods?
• How would you analyze the problem (qualitatively and quantitatively)
• Justify your selection of an individual method found within qualitative or quantitative research that you would use to obtain data to address the issue.
• Provide a reasonable solution based on analysis.

Question 1 : How many observations and variables are in the table?

Question 4:

Question: 6 https://www.openintro.org/book/ims/

Question 7: https://www.openintro.org/book/ims/

First, read CHAPTER 7 Percentiles and Percentile Ranks from An Introduction to Statistical Reasoning in Quantitative Research Percentiles and percentile ranks are measurement of position within a reference group. Chances are that you scored above the 85th percentile of your high school graduating class. Lets say, for arguments sake, that you were listed as scoring at the 90th percentile. What does that mean In the RSH Data Set, look specifically at the variable Composite ACT Score Second Attempt. Determine the frequency of each ACT score. This can be done by viewing the number counts from a histogram. Report the frequency of each ACT second attempt score in the data set. (5 pt) Create a cumulative frequency table consisting of each X value (ACT score). Reference p. 66 as needed. (4 pt) What is the percentile rank for students scoring 23 on the ACT second attempt? (3 pt) What is the percentile rank for students scoring 27 on the ACT second attempt? (3 pts) Statistics Homework Five (15 pt) The Concept of Standard Scores. On page 95-99 of the required text, there is a section called the concept of standard scores. An example of a standard score is the Z-score. Z-scores are standard score translations of raw scores often used when interpreting a score within a distribution of scores (p. 96). In the RSH Data Set, look specifically at the variables Composite ACT Score First Attempt and Composite ACT Score Second Attempt. For each ACT datum point, calculate and report the respective Z-score (5 pt). What is the percentile rank for an individual with a Composite ACT First Attempt Score of 24? Use the Z table for the area under the curve on page 181 from the text (5 pt) What is the percentile rank for an individual with a Composite ACT Second Attempt Score of 24? Use the Z table for the area under the curve on page 181 from the text (5 pt)

After attending this week’s live session or watching the recording, click on the Week 3 Participation link to submit the answer to the question posed during session:

What is the answer to the question I asked during this week’s live session?