Group and Partner Projects

Univariate Data

Student Learning Objectives

Instructions

As you complete each task below, check it off. Answer all questions in your summary. * * *

\_\_\_\_ Decide what data you are going to study.

Here are two examples, but you may NOT use them: number of M&M's per bag, number of pencils students have in their backpacks.


\_\_\_\_ Are your data discrete or continuous? How do you know? * * *

\_\_\_\_ Decide how you are going to collect the data (for instance, buy 30 bags of M&M's; collect data from the World Wide Web). * * *

\_\_\_\_ Describe your sampling technique in detail. Use cluster, stratified, systematic, or simple random (using a random number generator) sampling. Do not use convenience sampling. Which method did you use? Why did you pick that method? * * *

\_\_\_\_ Conduct your survey. Your data size must be at least 30. * * *

\_\_\_\_ Summarize your data in a chart with columns showing data value, frequency, relative frequency and cumulative relative frequency. * * *

Answer the following (rounded to two decimal places):

  1. x ¯

    = \_\_\_\_\_

  2. s = \_\_\_\_\_
  3. First quartile = \_\_\_\_\_
  4. Median = \_\_\_\_\_
  5. 70th percentile = \_\_\_\_\_

\_\_\_\_ What value is two standard deviations above the mean?

\_\_\_\_ What value is 1.5 standard deviations below the mean? * * *

\_\_\_\_ Construct a histogram displaying your data. * * *

\_\_\_\_ In complete sentences, describe the shape of your graph. * * *

\_\_\_\_ Do you notice any potential outliers? If so, what values are they? Show your work in how you used the potential outlier formula to determine whether or not the values might be outliers. * * *

\_\_\_\_ Construct a box plot displaying your data. * * *

\_\_\_\_ Does the middle 50% of the data appear to be concentrated together or spread apart? Explain how you determined this. * * *

\_\_\_\_ Looking at both the histogram and the box plot, discuss the distribution of your data.

Assignment Checklist

You need to turn in the following typed and stapled packet, with pages in the following order:

Continuous Distributions and Central Limit Theorem

Student Learning Objectives

Instructions

As you complete each task below, check it off. Answer all questions in your summary.

Part I: Sampling

\_\_\_\_ Decide what continuous data you are going to study. (Here are two examples, but you may NOT use them: the amount of money a student spent on college supplies this term, or the length of time distance telephone call lasts.) * * *

\_\_\_\_ Describe your sampling technique in detail. Use cluster, stratified, systematic, or simple random (using a random number generator) sampling. Do not use convenience sampling. What method did you use? Why did you pick that method? * * *

\_\_\_\_ Conduct your survey. Gather at least 150 pieces of continuous, quantitative data. * * *

\_\_\_\_ Define (in words) the random variable for your data. X = \_\_\_\_\_\_\_ * * *

\_\_\_\_ Create two lists of your data: (1) unordered data, (2) in order of smallest to largest. * * *

\_\_\_\_ Find the sample mean and the sample standard deviation (rounded to two decimal places).

  1. x ¯

    = \_\_\_\_\_\_

  2. s = \_\_\_\_\_\_

\_\_\_\_ Construct a histogram of your data containing five to ten intervals of equal width. The histogram should be a representative display of your data. Label and scale it.

Part II: Possible Distributions

\_\_\_\_ Suppose that X followed the following theoretical distributions. Set up each distribution using the appropriate information from your data. * * *

\_\_\_\_ Uniform: X ~ U \_\_\_\_\_\_\_\_\_\_\_\_ Use the lowest and highest values as a and b. * * *

\_\_\_\_ Normal: X ~ N \_\_\_\_\_\_\_\_\_\_\_\_ Use x ¯

to estimate for μ and s to estimate for σ. * * *

\_\_\_\_ Must your data fit one of the above distributions? Explain why or why not. * * *

\_\_\_\_ Could the data fit two or three of the previous distributions (at the same time)? Explain. * * *

\_\_\_\_ Calculate the value k(an X value) that is 1.75 standard deviations above the sample mean. k = \_\_\_\_\_\_\_\_\_ (rounded to two decimal places) Note: k = x ¯

\_\_\_\_ Determine the relative frequencies (RF) rounded to four decimal places.

Note
RF= frequency total number surveyed
  1. RF(X < k) = \_\_\_\_\_\_
  2. RF(X > k) = \_\_\_\_\_\_
  3. RF(X = k) = \_\_\_\_\_\_
Note

You should have one page for the uniform distribution, one page for the exponential distribution, and one page for the normal distribution.

\_\_\_\_ State the distribution: X ~ \_\_\_\_\_\_\_\_\_ * * *

\_\_\_\_ Draw a graph for each of the three theoretical distributions. Label the axes and mark them appropriately. * * *

\_\_\_\_ Find the following theoretical probabilities (rounded to four decimal places).

  1. P(X < k) = \_\_\_\_\_\_
  2. P(X > k) = \_\_\_\_\_\_
  3. P(X = k) = \_\_\_\_\_\_

\_\_\_\_ Compare the relative frequencies to the corresponding probabilities. Are the values close? * * *

\_\_\_\_ Does it appear that the data fit the distribution well? Justify your answer by comparing the probabilities to the relative frequencies, and the histograms to the theoretical graphs.

Part III: CLT Experiments

\_\_\_\_\_\_ From your original data (before ordering), use a random number generator to pick 40 samples of size five. For each sample, calculate the average. * * *

\_\_\_\_\_\_ On a separate page, attached to the summary, include the 40 samples of size five, along with the 40 sample averages. * * *

\_\_\_\_\_\_ List the 40 averages in order from smallest to largest. * * *

\_\_\_\_\_\_ Define the random variable, X ¯

, in words. X ¯

= \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ * * *

\_\_\_\_\_\_ State the approximate theoretical distribution of X ¯

. X ¯

~ \_\_\_\_\_\_\_\_\_\_\_\_\_\_ * * *

\_\_\_\_\_\_ Base this on the mean and standard deviation from your original data. * * *

\_\_\_\_\_\_ Construct a histogram displaying your data. Use five to six intervals of equal width. Label and scale it. * * *

Calculate the value k ¯

(an X ¯

value) that is 1.75 standard deviations above the sample mean. k ¯

= \_\_\_\_\_ (rounded to two decimal places) * * *

Determine the relative frequencies (RF) rounded to four decimal places.

  1. RF( X ¯

    <

    k ¯

    ) = \_\_\_\_\_\_\_

  2. RF( X ¯

    >

    k ¯

    ) = \_\_\_\_\_\_\_

  3. RF( X ¯

    =

    k ¯

    ) = \_\_\_\_\_\_\_

Find the following theoretical probabilities (rounded to four decimal places).

  1. P( X ¯

    <

    k ¯

    ) = \_\_\_\_\_\_\_

  2. P( X ¯

    >

    k ¯

    ) = \_\_\_\_\_\_\_

  3. P( X ¯

    =

    k ¯

    ) = \_\_\_\_\_\_\_

\_\_\_\_\_\_ Draw the graph of the theoretical distribution of X

. * * *

\_\_\_\_\_\_ Compare the relative frequencies to the probabilities. Are the values close? * * *

\_\_\_\_\_\_ Does it appear that the data of averages fit the distribution of X ¯

well? Justify your answer by comparing the probabilities to the relative frequencies, and the histogram to the theoretical graph. * * *

In three to five complete sentences for each, answer the following questions. Give thoughtful explanations. * * *

\_\_\_\_\_\_ In summary, do your original data seem to fit the uniform, exponential, or normal distributions? Answer why or why not for each distribution. If the data do not fit any of those distributions, explain why. * * *

\_\_\_\_\_\_ What happened to the shape and distribution when you averaged your data? In theory, what should have happened? In theory, would “it” always happen? Why or why not? * * *

\_\_\_\_\_\_ Were the relative frequencies compared to the theoretical probabilities closer when comparing the X

or X ¯

distributions? Explain your answer.

Assignment Checklist

You need to turn in the following typed and stapled packet, with pages in the following order: * * *

\_\_\_\_ Cover sheet: name, class time, and name of your study * * *

\_\_\_\_ Summary pages: These should contain several paragraphs written with complete sentences that describe the experiment, including what you studied and your sampling technique, as well as answers to all of the questions previously asked questions * * *

\_\_\_\_ URL for data, if your data are from the World Wide Web * * *

\_\_\_\_ Pages, one for each theoretical distribution, with the distribution stated, the graph, and the probability questions answered * * *

\_\_\_\_ Pages of the data requested * * *

\_\_\_\_ All graphs required

Hypothesis Testing-Article

Student Learning Objectives

Instructions

As you complete each task, check it off. Answer all questions in your summary. * * *

\_\_\_\_Find an article in a newspaper, magazine, or on the internet which makes a claim about ONE population mean or ONE population proportion. The claim may be based upon a survey that the article was reporting on. Decide whether this claim is the null or alternate hypothesis. * * *

\_\_\_\_Copy or print out the article and include a copy in your project, along with the source. * * *

\_\_\_\_State how you will collect your data. (Convenience sampling is not acceptable.) * * *

\_\_\_\_Conduct your survey. You must have more than 50 responses in your sample. When you hand in your final project, attach the tally sheet or the packet of questionnaires that you used to collect data. Your data must be real. * * *

\_\_\_\_State the statistics that are a result of your data collection: sample size, sample mean, and sample standard deviation, OR sample size and number of successes. * * *

\_\_\_\_Make two copies of the appropriate solution sheet. * * *

\_\_\_\_Record the hypothesis test on the solution sheet, based on your experiment. Do a DRAFT solution first on one of the solution sheets and check it over carefully. Have a classmate check your solution to see if it is done correctly. Make your decision using a 5% level of significance. Include the 95% confidence interval on the solution sheet. * * *

\_\_\_\_Create a graph that illustrates your data. This may be a pie or bar graph or may be a histogram or box plot, depending on the nature of your data. Produce a graph that makes sense for your data and gives useful visual information about your data. You may need to look at several types of graphs before you decide which is the most appropriate for the type of data in your project. * * *

\_\_\_\_Write your summary (in complete sentences and paragraphs, with proper grammar and correct spelling) that describes the project. The summary MUST include:

  1. Brief discussion of the article, including the source
  2. Statement of the claim made in the article (one of the hypotheses).
  3. Detailed description of how, where, and when you collected the data, including the sampling technique; did you use cluster, stratified, systematic, or simple random sampling (using a random number generator)? As previously mentioned, convenience sampling is not acceptable.
  4. Conclusion about the article claim in light of your hypothesis test; this is the conclusion of your hypothesis test, stated in words, in the context of the situation in your project in sentence form, as if you were writing this conclusion for a non-statistician.
  5. Sentence interpreting your confidence interval in the context of the situation in your project

Assignment Checklist

Turn in the following typed (12 point) and stapled packet for your final project: * * *

\_\_\_\_Cover sheet containing your name(s), class time, and the name of your study * * *

\_\_\_\_Summary, which includes all items listed on summary checklist * * *

\_\_\_\_Solution sheet neatly and completely filled out. The solution sheet does not need to be typed. * * *

\_\_\_\_Graphic representation of your data, created following the guidelines previously discussed; include only graphs which are appropriate and useful. * * *

\_\_\_\_Raw data collected AND a table summarizing the sample data (n, x ¯

and s; or x, n, and p’, as appropriate for your hypotheses); the raw data does not need to be typed, but the summary does. Hand in the data as you collected it. (Either attach your tally sheet or an envelope containing your questionnaires.)

Bivariate Data, Linear Regression, and Univariate Data

Student Learning Objectives

Instructions

  1. As you complete each task below, check it off. Answer all questions in your introduction or summary.
  2. Check your course calendar for intermediate and final due dates.
  3. Graphs may be constructed by hand or by computer, unless your instructor informs you otherwise. All graphs must be neat and accurate.
  4. All other responses must be done on the computer.
  5. Neatness and quality of explanations are used to determine your final grade.

Part I: Bivariate Data

Introduction\_\_\_\_State the bivariate data your group is going to study.

Here are two examples, but you may NOT use them: height vs. weight and age vs. running distance.


\_\_\_\_Describe your sampling technique in detail. Use cluster, stratified, systematic, or simple random sampling (using a random number generator) sampling. Convenience sampling is NOT acceptable. * * *

\_\_\_\_Conduct your survey. Your number of pairs must be at least 30. * * *

\_\_\_\_Print out a copy of your data.

Analysis \_\_\_\_On a separate sheet of paper construct a scatter plot of the data. Label and scale both axes. * * *

\_\_\_\_State the least squares line and the correlation coefficient. * * *

\_\_\_\_On your scatter plot, in a different color, construct the least squares line. * * *

\_\_\_\_Is the correlation coefficient significant? Explain and show how you determined this. * * *

\_\_\_\_Interpret the slope of the linear regression line in the context of the data in your project. Relate the explanation to your data, and quantify what the slope tells you. * * *

\_\_\_\_Does the regression line seem to fit the data? Why or why not? If the data does not seem to be linear, explain if any other model seems to fit the data better. * * *

\_\_\_\_Are there any outliers? If so, what are they? Show your work in how you used the potential outlier formula in the Linear Regression and Correlation chapter (since you have bivariate data) to determine whether or not any pairs might be outliers.

Part II: Univariate Data

In this section, you will use the data for ONE variable only. Pick the variable that is more interesting to analyze. For example: if your independent variable is sequential data such as year with 30 years and one piece of data per year, your x-values might be 1971, 1972, 1973, 1974, …, 2000. This would not be interesting to analyze. In that case, choose to use the dependent variable to analyze for this part of the project. * * *

\_\_\_\_\_Summarize your data in a chart with columns showing data value, frequency, relative frequency, and cumulative relative frequency. * * *

\_\_\_\_\_Answer the following question, rounded to two decimal places:

  1. Sample mean = \_\_\_\_\_\_
  2. Sample standard deviation = \_\_\_\_\_\_
  3. First quartile = \_\_\_\_\_\_
  4. Third quartile = \_\_\_\_\_\_
  5. Median = \_\_\_\_\_\_
  6. 70th percentile = \_\_\_\_\_\_
  7. Value that is 2 standard deviations above the mean = \_\_\_\_\_\_
  8. Value that is 1.5 standard deviations below the mean = \_\_\_\_\_\_

\_\_\_\_\_Construct a histogram displaying your data. Group your data into six to ten intervals of equal width. Pick regularly spaced intervals that make sense in relation to your data. For example, do NOT group data by age as 20-26,27-33,34-40,41-47,48-54,55-61 . . . Instead, maybe use age groups 19.5-24.5, 24.5-29.5, . . . or 19.5-29.5, 29.5-39.5, 39.5-49.5, . . . * * *

\_\_\_\_\_In complete sentences, describe the shape of your histogram. * * *

\_\_\_\_\_Are there any potential outliers? Which values are they? Show your work and calculations as to how you used the potential outlier formula in Descriptive Statistics (since you are now using univariate data) to determine which values might be outliers. * * *

\_\_\_\_\_Construct a box plot of your data. * * *

\_\_\_\_\_Does the middle 50% of your data appear to be concentrated together or spread out? Explain how you determined this. * * *

\_\_\_\_\_Looking at both the histogram AND the box plot, discuss the distribution of your data. For example: how does the spread of the middle 50% of your data compare to the spread of the rest of the data represented in the box plot; how does this correspond to your description of the shape of the histogram; how does the graphical display show any outliers you may have found; does the histogram show any gaps in the data that are not visible in the box plot; are there any interesting features of your data that you should point out.

Due Dates

Note

Include answers to ALL questions asked, even if not explicitly repeated in the items above.


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