Intro stats normal models a normal model can be a useful tool for
Intro Stats Normal Models
A normal model can be a useful tool for interpreting what data have to say – sometimes. Your task here is to check the usefulness of such a model for the data that you collect. There are three parts to the “project”.
- Collect the Data.
You need between 30 and 50 pieces of quantitative data, preferably something that you have an interest in.
- Describe the Data
Write a brief but thorough description of your data. Start with the “W’s” and remember to include some visual representation, numerical analysis and description of the data.
- Check the Normal Model
Use the mean and the standard deviation of your data to create a Normal Model. (Sketch a normal model and label the x-axis going out three standard deviations in each direction) Compare your model to the normal model and compare the Empirical Rule (68-95-99.7) to the actual percentages of data that lies within one, two and three standard deviations of the actual mean.
Scoring Rubric
Show:
- Data, Display, Statistics
- Collected Appropriate Data
- Shows a well-constructed histogram or other graphical display
- Scale for graph is appropriate for comparison
- Calculates summary statistics (5-Number Summary, Mean, Standard Deviation and IQR)
- Normal Model
- The sketch of the normal model is centered at the mean
- Has correct cutoffs based on the standard deviation
- Clearly shows and explains the 68%-95%-99.7% rule.
Tell:
- Describe the Data
- Describes all the “W’s” as thoroughly as possible
- Describes the shape of the actual distribution in detail
- Describes the best measure of center and spread for the distribution.
- Mentions any unusual features (if applicable) and checks for outliers (Show work)
- Evaluates the Usefulness of the Normal Model.
- Compares the shape of the actual distribution to the normal model’s shape
- Compares the actual distribution of the data to the 68%, 95%, 99.7% rule.
- States a Valid conclusion as to whether or not a normal model appears to be a “good fit” for the data by using supportive evidence.
Each of the four components makes up 25% of the grade for the project, and partial credit can be given on some or all of the bullet points.
