Student Topics and Questions

Humanities

Topic: Cultural Studies/Diversity

Education Research

Question 2: How does race/ethnicity and economic status affect college graduation rates?

Above Is The Subject I Chose For This Assignment.

The purpose of the Signature Assignment is to have you work with real-life data to answer a real-life question using the tools, technology, and skills of MTH/219. In Week 2, you will work with the data you collected to analyze the data to draw conclusions.

Input your data into Microsoft^® Excel^® (data and years).

Create a scatterplot.

Insert a linear trendline. Make sure to show the equation and the R-square value. Also, make sure you label each axis and give a title to your graph. If you need help creating your visual, watch How to create scatterplot with trendline in Excel^®.

In the same Microsoft^® Excel^® worksheet, answer the following questions. Each question should be 90 to 175 words.

1. Does the line fit the data? How can you tell?
2. What does the slope of the line mean in your real-life data? How can you interpret the slope of your line?
3. What does the y-intercept mean in your real-life data? How can you interpret the y-intercept?

Save and upload your Excel^® file, including the graphic and your answer to each question.

Your assignment will be graded with the Week 2 Signature Assignment Grading Guide.

Below Signature Assignment Grading Guide Foe Week 2

Week 2: Grading Guide for Signature Assingment: Scatterplot and Regression

Content (70%)	Points Earned:
All key elements of the assignment are covered in a substantive way. Major points are stated clearly and are supported by specific details, examples, or analysis. Scatterplot and trendline are present on graphic. Inputyour data into Microsoft®Excel®(5%) …data should have at least 10 data points. (3%) …top row of data is title or topic (3%) …source of data is listed (see Mechanics below) Create a scatter plot (4%) …with a linear trendline (4%) …show the equation and the R-square value (4%) …create a title on the scatter plot (2%) Create a text box within the Excel spreadsheet to answer the following three questions (3%) Question #1 – explain how data fits the model (7%) …use R-square value in that best fit explanation (7%) Question #2 – explain the concept of slope …as your data applies to slope in the “real world” (7%) …apply the regression equation’s slope(7%) Question #3 – should explain the concept of y-intercept …as y-intercept applies in the “real world” (7%) …apply the regression equation’s y-intercept (7%)	Comments:
Mechanics (30%)	Points Earned:
The spreadsheet, including tables and graphs, is consistent with APA formatting guidelines and meets course-level requirements. Intellectual property is recognized with in-text citations and a reference page. (7%) Rules of spelling, grammar usage, and punctuation are followed. Sentences are complete, clear, concise, and varied.	Comments:
Total Point Earned:

I Have Also Included Some Additional Information Provided By Instructor For Research;

Week 2: Input your data into a Microsoft® Excel® spreadsheet. Create a scatter plot from that data, and insert a linear trendline. Answer three specific questions listed in the syllabus.

What is a Scatter Plot? The Math 219 course textbook defines a scatter plot as “data presented in a visual form as a set of points” (p. 260).

For more information about scatter plots,
1. Be sure you have downloaded the entire Math 219 textbook into Vital Source. Then do a Vital Source search (not a Ctrl+F), for scatter plot. You will find information about scatter plots in Chapters 3, 4, and 12. 2. Study the Math Is Fun website for excellent information about scatter plots: https://www.mathisfun.com/data/scatter-xy-plots.ht… 3. Visit the Khan Academy website for lessons and practice on “Creating and Interpreting Scatterplots”. At Khan Academy, videos are marked with a  and practice exercises are marked with a  https://www.khanacademy.org/math/probability/scatt…

What is Regression Analysis? Math 219 is not a statistics class; however, a basic understanding of statistics is necessary to complete the Signature Assignment.
The Math 219 course textbook defines a regression line as “a line that best fits the data points in a scatter plot” (p. 261). When we do regression analysis, we calculate the regression line (also called a trend line or a line of best fit). We also calculate the equation of this line (also called a model of the data), and we find two values:
1. Correlation coefficient (r). Measures the strength and direction of a linear relationship. The value of r is between -1 and +1. If r is greater than 0.8, it is generally described as a strong correlation. A value of less than 0.5 for r is generally described as a weak correlation. The values of 0.8 and 0.5 are general and may vary depending on the type of data.

2. Coefficient of determination (R2). Measures the amount of variance or how well the regression line represents the data. The value of R2 gives the percentage of the data that is closest to the regression line. This percentage is written in decimal form, such as R2 = 0.823, which is 82.3%. Obviously, the higher the percentage, the better the data fits the line.
For more information about regression,
1. Be sure you have downloaded the entire Math 219 textbook into Vital Source. Then do a Vital Source search (not a Ctrl+F), for regression. You will find information about regression in Chapters 3, 11, and 12. 2. Study the Math Is Fun website for excellent information about scatter plots: https://www.mathisfun.com/data/correlation.html 3. Visit the Khan Academy website for lessons and practice on “Estimating with Trend Lines”. At Khan Academy, videos are marked with a  and practice exercises are marked with a  https://www.khanacademy.org/math/probability/scatt…

Interpolation and Extrapolation We use scatter plots and regression analysis to study data and make decisions. The regression line is extremely helpful to determine missing data and predict future data. We must be aware of the shape of the trend line and the sample size when making predictions. If the trend line is nonlinear or if the amount of data (sample size) is small, our predictions are limited or even inaccurate.
Interpolation is using existing data to estimate missing data. For example, if we have data from 1pm, 2pm, 4pm, 5pm, and 6pm, and if that data forms a straight trend line with a slope of six (in other words, the results are six more for each hour). We can use interpolation to find the data from 3pm. Depending on the r and R2 values, we can be reasonably confident on the interpolated data.
Extrapolation is using existing data to estimate future data. For example, if we have temperatures from the hours between 1pm and 6pm, we could use that data to predict the temperature at 10pm. Extrapolation uses the model (the equation) we create when doing regression analysis on the data. For example, if the temperatures from the hours between 1pm and 6pm form the linear equation = −2 + 40, where y is the temperature and x is the hour, we could extrapolate the temperature at 10pm. You can see from the calculations below that the temperature at 10pm will be 20 degrees.
= −2 + 40 10 = −2(10) + 40 = −20 + 40 = 20
Correlation Is Not Causation From the Math is Fun website, we are cautioned that correlation is not causation. There may be a strong relationship between data, but that does not indicate causation. “Correlation Is Not Causation … which says that a correlation does not mean that one thing causes the other (there could be other reasons the data has a good correlation).” https://www.mathsisfun.com/data/correlation.html The Math is Fun website shows data of ice cream sales and shoe sales that have a very strong correlation. That does not prove that higher ice cream sales cause more shoe sales (or vice versa).