Data analysis assignment example

Data Analysis Assignment #2 Spring 2017 STAT 250 Your submitted document should include the following items. Points will be deducted if the following are not included: 1. Type your Name, STAT 250 with your correct section number (e.g. STAT 250-xxx) and Data Analysis Assignment #2 centered on the top of page 1 of your document. 2. Number your pages across your entire solutions document. 3. Your document should include the ANSWERS ONLY to the following FOUR questions with each answer labeled by its corresponding number and subpart. Keep the questions in order. Do NOT include the questions in your submitted document. Please see posted model solution as an example. 4. Generate all requested graphs and tables using StatCrunch. 5. Upload your document onto Blackboard as a Word or pdf document using the link provided by your instructor. Elements of good technical writing: Use complete and coherent sentences to answer the questions. Graphs must be appropriately titled and should refer to the context of the question. Graphical displays must include labels with units if appropriate for each axis. Units should always be included when referring to numerical values. When making a comparison you must use comparative language, such as “greater than”, “less than”, or “about the same as.” Ensure that all graphs and tables appear on one page and are not split across two pages. Show all mathematical calculations when directed to compute an answer ‘by-hand.’ When writing mathematical expressions into your document you may use either an equation editor or common shortcuts such as: x can be written as sqrt(x), p̂ can be written as p-hat, x can be written as x-bar. 1 Instructions This data analysis assignment will use both StatCrunch applets to simulate random actions and the results from a survey administered to a nationwide random sample of high school students found in our StatCrunch group titled CensusAtSchool. Question 1 1a. Use StatCrunch to construct an appropriately titled and labeled scatterplot with “Armspan_cm” as the explanatory variable and “Height_cm” as the response variable. Copy your scatterplot into your document. 1b. Use Stat-> Summary Stats->Correlation to compute the correlation between “Armspan_cm” and “Height_cm”. Copy the table into your document. 1c. Use StatCrunch to construct an appropriately titled and labeled scatterplot with “Footlength_cm” as the explanatory variable and “Height_cm” as the response variable. Copy your scatterplot into your document. 1d. Use Stat-> Summary Stats->Correlation to compute the correlation between “Footlength_cm” and “Height_cm”. Copy the table into your document. 1e. If you were trying to predict a high school student’s height, would you be able to make a better prediction by knowing the student’s armspan or footlength? Use the scatterplots and correlation values from parts 1a-1d to justify your choice of armspan or footlength by comparing their shapes, trends and strengths. 1f. Use StatCrunch to conduct a regression analysis to predict a student’s height using their armspan. Use Stat->Regression->Simple Linear. Copy only the simple linear regression results (as shown below) into your document. Simple linear regression results: Dependent Variable: Independent Variable: Height_cm = Sample size: R (correlation coefficient) = R-sq = Estimate of error standard deviation: 1g. Interpret the value of the slope (rounded to three decimal places) in the regression equation found in part 1f in the context of the question, using a complete sentence. 2 1h. Use the regression equation (with both intercept and slope rounded to three decimal places) to predict the height of a student (in cm) if their armspan was found to be 165 cm. Show all of your hand calculations and remember to include units in your final answer. 1i. Interpret the value of the coefficient of determination (R-sq expressed as a percentage) in the context of the question in a complete sentence. Question 2 2. We will be comparing empirical (relative frequencies based on an observation of a real-life process) to theoretical (long-run relative frequency) probabilities. We will use StatCrunch to simulate rolling two dice. Conduct the following simulation by using the steps below: Step 1:Under Applets