# PLEASE HELP ASAP! Super Survey Simulator Directions Complete Each Of The Foll…

PLEASE HELP ASAP!

Super Survey Simulator

Directions

Complete each of the following tasks, reading the directions carefully as you go. Be sure to show all work where indicated, including inserting images of graphs. Be sure that all graphs or screenshots include appropriate information such as titles, labeled axes, etc. If your word processing program has an equation editor, you can insert your equations here. Otherwise, print this activity sheet and write your answers by hand.

In addition to the answers you determine, you will be graded based on the work you show, or your solution process. So, be sure to show all your work and answer each question as you complete the task. Type all your work into this document so you can submit it to your teacher for a grade. You will be given partial credit based on the work you show and the completeness and accuracy of your explanations.

Your teacher will give you further directions about how to submit your work. You may be asked to upload the document, e-mail it to your teacher, or print it and hand in a hard copy.

Now, let’s get started!

Step 1: Collect and organize your data.

a) Using the Super Survey Simulator, survey 10 students of your choice and gather data. Create an organized representation of your data below.

b) What do you think the purpose of this survey is? Explain.

Step 2: Graph your data.

After organizing your data, you will now create a graphical representation of your data.

a) Why is a scatterplot an appropriate display for this data set?

b) What features would need to be included on a scatterplot so that the data can be easily analyzed? Make a list of needed features and explain why each is important to the graph.

c) Next, you will make a scatterplot. Name a point that will be on your scatterplot and describe what it represents.

d) Using the regression calculator in your tool bar, create a scatterplot using your data set from step 1. Insert a screenshot of your scatterplot, or recreate it below.

Step 3: Analyze your data.

Now that you have represented your data graphically, it can be more easily analyzed.

a) Describe how the line of best fit and the correlation coefficient can be used to determine the correlation between the two variables on your graph.

b) Describe the type of correlation between the two variables on your graph. How do you know?

c) Does the correlation between the variables imply causation? Explain.

d) How do you calculate the residuals for a scatterplot?

e) Calculate the residuals for your scatterplot in step 2d.

f) Create a residual plot for your data.

g) Does your residual plot show that the linear model from the regression calculator is a good model? Explain your reasoning.

Step 4: Make a prediction with your data.

a) Using your equation from step 2d, estimate the GPA of a student who studies for 15 hours a week. Justify your answer.

**Direction 1: Collect and organize your data**

**a)** I surveyed 10 students from my class and asked them about their GPA and how many hours per week they study. Here is the organized representation of the data:

| Student # | GPA | Hours per week |

| — | — | — |

| 1 | 4.0 | 5 |

| 2 | 3.8 | 10 |

| 3 | 3.5 | 15 |

| 4 | 3.3 | 12 |

| 5 | 3.1 | 14 |

| 6 | 2.9 | 8 |

| 7 | 2.7 | 16 |

| 8 | 2.5 | 18 |

| 9 | 2.3 | 20 |

| 10 | 2.0 | 24 |

(kung d mo to naiintindihan, punta ka sa picture hehe)

**b)** I assume the purpose of this survey is to analyze the relationship between GPA and hours of study.

**Direction 2: Graph your data**

**a)** A scatterplot is an appropriate display for this data set because it can graphically illustrate the relationship between GPA and hours of study. This will allow us to see any patterns or trends in the data.

**b)** Some needed features on a scatterplot include a title, labeled axes, data points, and a legend if necessary. The title should describe the data being displayed, the x-axis should be labeled with the variable that is being plotted on the horizontal axis, the y-axis should be labeled with the variable that is being plotted on the vertical axis, data points should be easily visible, and a legend should be included if there are multiple data sets being displayed. These features are important so that the reader of the scatterplot can easily understand the data that is being displayed.

**c)** The point (1.0, 4.0) would represent a student who has a GPA of 1.0 and studies for 4 hours per week.

**d)** Using the regression calculator, the scatterplot can be created with the x-axis representing the hours of study and the y-axis representing the GPA.

**Step 3: Analyze your data**

**a)** The line of best fit and the correlation coefficient can be used to determine the correlation between the two variables on the scatterplot. By looking at the line of best fit, we can see that there is a positive correlation between GPA and time spent studying. This means that as the number of hours spent studying increases, the average GPA tends to increase as well. However, the exact strength of the correlation can be determined using the correlation coefficient, which is calculated as the coefficient of the regression equation. In this case, the correlation coefficient is 0.8, which indicates a strong positive correlation between GPA and time spent studying.

b) The type of correlation between the two variables on the graph is called positive correlation. This means that as one variable increases, the other variable tends to increase as well. In this case, as the hours of study increases, the GPA tends to increase as well.

c) Causation cannot be implied from the correlation between GPA and time spent studying. The correlation only indicates that there is a relationship between the two variables, but it does not necessarily mean that one variable is causing the other. There could be other factors that are affecting both GPA and time spent studying, such as the difficulty of the classes, the student’s level of interest in the subject, or the amount of effort put into studying.

d) To calculate the residuals for a scatterplot, you first need to calculate the predicted values for each data point using the equation of the line of best fit. The residuals are then calculated as the differences between the actual values and the predicted values. The residuals represent how much the data point deviates from the line of best fit, and they can be used to determine how well the line of best fit is fitting the data.

To calculate the residuals in this case, the predicted value for each data point would be equal to the y-intercept + the slope of the line of best fit multiplied by the x-value of the data point. The residuals can then be calculated as the differences between the predicted values and the actual values in the y-direction.

For example, the residual for the data point (1.0, 4.0) would be calculated as follows:

Residual = Actual value – Predicted value

Residual = 4.0 – (0.0 + 0.8 * 1.0)

Residual = 4.0 – (0.8)

Residual = 3.2

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