Access the Nutrition Lab: http://www.mhhe.com/biosci/genbio/virtual_labs_2K8/labs/BL_15/index.html

1. Read the background information found under the “Question” area first, and then continue on to reading the procedure information posted there as well.

2. Click on the “Nutrition Facts Label Guidebook” on the lower portion of the activity area and read through the information presented. You should also click on the “Daily Calories Chart” for additional information regarding individual energy requirements and expenditures.

a. Based on the Daily Caloric Recommendations Chart, I should consume approximately _____________ calories a day.

b. The three factors I used to decide on this number were ____________, _____________ and _________________.

3. Pick today (or a day this week) and document what you ate (as close as possible) by selecting a food items and dragging them to the various meal times in the laboratory area.

4. Make sure that you note each food choice made (and the number of servings) in Table I below.

5. When you are done your day, please be sure to note the information from your “Daily Nutritional Values” graph in Table II.

Table I:

 

Mealtime

Grains

Fruit

Vegetables

Protein

Dairy

Fats, oils and   sweets

Prepared Foods

 

Breakfast

 

Snack

 

Lunch

 

Snack

 

Dinner

Table II:

 

Calories

Total Fat

Saturated Fat

Trans Fat

Cholesterol

Sodium

Carbohydrates

Protein

 

% Daily Value

Total Calories for Menu: _________

When you are finished, answer all of the questions:

· Did your food choices follow the government’s recommendations for calories and nutrients? Were the calories you ate too high or too low?

· What changes do you need to make to your food choices in order to get close to the recommendations?

· Do any of the items you selected provide more than 30% in any nutritional area? If so, which foods and which nutrients?

· What did you notice about the food group icons in your Daily Meals area? Did you favor a specific food group? Explain.

· How can you use the Nutrition Facts label to make healthy decisions?

· What did you notice about the nutrients in fast foods?

 

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Lab: Digestive System & Nutrition was first posted on July 22, 2020 at 12:24 pm.
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• What’s an attribute? What’s a data instance?

1. What’s noise? How can noise be reduced in a dataset?

1. Define outlier. Describe 2 different approaches to detect outliers in a dataset.

1. Describe 3 different techniques to deal with missing values in a dataset. Explain when each of these techniques would be most appropriate.

1. Given a sample dataset with missing values, apply an appropriate technique to deal with them.

1. Give 2 examples in which aggregation is useful.

1. Given a sample dataset, apply aggregation of data values.

1. What’s sampling?

1. What’s simple random sampling? Is it possible to sample data instances using a distribution different from the uniform distribution? If so, give an example of a probability distribution of the data instances that is different from uniform (i.e., equal probability).

1. What’s stratified sampling?

1. What’s “the curse of dimensionality”?

1. Provide a brief description of what Principal Components Analysis (PCA) does. [Hint: See Appendix A and your lecture notes.] State what’s the input and what the output of PCA is.

1. What’s the difference between dimensionality reduction and feature selection?

1. Describe in detail 2 different techniques for feature selection.

1. Given a sample dataset (represented by a set of attributes, a correlation matrix, a co-variance matrix, …), apply feature selection techniques to select the best attributes to keep (or equivalently, the best attributes to remove).

1. What’s the difference between feature selection and feature extraction?

1. Give two examples of data in which feature extraction would be useful.

1. Given a sample dataset, apply feature extraction.

1. What’s data discretization and when is it needed?

1. What’s the difference between supervised and unsupervised discretization?

1. Given a sample dataset, apply unsupervised (e.g., equal width, equal frequency) discretization, or supervised discretization (e.g., using entropy).

1. Describe 2 approaches to handle nominal attributes with too many values.

1. Given a dataset, apply variable transformation: Either a simple given function, normalization, or standardization.

Definition of Correlation and Covariance, and how to use them in data pre-processing (see pp.

 

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