Curve-fitting Project - Linear Model (due at the end of Week 5)InstructionsFor this assignment, collect data exhibiting a relatively linear trend, find the line of best fit, plot the data and the line, interpret the slope, and use the linear equation to make a prediction. Also, find r2 (coefficient of determination) and r (correlation coefficient). Discuss your findings. Your topic may be that is related to sports, your work, a hobby, or something you find interesting. If you choose, you may use the suggestions described below. A Linear Model Example and Technology Tips are provided in separate documents.Tasks for Linear Regression Model (LR)(LR-1) Describe your topic, provide your data, and cite your source. Collect at least 8 data points. Label appropriately. (Highly recommended: Post this information in the Linear Model Project discussion as well as in your completed project. Include a brief informative description in the title of your posting. Each student must use different data.) The idea with the discussion posting is two-fold: (1) To share your interesting project idea with your classmates, and (2) To give me a chance to give you a brief thumbs-up or thumbs-down about your proposed topic and data. Sometimes students get off on the wrong foot or misunderstand the intent of the project, and your posting provides an opportunity for some feedback. Remark: Students may choose similar topics, but must have different data sets. For example, several students may be interested in a particular Olympic sport, and that is fine, but they must collect different data, perhaps from different events or different gender.(LR-2) Plot the points (x, y) to obtain a scatterplot. Use an appropriate scale on the horizontal and vertical axes and be sure to label carefully. Visually judge whether the data points exhibit a relatively linear trend. (If so, proceed. If not, try a different topic or data set.)(LR-3) Find the line of best fit (regression line) and graph it on the scatterplot. State the equation of the line.(LR-4) State the slope of the line of best fit. Carefully interpret the meaning of the slope in a sentence or two.(LR-5) Find and state the value of r2, the coefficient of determination, and r, the correlation coefficient. Discuss your findings in a few sentences. Is r positive or negative? Why? Is a line a good curve to fit to this data? Why or why not? Is the linear relationship very strong, moderately strong, weak, or nonexistent?(LR-6) Choose a value of interest and use the line of best fit to make an estimate or prediction. Show calculation work.(LR-7) Write a brief narrative of a paragraph or two. Summarize your findings and be sure to mention any aspect of the linear model project (topic, data, scatterplot, line, r, or estimate, etc.) that you found particularly important or interesting. You may submit all of your project in one document or a combination of documents, which may consist of word processing documents or spreadsheets or scanned handwritten work, provided it is clearly labeled where each task can be found. Be sure to include your name. Projects are graded on the basis of completeness, correctness, ease in locating all of the checklist items, and strength of the narrative portions.Here are some possible topics: Choose an Olympic sport -- an event that interests you. Go to https://www.olympic.org/olympic-results and collect data for winners in the event for at least 8 Olympic games (dating back to at least 1980). (Example: Winning times in Men's 400 m dash). Make a quick plot for yourself to "eyeball" whether the data points exhibit a relatively linear trend. (If so, proceed. If not, try a different event.) After you find the line of best fit, use your line to make a prediction for the next Olympics (2014 for a winter event, 2016 for a summer event ).Choose a particular type of food. (Examples: Fish sandwich at fast-food chains, cheese pizza, breakfast cereal) For at least 8 brands, look up the fat content and the associated calorie total per serving. Make a quick plot for yourself to "eyeball" whether the data exhibit a relatively linear trend. (If so, proceed. If not, try a different type of food.) After you find the line of best fit, use your line to make a prediction corresponding to a fat amount not occurring in your data set.) Alternative: Look up carbohydrate content and associated calorie total per serving.Choose a sport that particularly interests you and find two variables that may exhibit a linear relationship. For instance, for each team for a particular season in baseball, find the total runs scored and the number of wins. Excellent websites: http://www.databasesports.com/ and http://www.baseball-reference.com/

Variables Math: Math SAT score Verbal: Verbal SAT score Credits: Number of credits the student is registered for Year: Year in college (1=Freshman, 2=Sophomore, 3=Junior, 4=Senior) Exercise: Time (in minutes) spent exercising in a typical day Sleep: Time (in hours) spent sleeping in a typical day Veg: Are you a vegetarian (yes, no, some) Cell: Do you own a smart phone (yes, no) Data If you have not done so already, download the cell-phones datafile. As always, if you are prompted to login, just close the prompt and continue with the download. Upload the datafile into StatCrunch. Note: the symbol * in the spreadsheet indicates a missing value; this means that the associated individual did not answer the question. Context In 2015 college students at a large state university completed a survey about their academic and personal life. Questions ranged from "How many credits are you registered for this semester?" to "Would you define yourself as a vegetarian?" The researcher randomly selected 325 students from the university; 312 responded to the survey. Prompt The cell-phone datafile is available in the Data section below. Once again, here is the research question for this lab. Based on a recent study, roughly 80% of college students in the U.S. own a smartphone. Is the proportion of smartphone owners lower at this university? Respond to each of the following in your initial post. State your hypotheses in symbolic form and in words. (The following should be clear in your answer: the population of interest and the meaning of the proportion p in terms of the variable Cell.) StatCrunch uses a normal model to estimate the P-value probability. Verify that normality conditions are met.  Use StatCrunch to conduct the hypothesis test. (directions) Copy and paste the results (the StatCrunch output window) into your initial post. Give your P-value and interpret its meaning as a conditional probability. State a conclusion that answers the research question. Use a significance level of 5%. (Your answer should include the P-value and reference the population and the variable Cell.)

In this week’s Discussion, you determined the benefits of visually displaying your data. You also learned that different types of data require different visual displays to adequately “tell the story” of the phenomena. Although the Discussion was difficult, what is even more difficult and challenging will be deciding on how best to display the data for presentation purposes. As you move further along in your dissertation or doctoral study, you will find that displaying data will be critical component in presenting a robust and clear capstone project. For this Introduction to Quantitative Analysis Assignment, you will explore how to visually display data for optimal use. To prepare for this Assignment: Review this week’s Learning Resources and consider visual displays of data. For additional support, review the Skill Builder: Unit of Analysis and the Skill Builder: Levels of Measurement, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane. Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in this week’s Learning Resources. From the dataset you chose, choose one categorical and one continuous variable and perform the appropriate visual display for each variable. Once you visually display each variable, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document. For this Assignment: Write a 2- to 3-paragraph analysis of your results and include a copy and paste of the appropriate visual display of the data into your document. Based on the results of your data, provide a brief explanation of what the implications for social change might be. Early in your Assignment, when you relate which dataset you analyzed, please include the mean of the following variables. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the HS Long Survey Dataset, report the mean of X1SES.