1. What is the correlation coefficient (Pearson’s r) between the variable's calories and carb? 2. Interpret the strength of the relationship between the calories and the amount of carbohydrates (in grams) contained in the food menu at Starbucks. 3. Using JASP descriptive statistics, find the mean and standard deviation for the variable calories and carb
1. What is the correlation coefficient (Pearson’s r) between the variable's calories and carb? 2. Interpret the strength of the relationship between the calories and the amount of carbohydrates (in grams) contained in the food menu at Starbucks. 3. Using JASP descriptive statistics, find the mean and standard deviation for the variable calories and carb
1. What is the correlation coefficient (Pearson’s r) between the variable's calories and carb? 2. Interpret the strength of the relationship between the calories and the amount of carbohydrates (in grams) contained in the food menu at Starbucks. 3. Using JASP descriptive statistics, find the mean and standard deviation for the variable calories and carb
It is natural to think there will be a relationship between the number of calories and the amount of carbohydrates (in grams). In this journal, we will conduct a study using the nutrition data for several Starbucks food items. Click here for the dataset (spreadsheet) or Dataset (pdf)
Import the data to JASP, run the command and take a screenshot of your output. Based on that, answer the following questions.
1. What is the correlation coefficient (Pearson’s r) between the variable's calories and carb?
2. Interpret the strength of the relationship between the calories and the amount of carbohydrates (in grams) contained in the food menu at Starbucks.
3. Using JASP descriptive statistics, find the mean and standard deviation for the variable calories and carb
4. In a food label at Starbucks, the number of calories is indicated but the amount of carbohydrates (in grams) is missing. Write the equation of the regression line for prediction of the amount of carbohydrates (the response or dependent variables) given the number of calories (explanatory variable or covariate)
First calculate the slope (b_ 1).
Calculate the intercept (b_ 0).
Write the regression equation.
5. Using JASP linear regression, validate the regression equation found in c.
6. Calculate R2 of the regression line for predicting the amount of carbohydrates from the number of calories and interpret it in the context of the application.
Your submission of the Learning Journal assignment can be on a word document, a spreadsheet or a PDF.
Database PDF is attached via Images
Transcribed Image Text:The table shown provides nutritional information for various food items categorized under "bakery" and "bistro box" types. Each item includes measurements for calories, fat, carbohydrates, fiber, and protein.
### Key Components of the Table:
- **Columns:**
1. **Item**: Names of food items.
2. **Calories**: Total caloric content of each item.
3. **Fat**: Amount of fat in grams.
4. **Carb (Carbohydrates)**: Amount of carbohydrates in grams.
5. **Fiber**: Amount of dietary fiber in grams.
6. **Protein**: Amount of protein in grams.
7. **Type**: Category, either "bakery" or "bistro box."
- **Sample Rows Explained:**
- *8-Grain Roll*: Contains 350 calories, 8g fat, 67g carbs, 5g fiber, 10g protein, categorized as bakery.
- *Blueberry Scone*: Contains 460 calories, 22g fat, 61g carbs, 2g fiber, 7g protein, categorized as bakery.
- *Chicken & Hummus*: Contains 270 calories, 8g fat, 29g carbs, 6g fiber, 16g protein, categorized as bistro box.
- **Analysis:**
- Items range from low to high in calorie content, with the highest being "Cinnamon" at 480 calories.
- Bakery items tend to have higher carbohydrate content compared to bistro box items.
- Protein varies widely, with bistro box items generally providing more protein.
This table can serve as a useful guide for nutritional comparisons and for making informed dietary choices based on individual nutritional needs or preferences.
Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...
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